Radio-pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Prostate CancerSean D. McGarry, Peter S. LaViolette, Anjishnu Banerjee et al.|International Journal of Radiation Oncology*Biology*Physics|2018Cited by 70
Gleason Probability Maps: A Radiomics Tool for Mapping Prostate Cancer Likelihood in MRI SpaceSean D. McGarry, Peter S. LaViolette, John D. Bukowy et al.|Tomography|2019Cited by 60
Radio-pathomic mapping model generated using annotations from five pathologists reliably distinguishes high-grade prostate cancerSean D. McGarry, Peter S. LaViolette, John D. Bukowy et al.|Journal of medical imaging|2020Cited by 21
T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical RecurrenceSavannah Duenweg, Peter S. LaViolette, Samuel Bobholz et al.|Cancers|2023Cited by 17
Accurate segmentation of prostate cancer histomorphometric features using a weakly supervised convolutional neural networkJohn D. Bukowy, Peter S. LaViolette, Halle Foss et al.|Journal of medical imaging|2020Cited by 12