research topics in interventional radiology

Janice Sung, MD

Locations and Appointments

Cuimc/herbert irving pavilion, about janice sung, md.

Dr. Janice Sung is a board-certified radiologist specializing in breast imaging. Sheis an associate professor of radiology and an attending radiologist at NewYork-Presbyterian Hospital. Dr. Sung is experienced in all types of breast imaging and breast cancer diagnosis, including mammography, ultrasound, and MRI, as well as image-guided interventional procedures such as stereotactic breast biopsy, ultrasound-guided biopsy, MR-guided biopsy, and radioactive seed placement and wire localizations.

Dr. Sung's research focuses on imaging for early breast cancer detection in women who are at intermediate and high risk for developing breast cancer. She has co-authored multiple scientific articles defining groups of women who will benefit from screening that is more intensive than routine, annual mammography. She is frequently invited to lecture on this and other topics in breast imaging. She also trains and supervises residents and fellows in interpreting breast-images and in interventional breast procedures. In recognition of her accomplishments, she has been named a Fellow of the Society of Breast Imaging.

Dr. Sung received her medical degree from the State University of New York, Buffalo, and completed a residency in diagnostic radiology at the University of Pennsylvania. She then completed a one-year fellowship in breast and body imaging at Memorial Sloan Kettering Cancer Center.

Specialties & Expertise

• Breast Biopsy
• Breast Cancer
• Breast Cancer High Risk Screening
• Breast Imaging
• Breast Ultrasound
• Fine Needle Aspiration Biopsy
• Image Guided Biopsies
• Mammography
• Needle Core Biopsy
• Stereotactic Breast Biopsy
• Tomosynthesis
• Ultrasound Guided Breast Biopsy
• Medical School: University at Buffalo School of Medicine and Biomedical Sciences
• Residency: University of Pennsylvania
• Fellowship: Memorial Sloan Kettering Cancer Center

Leadership, Titles & Positions

• Associate Chief, Division of Breast Imaging
• Associate Professor of Radiology

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Research publications in vascular and interventional radiology: research topics, study designs, and statistical methods

Affiliation.

• 1 Departments of Radiology, University of California San Francisco, 505 Parnassus Avenue, M-361, Box 0628, San Francisco, CA 94143, USA.
• PMID: 11875084
• DOI: 10.1016/s1051-0443(07)61717-5

Purpose: Statistical analysis is the universal language of medical research and is a vital tool for communicating the results of vascular and interventional radiology (VIR) procedures. Major articles in two radiology journals were surveyed to characterize the research topics, study designs, and statistical methods seen in recent VIR research publications.

Materials and methods: The authors retrospectively reviewed 130 major clinical VIR articles published from July 2000 to June 2001: 72 articles (55%) from JVIR and 58 articles (45%) from RADIOLOGY: Articles were categorized by research topic and study design. Data were collected on the statistical methodology of each article.

Results: Research topics included vascular intervention in 65 of 130 articles (50%), nonvascular intervention in 26 (20%), vascular imaging in 23 (18%), biopsy in nine (7%), and other topics in seven (5%). Study design was descriptive in 87 studies (67%), comparative in 39 studies (30%), and involved secondary data analysis in four studies (3%). Of 126 primary clinical studies, outcome was cross-sectional (assessed at a single time point) in 40 studies (32%) and longitudinal (measured over time) in 86 studies (68%). Median sample size was 61. Basic tests of association (t-test, chi(2) test, etc.) were used in 71 articles (56%) and advanced tests of association (regression analysis) were presented in 25 (20%). Survival analysis was applied in 34 articles (27%). Decision statistics such as sensitivity/specificity were not used commonly (12%). Confidence intervals and power calculations were reported infrequently (15% and 7%, respectively).

Conclusions: VIR publications focus on time-dependent outcomes after therapeutic interventions. Readers should understand basic tests of association and survival analysis--these include only 20 named statistical tests.

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Head Start Your Radiology Residency [Online] ↗️

• Radiology Thesis – More than 400 Research Topics (2022)!

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Introduction

A thesis or dissertation, as some people would like to call it, is an integral part of the Radiology curriculum, be it MD, DNB, or DMRD. We have tried to aggregate radiology thesis topics from various sources for reference.

Not everyone is interested in research, and writing a Radiology thesis can be daunting. But there is no escape from preparing, so it is better that you accept this bitter truth and start working on it instead of cribbing about it (like other things in life. #PhilosophyGyan!)

Start working on your thesis as early as possible and finish your thesis well before your exams, so you do not have that stress at the back of your mind. Also, your thesis may need multiple revisions, so be prepared and allocate time accordingly.

Tips for Choosing Radiology Thesis and Research Topics

Keep it simple silly (kiss).

Retrospective > Prospective

Retrospective studies are better than prospective ones, as you already have the data you need when choosing to do a retrospective study. Prospective studies are better quality, but as a resident, you may not have time (, energy and enthusiasm) to complete these.

Choose a simple topic that answers a single/few questions

Original research is challenging, especially if you do not have prior experience. I would suggest you choose a topic that answers a single or few questions. Most topics that I have listed are along those lines. Alternatively, you can choose a broad topic such as “Role of MRI in evaluation of perianal fistulas.”

You can choose a novel topic if you are genuinely interested in research AND have a good mentor who will guide you. Once you have done that, make sure that you publish your study once you are done with it.

Get it done ASAP.

In most cases, it makes sense to stick to a thesis topic that will not take much time. That does not mean you should ignore your thesis and ‘Ctrl C + Ctrl V’ from a friend from another university. Thesis writing is your first step toward research methodology so do it as sincerely as possible. Do not procrastinate in preparing the thesis. As soon as you have been allotted a guide, start researching topics and writing a review of the literature.

At the same time, do not invest a lot of time in writing/collecting data for your thesis. You should not be busy finishing your thesis a few months before the exam. Some people could not appear for the exam because they could not submit their thesis in time. So DO NOT TAKE thesis lightly.

Do NOT Copy-Paste

Reiterating once again, do not simply choose someone else’s thesis topic. Find out what are kind of cases that your Hospital caters to. It is better to do a good thesis on a common topic than a crappy one on a rare one.

Books to help you write a Radiology Thesis

Event country/university has a different format for thesis; hence these book recommendations may not work for everyone.

• Amazon Kindle Edition
• Gupta, Piyush (Author)
• English (Publication Language)
• 206 Pages - 10/12/2020 (Publication Date) - Jaypee Brothers Medical Publishers (P) Ltd. (Publisher)

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List of Radiology Research /Thesis / Dissertation Topics

• State of the art of MRI in the diagnosis of hepatic focal lesions
• Multimodality imaging evaluation of sacroiliitis in newly diagnosed patients of spondyloarthropathy
• Multidetector computed tomography in oesophageal varices
• Role of positron emission tomography with computed tomography in the diagnosis of cancer Thyroid
• Evaluation of focal breast lesions using ultrasound elastography
• Role of MRI diffusion tensor imaging in the assessment of traumatic spinal cord injuries
• Sonographic imaging in male infertility
• Comparison of color Doppler and digital subtraction angiography in occlusive arterial disease in patients with lower limb ischemia
• The role of CT urography in Haematuria
• Role of functional magnetic resonance imaging in making brain tumor surgery safer
• Prediction of pre-eclampsia and fetal growth restriction by uterine artery Doppler
• Role of grayscale and color Doppler ultrasonography in the evaluation of neonatal cholestasis
• Validity of MRI in the diagnosis of congenital anorectal anomalies
• Role of sonography in assessment of clubfoot
• Role of diffusion MRI in preoperative evaluation of brain neoplasms
• Imaging of upper airways for pre-anaesthetic evaluation purposes and for laryngeal afflictions.
• A study of multivessel (arterial and venous) Doppler velocimetry in intrauterine growth restriction
• Multiparametric 3tesla MRI of suspected prostatic malignancy.
• Role of Sonography in Characterization of Thyroid Nodules for differentiating benign from
• Role of advances magnetic resonance imaging sequences in multiple sclerosis
• Role of multidetector computed tomography in evaluation of jaw lesions
• Role of Ultrasound and MR Imaging in the Evaluation of Musculotendinous Pathologies of Shoulder Joint
• Role of perfusion computed tomography in the evaluation of cerebral blood flow, blood volume and vascular permeability of cerebral neoplasms
• MRI flow quantification in the assessment of the commonest csf flow abnormalities
• Role of diffusion-weighted MRI in evaluation of prostate lesions and its histopathological correlation
• CT enterography in evaluation of small bowel disorders
• Comparison of perfusion magnetic resonance imaging (PMRI), magnetic resonance spectroscopy (MRS) in and positron emission tomography-computed tomography (PET/CT) in post radiotherapy treated gliomas to detect recurrence
• Role of multidetector computed tomography in evaluation of paediatric retroperitoneal masses
• Role of Multidetector computed tomography in neck lesions
• Estimation of standard liver volume in Indian population
• Role of MRI in evaluation of spinal trauma
• Role of modified sonohysterography in female factor infertility: a pilot study.
• The role of pet-CT in the evaluation of hepatic tumors
• Role of 3D magnetic resonance imaging tractography in assessment of white matter tracts compromise in supratentorial tumors
• Role of dual phase multidetector computed tomography in gallbladder lesions
• Role of multidetector computed tomography in assessing anatomical variants of nasal cavity and paranasal sinuses in patients of chronic rhinosinusitis.
• magnetic resonance spectroscopy in multiple sclerosis
• Evaluation of thyroid nodules by ultrasound elastography using acoustic radiation force impulse (ARFI) imaging
• Role of Magnetic Resonance Imaging in Intractable Epilepsy
• Evaluation of suspected and known coronary artery disease by 128 slice multidetector CT.
• Role of regional diffusion tensor imaging in the evaluation of intracranial gliomas and its histopathological correlation
• Role of chest sonography in diagnosing pneumothorax
• Role of CT virtual cystoscopy in diagnosis of urinary bladder neoplasia
• Role of MRI in assessment of valvular heart diseases
• High resolution computed tomography of temporal bone in unsafe chronic suppurative otitis media
• Multidetector CT urography in the evaluation of hematuria
• Contrast-induced nephropathy in diagnostic imaging investigations with intravenous iodinated contrast media
• Comparison of dynamic susceptibility contrast-enhanced perfusion magnetic resonance imaging and single photon emission computed tomography in patients with little’s disease
• Role of Multidetector Computed Tomography in Bowel Lesions.
• Role of diagnostic imaging modalities in evaluation of post liver transplantation recipient complications.
• Role of multislice CT scan and barium swallow in the estimation of oesophageal tumour length
• Malignant Lesions-A Prospective Study.
• Value of ultrasonography in assessment of acute abdominal diseases in pediatric age group
• Role of three dimensional multidetector CT hysterosalpingography in female factor infertility
• Comparative evaluation of multi-detector computed tomography (MDCT) virtual tracheo-bronchoscopy and fiberoptic tracheo-bronchoscopy in airway diseases
• Role of Multidetector CT in the evaluation of small bowel obstruction
• Sonographic evaluation in adhesive capsulitis of shoulder
• Utility of MR Urography Versus Conventional Techniques in Obstructive Uropathy
• MRI of the postoperative knee
• Role of 64 slice-multi detector computed tomography in diagnosis of bowel and mesenteric injury in blunt abdominal trauma.
• Sonoelastography and triphasic computed tomography in the evaluation of focal liver lesions
• Evaluation of Role of Transperineal Ultrasound and Magnetic Resonance Imaging in Urinary Stress incontinence in Women
• Multidetector computed tomographic features of abdominal hernias
• Evaluation of lesions of major salivary glands using ultrasound elastography
• Transvaginal ultrasound and magnetic resonance imaging in female urinary incontinence
• MDCT colonography and double-contrast barium enema in evaluation of colonic lesions
• Role of MRI in diagnosis and staging of urinary bladder carcinoma
• Spectrum of imaging findings in children with febrile neutropenia.
• Spectrum of radiographic appearances in children with chest tuberculosis.
• Role of computerized tomography in evaluation of mediastinal masses in pediatric
• Diagnosing renal artery stenosis: Comparison of multimodality imaging in diabetic patients
• Role of multidetector CT virtual hysteroscopy in the detection of the uterine & tubal causes of female infertility
• Role of multislice computed tomography in evaluation of crohn’s disease
• CT quantification of parenchymal and airway parameters on 64 slice MDCT in patients of chronic obstructive pulmonary disease
• Comparative evaluation of MDCT  and 3t MRI in radiographically detected jaw lesions.
• Evaluation of diagnostic accuracy of ultrasonography, colour Doppler sonography and low dose computed tomography in acute appendicitis
• Ultrasonography , magnetic resonance cholangio-pancreatography (MRCP) in assessment of pediatric biliary lesions
• Multidetector computed tomography in hepatobiliary lesions.
• Evaluation of peripheral nerve lesions with high resolution ultrasonography and colour Doppler
• Multidetector computed tomography in pancreatic lesions
• Multidetector Computed Tomography in Paediatric abdominal masses.
• Evaluation of focal liver lesions by colour Doppler and MDCT perfusion imaging
• Sonographic evaluation of clubfoot correction during Ponseti treatment
• Role of multidetector CT in characterization of renal masses
• Study to assess the role of Doppler ultrasound in evaluation of arteriovenous (av) hemodialysis fistula and the complications of hemodialysis vasular access
• Comparative study of multiphasic contrast-enhanced CT and contrast-enhanced MRI in the evaluation of hepatic mass lesions
• Sonographic spectrum of rheumatoid arthritis
• Diagnosis & staging of liver fibrosis by ultrasound elastography in patients with chronic liver diseases
• Role of multidetector computed tomography in assessment of jaw lesions.
• Role of high-resolution ultrasonography in the differentiation of benign and malignant thyroid lesions
• Radiological evaluation of aortic aneurysms in patients selected for endovascular repair
• Role of conventional MRI, and diffusion tensor imaging tractography in evaluation of congenital brain malformations
• To evaluate the status of coronary arteries in patients with non-valvular atrial fibrillation using 256 multirow detector CT scan
• A comparative study of ultrasonography and CT – arthrography in diagnosis of chronic ligamentous and meniscal injuries of knee
• Multi detector computed tomography evaluation in chronic obstructive pulmonary disease and correlation with severity of disease
• Diffusion weighted and dynamic contrast enhanced magnetic resonance imaging in chemoradiotherapeutic response evaluation in cervical cancer.
• High resolution sonography in the evaluation of non-traumatic painful wrist
• The role of trans-vaginal ultrasound versus magnetic resonance imaging in diagnosis & evaluation of cancer cervix
• Role of multidetector row computed tomography in assessment of maxillofacial trauma
• Imaging of vascular complication after liver transplantation.
• Role of magnetic resonance perfusion weighted imaging & spectroscopy for grading of glioma by correlating perfusion parameter of the lesion with the final histopathological grade
• Magnetic resonance evaluation of abdominal tuberculosis.
• Diagnostic usefulness of low dose spiral HRCT in diffuse lung diseases
• Role of dynamic contrast enhanced and diffusion weighted magnetic resonance imaging in evaluation of endometrial lesions
• Contrast enhanced digital mammography anddigital breast tomosynthesis in early diagnosis of breast lesion
• Evaluation of Portal Hypertension with Colour Doppler flow imaging and magnetic resonance imaging
• Evaluation of musculoskeletal lesions by magnetic resonance imaging
• Role of diffusion magnetic resonance imaging in assessment of neoplastic and inflammatory brain lesions
• Radiological spectrum of chest diseases in HIV infected children High resolution ultrasonography in neck masses in children
• with surgical findings
• Sonographic evaluation of peripheral nerves in type 2 diabetes mellitus.
• Role of perfusion computed tomography in the evaluation of neck masses and correlation
• Role of ultrasonography in the diagnosis of knee joint lesions
• Role of ultrasonography in evaluation of various causes of pelvic pain in first trimester of pregnancy.
• Role of Magnetic Resonance Angiography in the Evaluation of Diseases of Aorta and its Branches
• MDCT fistulography in evaluation of fistula in Ano
• Role of multislice CT in diagnosis of small intestine tumors
• Role of high resolution CT in differentiation between benign and malignant pulmonary nodules in children
• A study of multidetector computed tomography urography in urinary tract abnormalities
• Role of high resolution sonography in assessment of ulnar nerve in patients with leprosy.
• Pre-operative radiological evaluation of locally aggressive and malignant musculoskeletal tumours by computed tomography and magnetic resonance imaging.
• The role of ultrasound & MRI in acute pelvic inflammatory disease
• Ultrasonography compared to computed tomographic arthrography in the evaluation of shoulder pain
• Role of Multidetector Computed Tomography in patients with blunt abdominal trauma.
• The Role of Extended field-of-view Sonography and compound imaging in Evaluation of Breast Lesions
• Evaluation of focal pancreatic lesions by Multidetector CT and perfusion CT
• Evaluation of breast masses on sono-mammography and colour Doppler imaging
• Role of CT virtual laryngoscopy in evaluation of laryngeal masses
• Triple phase multi detector computed tomography in hepatic masses
• Role of transvaginal ultrasound in diagnosis and treatment of female infertility
• Role of ultrasound and color Doppler imaging in assessment of acute abdomen due to female genetal causes
• High resolution ultrasonography and color Doppler ultrasonography in scrotal lesion
• Evaluation of diagnostic accuracy of ultrasonography with colour Doppler vs low dose computed tomography in salivary gland disease
• Role of multidetector CT in diagnosis of salivary gland lesions
• Comparison of diagnostic efficacy of ultrasonography and magnetic resonance cholangiopancreatography in obstructive jaundice: A prospective study
• Evaluation of varicose veins-comparative assessment of low dose CT venogram with sonography: pilot study
• Role of mammotome in breast lesions
• The role of interventional imaging procedures in the treatment of selected gynecological disorders
• Role of transcranial ultrasound in diagnosis of neonatal brain insults
• Role of multidetector CT virtual laryngoscopy in evaluation of laryngeal mass lesions
• Evaluation of adnexal masses on sonomorphology and color Doppler imaginig
• Role of radiological imaging in diagnosis of endometrial carcinoma
• Comprehensive imaging of renal masses by magnetic resonance imaging
• The role of 3D & 4D ultrasonography in abnormalities of fetal abdomen
• Diffusion weighted magnetic resonance imaging in diagnosis and characterization of brain tumors in correlation with conventional MRI
• Role of diffusion weighted MRI imaging in evaluation of cancer prostate
• Role of multidetector CT in diagnosis of urinary bladder cancer
• Role of multidetector computed tomography in the evaluation of paediatric retroperitoneal masses.
• Comparative evaluation of gastric lesions by double contrast barium upper G.I. and multi detector computed tomography
• Evaluation of hepatic fibrosis in chronic liver disease using ultrasound elastography
• Role of MRI in assessment of hydrocephalus in pediatric patients
• The role of sonoelastography in characterization of breast lesions
• The influence of volumetric tumor doubling time on survival of patients with intracranial tumours
• Role of perfusion computed tomography in characterization of colonic lesions
• Role of proton MRI spectroscopy in the evaluation of temporal lobe epilepsy
• Role of Doppler ultrasound and multidetector CT angiography in evaluation of peripheral arterial diseases.
• Role of multidetector computed tomography in paranasal sinus pathologies
• Role of virtual endoscopy using MDCT in detection & evaluation of gastric pathologies
• High resolution 3 Tesla MRI in the evaluation of ankle and hindfoot pain.
• Transperineal ultrasonography in infants with anorectal malformation
• CT portography using MDCT versus color Doppler in detection of varices in cirrhotic patients
• Role of CT urography in the evaluation of a dilated ureter
• Characterization of pulmonary nodules by dynamic contrast-enhanced multidetector CT
• Comprehensive imaging of acute ischemic stroke on multidetector CT
• The role of fetal MRI in the diagnosis of intrauterine neurological congenital anomalies
• Role of Multidetector computed tomography in pediatric chest masses
• Multimodality imaging in the evaluation of palpable & non-palpable breast lesion.
• Sonographic Assessment Of Fetal Nasal Bone Length At 11-28 Gestational Weeks And Its Correlation With Fetal Outcome.
• Role Of Sonoelastography And Contrast-Enhanced Computed Tomography In Evaluation Of Lymph Node Metastasis In Head And Neck Cancers
• Role Of Renal Doppler And Shear Wave Elastography In Diabetic Nephropathy
• Evaluation Of Relationship Between Various Grades Of Fatty Liver And Shear Wave Elastography Values
• Evaluation and characterization of pelvic masses of gynecological origin by USG, color Doppler and MRI in females of reproductive age group
• Radiological evaluation of small bowel diseases using computed tomographic enterography
• Role of coronary CT angiography in patients of coronary artery disease
• Role of multimodality imaging in the evaluation of pediatric neck masses
• Role of CT in the evaluation of craniocerebral trauma
• Role of magnetic resonance imaging (MRI) in the evaluation of spinal dysraphism
• Comparative evaluation of triple phase CT and dynamic contrast-enhanced MRI in patients with liver cirrhosis
• Evaluation of the relationship between carotid intima-media thickness and coronary artery disease in patients evaluated by coronary angiography for suspected CAD
• Assessment of hepatic fat content in fatty liver disease by unenhanced computed tomography
• Correlation of vertebral marrow fat on spectroscopy and diffusion-weighted MRI imaging with bone mineral density in postmenopausal women.
• Comparative evaluation of CT coronary angiography with conventional catheter coronary angiography
• Ultrasound evaluation of kidney length & descending colon diameter in normal and intrauterine growth-restricted fetuses
• A prospective study of hepatic vein waveform and splenoportal index in liver cirrhosis: correlation with child Pugh’s classification and presence of esophageal varices.
• CT angiography to evaluate coronary artery by-pass graft patency in symptomatic patient’s functional assessment of myocardium by cardiac MRI in patients with myocardial infarction
• MRI evaluation of HIV positive patients with central nervous system manifestations
• MDCT evaluation of mediastinal and hilar masses
• Evaluation of rotator cuff & labro-ligamentous complex lesions by MRI & MRI arthrography of shoulder joint
• Role of imaging in the evaluation of soft tissue vascular malformation
• Role of MRI and ultrasonography in the evaluation of multifidus muscle pathology in chronic low back pain patients
• Role of ultrasound elastography in the differential diagnosis of breast lesions
• Role of magnetic resonance cholangiopancreatography in evaluating dilated common bile duct in patients with symptomatic gallstone disease.
• Comparative study of CT urography & hybrid CT urography in patients with haematuria.
• Role of MRI in the evaluation of anorectal malformations
• Comparison of ultrasound-Doppler and magnetic resonance imaging findings in rheumatoid arthritis of hand and wrist
• Role of Doppler sonography in the evaluation of renal artery stenosis in hypertensive patients undergoing coronary angiography for coronary artery disease.
• Comparison of radiography, computed tomography and magnetic resonance imaging in the detection of sacroiliitis in ankylosing spondylitis.
• Mr evaluation of painful hip
• Role of MRI imaging in pretherapeutic assessment of oral and oropharyngeal malignancy
• Evaluation of diffuse lung diseases by high resolution computed tomography of the chest
• Mr evaluation of brain parenchyma in patients with craniosynostosis.
• Diagnostic and prognostic value of cardiovascular magnetic resonance imaging in dilated cardiomyopathy
• Role of multiparametric magnetic resonance imaging in the detection of early carcinoma prostate
• Role of magnetic resonance imaging in white matter diseases
• Role of sonoelastography in assessing the response to neoadjuvant chemotherapy in patients with locally advanced breast cancer.
• Role of ultrasonography in the evaluation of carotid and femoral intima-media thickness in predialysis patients with chronic kidney disease
• Role of H1 MRI spectroscopy in focal bone lesions of peripheral skeleton choline detection by MRI spectroscopy in breast cancer and its correlation with biomarkers and histological grade.
• Ultrasound and MRI evaluation of axillary lymph node status in breast cancer.
• Role of sonography and magnetic resonance imaging in evaluating chronic lateral epicondylitis.
• Comparative of sonography including Doppler and sonoelastography in cervical lymphadenopathy.
• Evaluation of Umbilical Coiling Index as Predictor of Pregnancy Outcome.
• Computerized Tomographic Evaluation of Azygoesophageal Recess in Adults.
• Lumbar Facet Arthropathy in Low Backache.
• “Urethral Injuries After Pelvic Trauma: Evaluation with Uretrography
• Role Of Ct In Diagnosis Of Inflammatory Renal Diseases
• Role Of Ct Virtual Laryngoscopy In Evaluation Of Laryngeal Masses
• “Ct Portography Using Mdct Versus Color Doppler In Detection Of Varices In
• Cirrhotic Patients”
• Role Of Multidetector Ct In Characterization Of Renal Masses
• Role Of Ct Virtual Cystoscopy In Diagnosis Of Urinary Bladder Neoplasia
• Role Of Multislice Ct In Diagnosis Of Small Intestine Tumors
• “Mri Flow Quantification In The Assessment Of The Commonest CSF Flow Abnormalities”
• “The Role Of Fetal Mri In Diagnosis Of Intrauterine Neurological CongenitalAnomalies”
• Role Of Transcranial Ultrasound In Diagnosis Of Neonatal Brain Insults
• “The Role Of Interventional Imaging Procedures In The Treatment Of Selected Gynecological Disorders”
• Role Of Radiological Imaging In Diagnosis Of Endometrial Carcinoma
• “Role Of High-Resolution Ct In Differentiation Between Benign And Malignant Pulmonary Nodules In Children”
• Role Of Ultrasonography In The Diagnosis Of Knee Joint Lesions
• “Role Of Diagnostic Imaging Modalities In Evaluation Of Post Liver Transplantation Recipient Complications”
• “Diffusion-Weighted Magnetic Resonance Imaging In Diagnosis And
• Characterization Of Brain Tumors In Correlation With Conventional Mri”
• The Role Of PET-CT In The Evaluation Of Hepatic Tumors
• “Role Of Computerized Tomography In Evaluation Of Mediastinal Masses In Pediatric patients”
• “Trans Vaginal Ultrasound And Magnetic Resonance Imaging In Female Urinary Incontinence”
• Role Of Multidetector Ct In Diagnosis Of Urinary Bladder Cancer
• “Role Of Transvaginal Ultrasound In Diagnosis And Treatment Of Female Infertility”
• Role Of Diffusion-Weighted Mri Imaging In Evaluation Of Cancer Prostate
• “Role Of Positron Emission Tomography With Computed Tomography In Diagnosis Of Cancer Thyroid”
• The Role Of CT Urography In Case Of Haematuria
• “Value Of Ultrasonography In Assessment Of Acute Abdominal Diseases In Pediatric Age Group”
• “Role Of Functional Magnetic Resonance Imaging In Making Brain Tumor Surgery Safer”
• The Role Of Sonoelastography In Characterization Of Breast Lesions
• “Ultrasonography, Magnetic Resonance Cholangiopancreatography (MRCP) In Assessment Of Pediatric Biliary Lesions”
• “Role Of Ultrasound And Color Doppler Imaging In Assessment Of Acute Abdomen Due To Female Genital Causes”
• “Role Of Multidetector Ct Virtual Laryngoscopy In Evaluation Of Laryngeal Mass Lesions”
• MRI Of The Postoperative Knee
• Role Of Mri In Assessment Of Valvular Heart Diseases
• The Role Of 3D & 4D Ultrasonography In Abnormalities Of Fetal Abdomen
• State Of The Art Of Mri In Diagnosis Of Hepatic Focal Lesions
• Role Of Multidetector Ct In Diagnosis Of Salivary Gland Lesions
• “Role Of Virtual Endoscopy Using Mdct In Detection & Evaluation Of Gastric Pathologies”
• The Role Of Ultrasound & Mri In Acute Pelvic Inflammatory Disease
• “Diagnosis & Staging Of Liver Fibrosis By Ultraso Und Elastography In
• Patients With Chronic Liver Diseases”
• Role Of Mri In Evaluation Of Spinal Trauma
• Validity Of Mri In Diagnosis Of Congenital Anorectal Anomalies
• Imaging Of Vascular Complication After Liver Transplantation
• “Contrast-Enhanced Digital Mammography And Digital Breast Tomosynthesis In Early Diagnosis Of Breast Lesion”
• Role Of Mammotome In Breast Lesions
• “Role Of MRI Diffusion Tensor Imaging (DTI) In Assessment Of Traumatic Spinal Cord Injuries”
• “Prediction Of Pre-eclampsia And Fetal Growth Restriction By Uterine Artery Doppler”
• “Role Of Multidetector Row Computed Tomography In Assessment Of Maxillofacial Trauma”
• “Role Of Diffusion Magnetic Resonance Imaging In Assessment Of Neoplastic And Inflammatory Brain Lesions”
• Role Of Diffusion Mri In Preoperative Evaluation Of Brain Neoplasms
• “Role Of Multidetector Ct Virtual Hysteroscopy In The Detection Of The
• Uterine & Tubal Causes Of Female Infertility”
• Role Of Advances Magnetic Resonance Imaging Sequences In Multiple Sclerosis Magnetic Resonance Spectroscopy In Multiple Sclerosis
• “Role Of Conventional Mri, And Diffusion Tensor Imaging Tractography In Evaluation Of Congenital Brain Malformations”
• Role Of MRI In Evaluation Of Spinal Trauma
• Diagnostic Role Of Diffusion-weighted MR Imaging In Neck Masses
• “The Role Of Transvaginal Ultrasound Versus Magnetic Resonance Imaging In Diagnosis & Evaluation Of Cancer Cervix”
• “Role Of 3d Magnetic Resonance Imaging Tractography In Assessment Of White Matter Tracts Compromise In Supra Tentorial Tumors”
• Role Of Proton MR Spectroscopy In The Evaluation Of Temporal Lobe Epilepsy
• Role Of Multislice Computed Tomography In Evaluation Of Crohn’s Disease
• Role Of MRI In Assessment Of Hydrocephalus In Pediatric Patients
• The Role Of MRI In Diagnosis And Staging Of Urinary Bladder Carcinoma
• USG and MRI correlation of congenital CNS anomalies
• HRCT in interstitial lung disease
• X-Ray, CT and MRI correlation of bone tumors
• “Study on the diagnostic and prognostic utility of X-Rays for cases of pulmonary tuberculosis under RNTCP”
• “Role of magnetic resonance imaging in the characterization of female adnexal  pathology”
• “CT angiography of carotid atherosclerosis and NECT brain in cerebral ischemia, a correlative analysis”
• Role of CT scan in the evaluation of paranasal sinus pathology
• USG and MRI correlation on shoulder joint pathology
• “Radiological evaluation of a patient presenting with extrapulmonary tuberculosis”
• CT and MRI correlation in focal liver lesions”
• Comparison of MDCT virtual cystoscopy with conventional cystoscopy in bladder tumors”
• “Bleeding vessels in life-threatening hemoptysis: Comparison of 64 detector row CT angiography with conventional angiography prior to endovascular management”
• “Role of transarterial chemoembolization in unresectable hepatocellular carcinoma”
• “Comparison of color flow duplex study with digital subtraction angiography in the evaluation of peripheral vascular disease”
• “A Study to assess the efficacy of magnetization transfer ratio in differentiating tuberculoma from neurocysticercosis”
• “MR evaluation of uterine mass lesions in correlation with transabdominal, transvaginal ultrasound using HPE as a gold standard”
• “The Role of power Doppler imaging with trans rectal ultrasonogram guided prostate biopsy in the detection of prostate cancer”
• “Lower limb arteries assessed with doppler angiography – A prospective comparative study with multidetector CT angiography”
• “Comparison of sildenafil with papaverine in penile doppler by assessing hemodynamic changes”
• “Evaluation of efficacy of sonosalphingogram for assessing tubal patency in infertile patients with hysterosalpingogram as the gold standard”
• Role of CT enteroclysis in the evaluation of small bowel diseases
• “MRI colonography versus conventional colonoscopy in the detection of colonic polyposis”
• “Magnetic Resonance Imaging of anteroposterior diameter of the midbrain – differentiation of progressive supranuclear palsy from Parkinson disease”
• “MRI Evaluation of anterior cruciate ligament tears with arthroscopic correlation”
• “The Clinicoradiological profile of cerebral venous sinus thrombosis with prognostic evaluation using MR sequences”
• “Role of MRI in the evaluation of pelvic floor integrity in stress incontinent patients” “Doppler ultrasound evaluation of hepatic venous waveform in portal hypertension before and after propranolol”
• “Role of transrectal sonography with colour doppler and MRI in evaluation of prostatic lesions with TRUS guided biopsy correlation”
• “Ultrasonographic evaluation of painful shoulders and correlation of rotator cuff pathologies and clinical examination”
• “Colour Doppler Evaluation of Common Adult Hepatic tumors More Than 2 Cm  with HPE and CECT Correlation”
• “Clinical Relevance of MR Urethrography in Obliterative Posterior Urethral Stricture”
• “Prediction of Adverse Perinatal Outcome in Growth Restricted Fetuses with Antenatal Doppler Study”
• Radiological evaluation of spinal dysraphism using CT and MRI
• “Evaluation of temporal bone in cholesteatoma patients by high resolution computed tomography”
• “Radiological evaluation of primary brain tumours using computed tomography and magnetic resonance imaging”
• “Three dimensional colour doppler sonographic assessment of changes in  volume and vascularity of fibroids – before and after uterine artery embolization”
• “In phase opposed phase imaging of bone marrow differentiating neoplastic lesions”
• “Role of dynamic MRI in replacing the isotope renogram in the functional evaluation of PUJ obstruction”
• Characterization of adrenal masses with contrast-enhanced CT – washout study
• A study on accuracy of magnetic resonance cholangiopancreatography
• “Evaluation of median nerve in carpal tunnel syndrome by high-frequency ultrasound & color doppler in comparison with nerve conduction studies”
• “Correlation of Agatston score in patients with obstructive and nonobstructive coronary artery disease following STEMI”
• “Doppler ultrasound assessment of tumor vascularity in locally advanced breast cancer at diagnosis and following primary systemic chemotherapy.”
• “Validation of two-dimensional perineal ultrasound and dynamic magnetic resonance imaging in pelvic floor dysfunction.”
• “Role of MR urethrography compared to conventional urethrography in the surgical management of obliterative urethral stricture.”

Search Diagnostic Imaging Research Topics

You can also search research-related resources and direct download PDFs for radiology articles on our custom radiology search engine .

Free Resources for Preparing Radiology Thesis

• Radiology thesis topics- Benha University – Free to download thesis
• Radiology thesis topics – Faculty of Medical Science Delhi
• Radiology thesis topics – IPGMER
• Fetal Radiology thesis Protocols
• Radiology thesis and dissertation topics
• Radiographics

Proofreading Your Thesis:

Make sure you use Grammarly to correct your spelling ,  grammar , and plagiarism for your thesis. Grammarly has affordable paid subscriptions, windows/macOS apps, and FREE browser extensions. It is an excellent tool to avoid inadvertent spelling mistakes in your research projects. It has an extensive built-in vocabulary, but you should make an account and add your own medical glossary to it.

Guidelines for Writing a Radiology Thesis:

These are general guidelines and not about radiology specifically. You can share these with colleagues from other departments as well. Special thanks to Dr. Sanjay Yadav sir for these. This section is best seen on a desktop. Here are a couple of handy presentations to start writing a thesis:

Read the general guidelines for writing a thesis (the page will take some time to load- more than 70 pages!

A format for thesis protocol with a sample patient information sheet, sample patient consent form, sample application letter for thesis, and sample certificate.

Resources and References:

• Guidelines for thesis writing.
• Format for thesis protocol
• Thesis protocol writing guidelines DNB
• Informed consent form for Research studies from AIIMS 
• Radiology Informed consent forms in local Indian languages.
• Sample Informed Consent form for Research in Hindi
• Guide to write a thesis by Dr. P R Sharma
• Guidelines for thesis writing by Dr. Pulin Gupta.
• Preparing MD/DNB thesis by A Indrayan
• Another good thesis reference protocol

Hopefully, this post will make the tedious task of writing a Radiology thesis a little bit easier for you. Best of luck with writing your thesis and your residency too!

More guides for residents :

Guide for the md/dmrd/dnb radiology exam.

• Guide for First-Year Radiology Residents
• FRCR Exam: THE Most Comprehensive Guide (2022)!
• Radiology Practical Exams Questions compilation for MD/DNB/DMRD !
• Radiology Exam Resources (Oral Recalls, Instruments, etc )!
• Tips and Tricks for DNB/MD Radiology Practical Exam
• FRCR 2B exam- Tips and Tricks !
• FRCR exam preparation – An alternative take!

Why did I take up Radiology?

• Radiology Conferences – A comprehensive guide!
• ECR (European Congress Of Radiology)
• European Diploma in Radiology (EDiR) – The Complete Guide!
• Radiology NEET PG guide – How to select THE best college for post-graduation in Radiology (includes personal insights)!
• Interventional Radiology – All Your Questions Answered!
• What It Means To Be A Radiologist: A Guide For Medical Students!
• Radiology Mentors for Medical Students (Post NEET-PG)
• MD vs DNB Radiology: Which Path is Right for Your Career?
• DNB Radiology OSCE – Tips and Tricks

More radiology resources here: Radiology resources This page will be updated regularly. Kindly leave your feedback in the comments or send us a message here . Also, you can comment below regarding your department’s thesis topics.

Note: All topics have been compiled from available online resources. If anyone has an issue with any radiology thesis topics displayed here, you can message us here , and we can delete them. These are only sample guidelines. Thesis guidelines differ from institution to institution.

Image source: Thesis complete! (2018). Flickr. Retrieved 12 August 2018, from https://www.flickr.com/photos/cowlet/354911838 by Victoria Catterson

About The Author

Dr. amar udare, md, related posts ↓.

9 thoughts on “Radiology Thesis – More than 400 Research Topics (2022)!”

Amazing & The most helpful site for Radiology residents…

Thank you for your kind comments 🙂

Dr. I saw your Tips is very amazing and referable. But Dr. Can you help me with the thesis of Evaluation of Diagnostic accuracy of X-ray radiograph in knee joint lesion.

Wow! These are excellent stuff. You are indeed a teacher. God bless

Glad you liked these!

happy to see this

Glad I could help :).

Greetings Dr, thanks for your constant guides. pls Dr, I need a thesis research material on “Retrieving information from scattered photons in medical imaging”

Hey! Unfortunately I do not have anything relevant to that thesis topic.

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Modern trends in interventional radiology

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Tarun Sabharwal, Nicos Fotiadis, Andreas Adam, Modern trends in interventional radiology, British Medical Bulletin , Volume 81-82, Issue 1, 2007, Pages 167–182, https://doi.org/10.1093/bmb/ldm006

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To review the current applications of Interventional Radiology (IR), outline newer technologies and techniques and emphasize the role of Interventional Radiologists as clinical practitioners.

IR is a clinical modality that makes use of imaging guidance for the performance of minimally invasive treatment. The development of new imaging technologies and interventional devices has greatly increased the number of medical conditions that may now be treated by IR.

Promising new treatments in cancer therapy, the treatment of fibroids, venous access and spine interventions as well as advances in non-invasive vascular imaging, pharmacological therapies and peripheral arterial and venous interventions are providing exciting opportunities for IR, attracting significant patient interest and promising tremendous public benefit.

• pharmacotherapy
• interventional radiology
• diagnostic imaging
• uterine fibroids
• cancer therapy
• radiofrequency ablation
• venous access
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Prime Time for Artificial Intelligence in Interventional Radiology

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• Published: 14 January 2022
• Volume 45 , pages 283–289, ( 2022 )

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• Jarrel Seah 1 , 2 ,
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Machine learning techniques, also known as artificial intelligence (AI), is about to dramatically change workflow and diagnostic capabilities in diagnostic radiology. The interest in AI in Interventional Radiology is rapidly gathering pace. With this early interest in AI in procedural medicine, IR could lead the way to AI research and clinical applications for all interventional medical fields. This review will address an overview of machine learning, radiomics and AI in the field of interventional radiology, enumerating the possible applications of such techniques, while also describing techniques to overcome the challenge of limited data when applying these techniques in interventional radiology. Lastly, this review will address common errors in research in this field and suggest pathways for those interested in learning and becoming involved about AI.

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Introduction

Artificial intelligence (AI) has been prominent in different fields including diagnostic radiology. AI breakthroughs are now also empowering the field of interventional radiology (IR) and recent surge in popularity was initially driven by the phenomenal success of deep neural networks in processing unstructured data such as images and audio through pattern recognition. The term AI has come to encompass all forms of machine learning (ML). In this review the term AI is used synonymously with ML, referring to techniques that construct predictive models from data, including deep learning, radiomics and other traditional machine learning techniques. The importance of the usage of AI is summarized in (Table 1 ).

A brief history of the field is difficult to summarize given its fragmented nature; however, the most popular techniques currently, artificial neural networks, date back to work by Rosenblatt[ 1 ] on the concept of the perceptron in the 1960s.

Artificial neural networks are inspired by the connectionist design of biological neural networks[ 2 ], comprising of artificial “neurons” which receive input from other neurons or the environment, performing a nonlinear activation function[ 3 ] on the sum of this input, and passing its output to other neurons. In short, each layer in an artificial neural network is a mathematical model loosely mimicking biological neurons, receiving information from one or more sources, processing the information and producing a response. This information is passed to other neurons for further analysis. To train a neural network, a large dataset of paired inputs and desired outputs, referred to as labels, must be collected. The training process then refers to altering the parameters (typically the weights) for each neuron such that the network produces the desired outputs for each input. Currently, the most popular architecture for processing images is the convolutional neural network (CNN) [ 4 ], where neurons are organized spatially, taking inputs from adjacent pixels and processing it into higher-order representations before passing this information onto successive layers, eventually producing predictions. These predictions are compared to the labels, and the gradient of the parameters of the network, with respect to the error, is calculated through a process referred to as backpropagation[ 5 ]. Supervised learning is a type of ML that utilizes a set of input and output labelled training data [ 6 ] and can be used to estimate relationships between input and output parameters.

AI using artificial neural networks were recently re-popularized in 2012 by Krizhevsky et al. [ 7 ] with the development of large-scale parallel processing through graphics processing units combined with the availability of large datasets. In their revolutionary publication in 2012, Krizhevsky et al. [ 7 ] trained a deep CNN to classify over 1 million images (from the ImageNet contest) into 1000 different classes. Their network was built with 650,000 neurons and 60 million parameters. Though networks have since become even more complex, the AlexNet model created by Krizhevsky remains a reference for image classification. It is important to acknowledge that CNNs are still at the core of most research in this field at this nascent stage.

Recent research in medical imaging AI has focused on 3D CNNs such as 3D versions of popular 2D CNNs such as EfficientNet or Densenet. [ 8 ] Alternative techniques such as 2.5D networks (taking into account axial, coronal and sagittal planes) have also been developed. [ 9 ]. Even more recently in the wider AI space is the shift to purely attention-based mechanisms such as vision transformers [ 10 ] even for image-based tasks. These popular architectures are mentioned in brief as it is beyond the scope of this review article to describe these in detail. These approaches work remarkably well when networks are scaled up to millions or billions of parameters. Where traditional ML algorithms such as logistic regression tend to overfit with so many parameters, neural networks remarkably appear to work better with more parameters, particularly when multiple layers are stacked deeply. [ 11 ] Why this is the case remains one of the central mysteries of deep learning.

One limitation of neural networks is that most deep learning algorithms have been restricted to data-rich domains such as photography or speech recognition, as training these algorithms requires large datasets. Radiology is well suited to this, being a data-rich specialty born into the information age, with explosive growth in AI research in diagnostic radiology[ 12 ]. Interventional radiology when compared to diagnostic radiology deals with much smaller datasets and may seem much less appealing to the AI researcher.

In comparison with other interventional and procedural fields such as surgery or endoscopy, IR is data-rich. IR is one of a few specialties where a record is kept of the entire procedure in a standardized format, is available retrospectively and these datasets are mostly unexploited today. With the recent developments in few shot learning made by the deep learning community[ 13 ], novel techniques may drastically reduce the dataset size required for clinically effective algorithms, making it truly the prime time for AI in interventional radiology.

Applications

Potential applications of AI in IR can be divided into pre-procedural, peri-procedural and post procedural.

Pre-procedural Setting: Improving Patient Selection

Better patient selection is similar to the concept of precision medicine. AI decision support systems may help tailor treatment decisions based on imaging phenotypes, yielding better clinical results.

Interventional radiologists often rely on multidisciplinary boards for oncological treatment strategies. These board discussions perform multiparametric risk-stratification, integrating the patient’s full data before a treatment is advised.

Several AI applications replicate and outperform these discussions by predicting the outcome from data available in each specialty (radiology, histology, molecular biology, etc.). The ability to incorporate clinical information, radiomics and genetic information may improve the objectivity and accuracy of decision-making. Such an approach could potentially play a role in triaging patients for IR and subsequent therapy by assessing risks and making predictions about therapeutic outcomes [ 14 ]

An example of this application in the field of acute ischaemic stroke is the use of CT perfusion software in endovascular clot retrieval to estimate physiological parameters such as ischaemic core and penumbral volume, facilitating the selection of patients who are likely to have an optimal outcome. AI approaches to identifying patients for clot retrieval are being investigated, for instance based on CT angiography rather than CT perfusion. [ 15 , 16 , 17 ]

Pretreatment patient selection using deep learning is also being investigated for interventional oncology. Morshid et al. [ 18 ] describe an algorithm to predict response to transcatheter arterial chemoembolization for hepatocellular carcinoma (HCC) using pretreatment CT, combined with the clinical BCLC stage. They demonstrate that an AI model utilizing image and clinical features can outperform traditional staging systems in predicting benefit from TACE. Similarly, Peng et al.[ 19 ] developed a deep learning model that predicts response to TACE with an accuracy of 84%. Kim et al. [ 20 ] demonstrated that a combined radiomics and clinical model of HCC in response to TACE improved survival estimation when compared to clinical models alone. Other research in HCC treatment has found similar results when applied to surgical or thermo-ablative resection [ 21 ].

Multimodal planning may also integrate genetic information using AI models. Ziv et al. [ 22 ] trained a model to identify the genes most predictive of response to TACE and Kuo et al. [ 23 ] utilized radiomic analysis to identity imaging phenotypes associated with doxorubicin drug response gene expression in HCC.

Peri-procedural: Improving Procedures

AI can improve interventional procedures by accelerating computationally intensive or manual procedures, such as the correction of translational motion via pixel shifting in angiography. Traditional image registration techniques such as those proposed by Meijering et al. [ 24 ] are computationally intensive and have not had widespread uptake. Deep learning approaches may speed up corrected digital subtraction angiography, such as methods proposed by Gao et al. [ 25 ] which use generative adversarial networks to generate subtraction images without the preliminary non-contrast acquisition, avoiding the issue of translational motion entirely. This is achieved by acquiring a dataset of satisfactorily subtracted images paired with the unsubtracted images and training a neural network to predict the subtracted images from the unsubtracted image. This teaches the neural network anatomical and physical assumptions about the nature of angiographic contrast. The resultant neural network is capable of predicting the subtracted images from the unsubtracted angiographic images, without the use of the preliminary non-contrast acquisition.

Deep learning approaches have also been applied to identifying guidewire and catheters during angiography [ 26 ]. Such methods may permit more advanced algorithms such as virtual road mapping of the vasculature without contrast. Real-time AI registration algorithms could superimpose high-resolution preoperative imaging with procedural fluoroscopy, guiding the interventional radiologist during catheter manipulation.

AI-based ultrasound guidance [ 27 ] has been used in echocardiography to help guide the acquisition of echocardiograms. Deep learning algorithms estimate diagnostic quality of the image and suggest manoeuvres to improve the quality of such images. AI may provide recommendations on needle trajectory or other facets of interventional procedures, which may be particularly useful for novice operators.

The selection and personalization of endovascular devices is another area for AI. Yang et al. [ 28 ] used AI to segment and quantify stenosis on coronary angiography. Such algorithms could be used to objectively select the optimal stent for each lesion. Lee et al. [ 29 ] imagine a future where AI may guide the personalized 3D printing of cardiovascular stents.

Cho et al.[ 30 ] developed AI to predict fractional flow reserve of coronary lesions on angiography. This opens the way to extract hemodynamic parameters/physiological parameters from angiography, and AI may be able to even estimate flow distribution maps in the future.

AI has been proposed for skin dose estimation by taking into account angulation of the X-ray tube and tissue density. Radiation exposure during endoscopy has been reduced using an AI-equipped fluoroscopy unit with an ultrafast collimation system that reduced radiation exposure by ~ 38%. Similar techniques could be used in interventional radiology. [ 31 ]

Respiratory motion compensation in PET/CT imaging has been implemented via elastic motion correction algorithms where AI determines a blurring kernel between a single motion corrected image and a single non-motion corrected target image. This results in a final image with reduced motion[ 32 ]. Similar applications could be applied in live fluoroscopy.

After Treatment: Improving Follow-Up

Following treatment, AI has a role to play in measuring response to treatment, prognostication and determining future management.

Most criteria used in diagnostic radiology for treatment response were not developed for interventional radiology, which may lead to misevaluation during follow-up. AI research could help better assess these specific treatment responses. AI can be useful in longitudinal studies during follow-up of treatments to detect subtle changes between images identifying disease progress or recurrence earlier.

In oncology, automated volumetric measurements of tumour sizes or response evaluation criteria in solid tumours (RECIST) reads may be possible through deep learning. [ 33 ]. RECIST criteria themselves as a marker of response can be outperformed by AI. Dohan et al. [ 34 ] developed a radiomic signature that was able to predict overall survival and identify good responders better than RECIST1.1 criteria in patients with liver metastases from colorectal cancer treated with chemotherapy. The same models could be applied to IR treatment in liver metastases, potentially outperforming routine RECIST and equivalent criteria.

Procedural findings and histological features can also play a role in the choice of adjuvant therapy as suggested by Saillard et al.[ 35 ] who built a prediction model of survival after HCC resection based on pretherapeutic and histological preprocedural features.

Similarly, AI has a role in assessing response to treatment in acute ischaemic stroke. Thrombolysis in cerebral infarction (TICI) scores are often used to grade results following endovascular clot retrieval. AI can improve interobserver reliability and thereby improve the utility of such scores in prognosticating patients. [ 36 , 37 ] AI algorithms may help reduce the time required to interpret post-treatment imaging and improve inter-observer variability. The ability of AI to extract quantitative metrics holds the promise of personalizing management plans, particularly in complex chronic conditions such as cancer.

While genetics and molecular pathology have played a large role in precision medicine, pre- and post-treatment imaging may identify additional disease phenotypes as well as quantify intervention success, which may help fine-tune management by prognosticating as well as determining the timing and need for follow-up imaging. [ 38 ]

Practical Challenges

The breadth of potential applications of AI in interventional radiology has seen a rise in academic papers published on this subject. Such projects face a common set of challenges..

The major challenge facing AI in interventional radiology is the relatively small dataset sizes when compared to diagnostic radiology, or in fact, to other non-medical applications of AI entirely. For instance, ImageNet, a widely used natural imagery database, contains over 14 million images. [ 39 ] In contrast, most medical applications have dataset sizes in the hundreds to thousands of unique samples. Therefore, standard deep learning models are difficult to train from scratch.

Perhaps the most simple method to reduce the number of samples required for a useful model is transfer learning, where models trained on different datasets might be used as a starting point, as information that these models might have learned from other datasets may be translated to this setting as well. [ 40 ]

Other approaches include the use of handcrafted features, an approach popular in the “traditional” computer vision literature in the early 2000s. These approaches, also known as radiomics when applied to imaging, reduce the number of required samples as the model does not have to learn the low-level features itself.

Another approach is to use data augmentation – standard transformations like affine transforms, adjusting brightness and contrast are useful, but novel augmentation techniques like MixUp [ 41 ] may help researchers get more out of their data. Medical imaging is often acquired quite differently from natural imagery, through techniques such as tomographic reconstruction. This offers the opportunity for different types of augmentations to introduce artefacts which are more typical in this setting, such as physics-based data augmentation [ 42 ]. Although this technique was found to be unsuccessful in previous work, it may be prove to be useful in more challenging datasets.

Recent developments in the deep learning field in semi-supervised learning, such as few-shot and zero-shot learning techniques [ 13 , 43 , 44 , 45 , 46 ], may also help reduce the number of labelled samples required, by using unsupervised datasets as additional information.

For categorical data, such as models using clinical variables like age and sex, oversampling techniques such as synthetic minority oversampling technique (SMOTE) [ 47 ] may help generate synthetic data points that may improve an AI model’s performance.

Small dataset sizes also exacerbate common mistakes made in AI projects. The use of checklists [ 48 ] may help prevent some of these avoidable errors. Common errors include the failure to split data by patient – i.e. including studies from a single patient in both the training and test datasets. This may lead to the model memorizing patient specific features, leading to over-optimistic results that do not translate into clinical practice. Other errors include not fully describing the hyper-parameter optimization process, or optimizing the hyper-parameters on the testing set, which again leads to over-estimation of the model’s performance.

The training and testing datasets must also have defined inclusion and exclusion criteria to prevent “Frankenstein” datasets[ 48 ], where positive and negative cases are drawn from different sources, potentially leading to data leakage as the AI model may recognize features specific to the dataset source rather than the disease of interest.

To prevent overstating the significance of any result, especially in small datasets, any measure of performance such as the accuracy or the area under the receiver operating characteristic (AUC) should be accompanied with confidence intervals. When applicable, AI models trained on images should be compared to a baseline clinical model using age, sex and other clinical features. If an AI model uses both imaging and clinical features, a sensitivity analysis should be performed by systematically modifying each input to assess its contribution on the final prediction. [ 49 ] In post-treatment and prognostication models, lack of complete follow-up in all participants is common and should be accounted for when measuring the accuracy of such models through censoring. [ 50 ]

Another source of unreliability stems from the constant evolution of clinical practice over time due to the introduction of new treatment approaches, technologies or changes in patient population [ 51 ]. Interventional radiology, in particular, is a rapidly evolving specialty with novel equipment and procedures constantly developing over time.

The use of AI in augmenting interventional radiologists is likely to increase as research in pretreatment, intra-treatment and post-treatment applications translate into clinical practice. Due to the potential benefits and risks for patients, stringent prospective evaluation such as controlled trials should be undertaken where necessary to ensure that promising applications translate well.

How to Get Started in Ai

Given the promise of AI in interventional radiology many clinicians may wish to get involved in AI research and development. Key factors to be able to successfully translate a project into clinical practice include a clear understanding of the clinical benefits andadvantages of using AI, the availability of data measured in independent samples (typically at a patient level), the use of computing resources such as graphics processing units or tensor processing units and the technical skills to construct an AI model.

Specific steps around training and coding of AI models are beyond the scope of this review article; however, it is becoming easier the advent of open source deep learning framework libraries such as Pytorch [ 52 ] and Tensorflow [ 53 ]. A recommendation for interventional radiologists who are interested in learning more about AI is to begin with learning basic software and data carpentry skills in programming languages such as Python [ 54 ] and then expand knowledge by undertaking courses in frameworks such as Pytorch and Tensorflow.

The emergence of novel deep learning techniques and applications in interventional radiology is hugely exciting and offers multiple opportunities to aid in patient selection for intervention, improve patient care during interventional treatment and optimize post-treatment clinical follow-up. Interventional radiology with its smaller dataset sizes compared to diagnostic radiology stands to benefit from novel techniques such as semi-supervised learning, zero and few shot learning in the deep learning literature. The application of such techniques in interventional radiology must be rigorous and generalizable, and common errors must be avoided in order for successful clinical translation.

Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev. 1958;65:386–408. https://doi.org/10.1037/h0042519 .

Article   CAS   PubMed   Google Scholar  

Crick F. The recent excitement about neural networks. Nature. 1989;337:129–32.

Article   CAS   Google Scholar  

Fukushima,. Visual Feature Extraction by a Multilayered Network of Analog Threshold Elements. IEEE Trans Syst Sci Cybern. 1969;5:322–33. https://doi.org/10.1109/TSSC.1969.300225 .

Article   Google Scholar  

LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86:2278–323. https://doi.org/10.1109/5.726791 .

Linnainmaa S. Taylor expansion of the accumulated rounding error. BIT. 1976;16:146–60. https://doi.org/10.1007/BF01931367 .

B L, CJ W, J C,. The role of artificial intelligence in interventional oncology: a primer. J Vasc Interv Radiol. 2019;30:38-41.e1. https://doi.org/10.1016/J.JVIR.2018.08.032 .

Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Adv Neural Inform Process Syst. 2012;25:1097–105.

Google Scholar  

Liang S, Zhang R, Liang D, et al. Multimodal 3D DenseNet for IDH genotype prediction in gliomas. Genes. 2018. https://doi.org/10.3390/GENES9080382 .

Article   PubMed   PubMed Central   Google Scholar  

Yang J, Huang X, He Y, et al Reinventing 2D convolutions for 3D images. 2017;1

Dosovitskiy A, Beyer L, Kolesnikov A, et al An image is worth 16x16 words: transformers for image recognition at scale. 2020

Tan M, Le Q V. EfficientNet: rethinking model scaling for convolutional neural networks. 36th Int Conf Mach Learn ICML. 2019;10691–10700

Bluemke DA, Moy L, Bredella MA, et al. Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers-from the radiology editorial board. Radiology. 2020;294:487–9.

Wang Y, Yao Q, Kwok JT, Ni LM. Generalizing from a few examples: a survey on few-shot learning. ACM Comput Surv. 2020;53:1–34. https://doi.org/10.1145/3386252 .

Keshava SN, Kalva S. Artificial Intelligence in Interventional Radiology. J Clin Interv Radiol ISVIR. 2019;03:071–071. https://doi.org/10.1055/S-0039-1694309 .

Hilbert A, Ramos LA, van Os HJA, et al. Data-efficient deep learning of radiological image data for outcome prediction after endovascular treatment of patients with acute ischemic stroke. Comput Biol Med. 2019;115: 103516. https://doi.org/10.1016/j.compbiomed.2019.103516 .

Zeleňák K, Krajina A, Meyer L, Fiehler J, Intelligence EA, Behme D, Bulja D, Caroff J, Chotai AA, Da Ros V, Gentric JC. How to improve the management of acute ischemic stroke by modern technologies, artificial intelligence, and new treatment methods. Life. 2021;11(6):488.

Hofmeister J, Bernava G, Rosi A, et al. Clot-based radiomics predict a mechanical thrombectomy strategy for successful recanalization in acute ischemic stroke. Stroke. 2020;51:2488–94. https://doi.org/10.1161/STROKEAHA.120.030334 .

Morshid A, Elsayes KM, Khalaf AM, et al. A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization. Radiol Artif Intell. 2019;1: e180021. https://doi.org/10.1148/ryai.2019180021 .

Peng J, Kang S, Ning Z, et al. Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging. Eur Radiol. 2020;30:413–24. https://doi.org/10.1007/s00330-019-06318-1 .

Article   PubMed   Google Scholar  

Kim J, Choi SJ, Lee SH, Lee HY, Park H. Predicting survival using pretreatment ct for patients with hepatocellular carcinoma treated with transarterial chemoembolization: comparison of models using radiomics. Am J Roentgenol. 2018;1:1026–34.

Ma X, Wei J, Gu D, et al. Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT. Eur Radiol. 2019;29:3595–605. https://doi.org/10.1007/s00330-018-5985-y .

Ziv E, Yarmohammadi H, Boas FE, et al. Gene signature associated with upregulation of the wnt/β-catenin signaling pathway predicts tumor response to transarterial embolization. J Vasc Interv Radiol. 2017;28:349-355.e1. https://doi.org/10.1016/j.jvir.2016.11.004 .

Md K, J G, CB S, et al. Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma. J Vasc Interv Radiol. 2007;18:821–30. https://doi.org/10.1016/J.JVIR.2007.04.031 .

Meijering EHW, Zuiderveld KJ, Viergever MA. Image registration for digital subtraction angiography. Int J Comput Vis. 1999;31:227–46. https://doi.org/10.1023/a:1008074100927 .

Gao Y, Song Y, Yin X, et al. Deep learning-based digital subtraction angiography image generation. Int J Comput Assist Radiol Surg. 2019;14:1775–84.

Ambrosini P, Ruijters D, Niessen WJ, et al. Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). 2017;10434:577–85.

Narang A, Bae R, Hong H, et al. Utility of a deep-learning algorithm to guide novices to acquire echocardiograms for limited diagnostic use. JAMA Cardiol. 2021. https://doi.org/10.1001/jamacardio.2021.0185 .

Yang S, Kweon J, Roh JH, et al. Deep learning segmentation of major vessels in X-ray coronary angiography. Sci Rep. 2019;9:1–11. https://doi.org/10.1038/s41598-019-53254-7 .

Lee Y, Veerubhotla K, Jeong MH, Lee CH. Deep learning in personalization of cardiovascular stents. J Cardiovasc Pharmacol Ther. 2020;25:110–20.

Cho H, Lee JG, Kang SJ, et al. Angiography-based machine learning for predicting fractional flow reserve in intermediate coronary artery lesions. J Am Heart Assoc. 2019. https://doi.org/10.1161/JAHA.118.011685 .

Bang JY, Hough M, Hawes RH, Varadarajulu S. Use of artificial intelligence to reduce radiation exposure at fluoroscopy-guided endoscopic procedures. Am J Gastroenterol. 2020;115(4):555–61. https://doi.org/10.14309/ajg.0000000000000565 .

Y L, K F, T M, et al. Respiratory motion compensation for pet/ct with motion information derived from matched attenuation-corrected gated PET data. J Nucl Med. 2018;59:1480–6. https://doi.org/10.2967/JNUMED.117.203000 .

Tang Y, Harrison AP, Bagheri M, Xiao J, Summers RM. Semi-automatic recist labeling on ct scans with cascaded convolutional neural networks. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G, editors. Medical image computing and computer assisted intervention – MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part IV. Cham: Springer International Publishing; 2018. p. 405–13. https://doi.org/10.1007/978-3-030-00937-3_47 .

Chapter   Google Scholar  

Dohan A, Gallix B, Guiu B, et al. Early evaluation using a radiomic signature of unresectable hepatic metastases to predict outcome in patients with colorectal cancer treated with FOLFIRI and bevacizumab. Gut. 2020;69:531–9. https://doi.org/10.1136/gutjnl-2018-316407 .

Saillard C, Schmauch B, Laifa O, et al. Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides. Hepatology. 2020;72:2000–13. https://doi.org/10.1002/hep.31207 .

Su R, Cornelissen SAP, van der Sluijs M, et al AutoTICI: Automatic brain tissue reperfusion scoring on 2D DSA images of acute ischemic stroke patients. 2020; arXiv XX:1

Nielsen M, Waldmann M, Frölich AM, et al. Deep learning-based automated thrombolysis in cerebral infarction scoring: a timely proof-of-principle study. Stroke. 2021;52:3497–504. https://doi.org/10.1161/STROKEAHA.120.033807 .

Oakden-Rayner L, Carneiro G, Bessen T, et al. Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework. Sci Rep. 2017;7:1–13. https://doi.org/10.1038/s41598-017-01931-w .

Deng J, Dong W, Socher R, et al. Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. 2009; pp. 248–255

Bozinovski S. Reminder of the first paper on transfer learning in neural networks, 1976. Informatica. 2020. https://doi.org/10.31449/inf.v44i3.2828 .

Zhang H, Cisse M, Dauphin YN, Lopez-Paz D. mixup: Beyond Empirical Risk Minimization. 2017; arXiv

Omigbodun AO, Noo F, McNitt-Gray M, et al. The effects of physics-based data augmentation on the generalizability of deep neural networks: Demonstration on nodule false-positive reduction. Med Phys. 2019;46:4563–74. https://doi.org/10.1002/mp.13755 .

Paul A, Shen TC, Lee S, et al. Generalized zero-shot chest X-ray diagnosis through trait-guided multi-view semantic embedding with self-training. IEEE Trans Med Imaging. 2021. https://doi.org/10.1109/TMI.2021.3054817 .

Paul A, Tang YX, Shen TC, Summers RM. Discriminative ensemble learning for few-shot chest x-ray diagnosis. Med Image Anal. 2021;68: 101911. https://doi.org/10.1016/j.media.2020.101911 .

Grill J-B, Strub F, Altché F, et al. Bootstrap your own latent: a new approach to self-supervised Learning. 2020; arXiv

Sohn K, Berthelot D, Li C-L, et al. FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. 2020; arXiv

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: Synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–57. https://doi.org/10.1613/jair.953 .

Roberts M, Driggs D, Thorpe M, et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell. 2021;3:199–217. https://doi.org/10.1038/s42256-021-00307-0 .

Ankenbrand MJ, Shainberg L, Hock M, et al. Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI. BMC Med Imaging. 2021. https://doi.org/10.1186/s12880-021-00551-1 .

Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of covid-19: Systematic review and critical appraisal. BMJ. 2020;369:26. https://doi.org/10.1136/bmj.m1328 .

Avanzo M, Trianni A, Botta F, et al. Artificial intelligence and the medical physicist: welcome to the machine. Appl Sci. 2021. https://doi.org/10.3390/APP11041691 .

Paszke A, Gross S, Massa F, et al. PyTorch: An imperative style, high-performance deep learning library. 2019

Abadi M, Agarwal A, Barham P, et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems. 2016

Wilson G. Software carpentry: lessons learned. F1000Res. 2016;3:62. https://doi.org/10.12688/f1000research.3-62.v2 .

Article   PubMed Central   Google Scholar  

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Seah, J., Boeken, T., Sapoval, M. et al. Prime Time for Artificial Intelligence in Interventional Radiology. Cardiovasc Intervent Radiol 45 , 283–289 (2022). https://doi.org/10.1007/s00270-021-03044-4

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• v.91(Suppl 8); 2020

Diagnostic and interventional radiology: an update

Andrea giovagnoni.

1 Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Ancona, AN, Italy

Massimo De Filippo

2 Department of Medicine and Surgery (DiMec), Section of Radiology, University of Parma, Maggiore Hospital, Parma, Italy

Antonio Barile

3 Department of Biotechnology and Applied Clinical Sciences, University of L’Aquila, L’Aquila, Italy

In recent years, radiology has undergone revolutionary changes in all aspects of the discipline ( 1 - 10 ). The progressive and rapid innovation of technology has led on the one hand to ever more significant and new applications in the diagnostic field; on the other hand, it has opened up to interventional radiology therapeutic possibilities that are radically changing the clinical approach to numerous pathologies ( 11 - 15 ). Furthermore, the advent of artificial intelligence is unveiling a new scenario with which the radiologist of the future will have to confront, and which will undoubtedly lead to important implications in the conception of radiology ( 16 - 18 ). In this context of innovation, the clinical and - above all - global approach of radiology remains fundamental ( 19 - 21 ); it is for this reason that with this Special Issue entitled “Diagnostic and interventional radiology: an update” we wanted to deal with some focuses that summarized the foundations of diagnostic and interventional radiology topics in light of the relative “state of the art”.

In the first part of the volume, dedicated to abdominal imaging ( 22 - 24 ), the first two articles, “Hepatic tumors: pitfalls in diagnostic imaging” and “The role of imaging in surgical planning for liver resection” represent a guide to the radiologist who have to integrate the different and multimodal diagnostic techniques, to confront and being a point of reference for the clinicians and the surgeons in the patient’s therapeutic management ( 25 - 27 ).

The third article by Reginelli et al., “MRI of perianal fistulas in Chron’s disease”, also deals with a very frequent pathology, for which a precise and therapy-oriented imaging diagnosis is fundamental. In particular, the authors provide valuable notions of anatomy and study technique, essential for the formulation of an exhaustive diagnosis ( 28 ).

Another contribution by Reginelli et al., “Extranodal lymphomas: a pictorial review for CT and MRI classification”, focuses on the study of the imaging classification of a pathology in which staging and treatment are primarily clinical, but supported by careful imaging study.

In the second section, we focused on thoracic and cardiovascular radiology topics ( 29 - 31 ). The article “Anterior chest wall non-traumatic diseases: a road map for the radiologist” is an accurate focus on the pathology of the chest wall, a topic for which the radiologist can often find difficulties about the information to provide to the clinician, and for which knowledge of anatomy and possible pathological pictures is of fundamental importance.

Following the recent tragic pandemic outbreak of Coronavirus pneumonia, Floridi et al. discuss, with a “practical guide”, the fundamental role of diagnostic imaging in the approach and management of patients with COVID-19.

The last article of the thoracic section is the contribution of Pradella et al., “Masses in right side of the heart: spectrum of imaging findings”, dedicated to cardiac radiology, in which the authors provide an overview of the radiological characteristics - either with coronary CT and with cardiac MRI – of cardiac tumors and masses.

The next section is dedicated to the great chapter of interventional radiology, of which we have collected some insights. The work by Ierardi et al., “Basic embolization techniques: tips and tricks” is a handy guide to the interventional radiologist, more or less expert, providing practical indications on the techniques and materials for one of the leading and most crucial interventional radiology endovascular procedures ( 32 ).

Another technique for which interventional radiology has become a fundamental prerogative is biopsy. Pagnini et al. discuss about it in their contribution “Imaging guided percutaneous renal biopsy”, underlining the importance of the knowledge of imaging in such a demanding approach as that of renal biopsy.

Turning to extravascular interventional neuroradiology, Negro et al., in their article “Predictive factors of volumetric reduction in lumbar disc herniation treated by O2-O3 chemiodiscolysis”, present an original study dealing with a popular, effective and minimally invasive technique for the treatment of low back pain ( 33 , 34 ).

The recent innovations applied to diagnostic and interventional radiology have led to significant changes also in the field of musculoskeletal radiology ( 35 - 39 ). In the article “Advanced diagnostic imaging and intervention in tendon diseases”, Bruno et al. describe the application of advanced MRI and US techniques in the study of degenerative tendon pathology, together with the description of the main imaging-guided interventional techniques ( 40 ).

More focused on diagnostics, the contribution of Acanfora et al. on the spectrum of synovial pathology describes the most frequent inflammatory, degenerative, and pseudotumoral pathology ( 41 , 42 ).

Dual-energy technology in CT imaging has been introduced recently ( 43 ); one of the most interesting and useful applications is the study of joint gouty crystals, described by Carotti et al. in the work “Clinical utility of Dual energy Computed Tomography in gout: current concepts and applications”.

The last two articles of the volume are dedicated to neuroradiology ( 44 - 48 ). The contribution of Palumbo et al. is a comprehensive review of the clinical, diagnostic, and therapeutic features of spondylodiscitis. In the article “Diagnosis and management of intralabyrinthine schwannoma: case series and review of the literature”, Di Lullo et al. integrate the clinical and imaging aspects of this pathology, with the consequent implications in therapeutic management.

Despite the difficulty in the exhaustive treatment of such vast and complex topics, we believe that the proposed works can be essential and useful targeted insights for radiologists of different subspecialties.

A heartfelt thanks to all the authors who made the realization of this project possible.

Conflict of interest:

Authors declare that they have no commercial associations (e.g. consultancies, stock ownership, equity interest, patent/licensing arrangement etc.) that might pose a conflict of interest in connection with the submitted article.