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Academic Radiology|October 15, 2024
Large Language Models can Help with Biostatistics and Coding Needed in Radiology ResearchAdarsh Ghosh, Hailong Li, Andrew T TroutAcademic Radiology|January 31, 2024
Histogram Analysis of Apparent Diffusion Coefficient Maps Provides Genotypic and Pretreatment Phenotypic Information in Pediatric and Young Adult RhabdomyosarcomaAdarsh Ghosh, Hailong Li, Alexander J Towbin, et al.Pediatric Radiology|March 18, 2025
T2-weighted MRI radiomics for the prediction of pediatric and young adult rhabdomyosarcoma alveolar subtype and distant metastasis: a pilot studyAdarsh Ghosh, Hailong Li, Alexander Towbin, et al.Pediatric Radiology|February 3, 2026
Prediction of Fontan failure and correlates of Fontan-associated liver disease severity using machine learning and radiomic features from multi-parametric abdominal MRIAyush Prasad, Alexander R Opotowsky, Andrew T Trout, et al.Journal of the American College of Radiology : JACR|October 20, 2018
Artificial Intelligence Using Open Source BI-RADS Data Exemplifying Potential Future UseAdarsh GhoshPediatric Radiology|January 6, 2025
Practical approach to quantitative liver and pancreas MRI in childrenJustine M Kemp, Adarsh Ghosh, Jonathan R Dillman, et al.Clinical Imaging|January 31, 2024
Patient factors affecting 18F FDG uptake in childrenPradipta Debnath, Andrew T TroutAJR. American Journal of Roentgenology|March 5, 2020
Interpretable Artificial Intelligence: Why and WhenAdarsh Ghosh, Devasenathipathy KandasamyJournal of Computer Assisted Tomography|August 27, 2024
Impact of Emerging Deep Learning-Based MR Image Reconstruction Algorithms on Abdominal MRI Radiomic FeaturesHailong Li, Vinicius Vieira Alves, Amol Pednekar, et al.Cancer Imaging : the Official Publication of the International Cancer Imaging Society|March 25, 2009
Multidetector CT of pancreatic hemangiopericytomaAndrew T Trout, Khaled M ElsayesPageof 119