A5070486532- Pancreatic and Hepatic Oncology Research
- Radiomics and Machine Learning in Medical Imaging
- Radiology practices and education
- Artificial Intelligence in Healthcare and Education
- Advanced X-ray and CT Imaging
- Radiation Dose and Imaging
- Neuroendocrine Tumor Research Advances
- Renal cell carcinoma treatment
- COVID-19 diagnosis using AI
- Cardiomyopathy and Myosin Studies
- AI in cancer detection
- Cardiovascular Function and Risk Factors
- Aortic aneurysm repair treatments
- MRI in cancer diagnosis
- Aortic Disease and Treatment Approaches
- Diversity and Career in Medicine
- Pancreatitis Pathology and Treatment
- Anatomy and Medical Technology
- Colorectal Cancer Screening and Detection
- Lung Cancer Diagnosis and Treatment
- Liver Disease Diagnosis and Treatment
- Hepatocellular Carcinoma Treatment and Prognosis
- Adrenal and Paraganglionic Tumors
- Cancer Genomics and Diagnostics
- Cardiac Imaging and Diagnostics
Johns Hopkins University
2016-2025
Johns Hopkins Medicine
2016-2025
Johns Hopkins Hospital
2012-2024
SUNY Upstate Medical University
2024
Monash University
2023
San Diego State University
2019-2020
Siemens (Germany)
2016-2018
Medical Education Institute
2013-2018
Bracco (Italy)
2016
National Institutes of Health
2016
Utility of CT Radiomics Features in Differentiation Pancreatic Ductal Adenocarcinoma From Normal TissueLinda C. Chu1, Seyoun Park1, Satomi Kawamoto1, Daniel F. Fouladi1, Shahab Shayesteh1, Eva S. Zinreich1, Jefferson Graves1, Karen M. Horton1, Ralph H. Hruban2, Alan L. Yuille3, Kenneth W. Kinzler4, Bert Vogelstein4 and Elliot K. Fishman1Audio Available | Share
Abstract Purpose To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research of radiomics studies. Methods We conducted an online modified Delphi study with group international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members identify the items be voted; Stage#3, four rounds exercise by panelists determine eligible for METRICS their weights. The...
The WHO grading of pancreatic neuroendocrine neoplasms (PanNENs) is essential in patient management and an independent prognostic factor for survival. Radiomics features from CE-CT images hold promise the outcome tumor grade prediction. However, variations reconstruction parameters can impact predictive value radiomics. 127 patients with histopathologically confirmed PanNENs underwent CT scans filtered back projection (B20f) iterative (I26f) kernels. 3190 radiomic were extracted tumors...
Purpose To test whether computer-aided diagnosis (CAD) approaches can increase the positive predictive value (PPV) and reduce false-positive rate in lung cancer screening for small nodules compared with human reading by thoracic radiologists. Materials Methods A matched case-control sample of low-dose computed tomography (CT) studies 186 participants 4–20-mm noncalcified who underwent biopsy National Lung Screening Trial (NLST) was selected. Variables used matching were age, sex, smoking...
Pancreatic neuroendocrine tumors (PanNETs) are typically solid neoplasms but in rare instances may present as cystic lesions. This unusual presentation can make clinical diagnosis challenging. In addition, the and histopathologic characteristics of PanNETs poorly defined. We identified 53 our single-institution experience 491 surgically resected PanNETs. Similar to PanNETs, developed with an equal sex distribution over a wide age range (23 91 y; mean, 52 y). The appearance made radiologic...
Purpose To evaluate myocardial strain and circumferential transmural difference (cTSD; the between epicardial endocardial strain) in a genotyped cohort with hypertrophic cardiomyopathy (HCM) to explore correlations cTSD other anatomic functional markers of disease status. Left ventricular (LV) dysfunction may indicate early preclinical HCM (sarcomere mutation carriers without LV hypertrophy). Cardiac MRI feature tracking be used sarcomere mutation. Materials Methods Participants their family...
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal AdenocarcinomaSeyoun Park, PhD1, Jonathan G. Sham, MD2, Satomi Kawamoto, MD1, Alex B. Blair, MD3, Noah Rozich, Daniel F. Fouladi, Shahab Shayesteh, Ralph H. Hruban, MD4, Jin He, Christopher L. Wolfgang, MD3,4,5, Alan Yuille, PhD6,7, Elliot K. Fishman, MD1 and Linda C. Chu, MD1Audio Available | Share