| Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: preliminary findings R Shiradkar, S Ghose, I Jambor, P Taimen, O Ettala, AS Purysko, ... Journal of Magnetic Resonance Imaging 48 (6), 1626-1636, 2018 | 147 | 2018 |
| Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings A Algohary, S Viswanath, R Shiradkar, S Ghose, S Pahwa, D Moses, ... Journal of Magnetic Resonance Imaging 48 (3), 818-828, 2018 | 127 | 2018 |
| Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI R Shiradkar, TK Podder, A Algohary, S Viswanath, RJ Ellis, ... Radiation oncology 11, 1-14, 2016 | 96 | 2016 |
| An integrated nomogram combining deep learning, Prostate Imaging–Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically … A Hiremath, R Shiradkar, P Fu, A Mahran, AR Rastinehad, A Tewari, ... The Lancet Digital Health 3 (7), e445-e454, 2021 | 77 | 2021 |
| Combination of peri-tumoral and intra-tumoral radiomic features on bi-parametric MRI accurately stratifies prostate cancer risk: a multi-site study A Algohary, R Shiradkar, S Pahwa, A Purysko, S Verma, D Moses, ... Cancers 12 (8), 2200, 2020 | 70 | 2020 |
| A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI L Li, R Shiradkar, P Leo, A Algohary, P Fu, SH Tirumani, A Mahran, ... EBioMedicine 63, 2021 | 47 | 2021 |
| Repeatability of radiomics and machine learning for DWI: Short‐term repeatability study of 112 patients with prostate cancer H Merisaari, P Taimen, R Shiradkar, O Ettala, M Pesola, J Saunavaara, ... Magnetic resonance in medicine 83 (6), 2293-2309, 2020 | 41 | 2020 |
| T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning–derived estimates of epithelium, lumen, and stromal composition on … R Shiradkar, A Panda, P Leo, A Janowczyk, X Farre, N Janaki, L Li, ... European radiology 31, 1336-1346, 2021 | 35 | 2021 |
| Integrating pathomics with radiomics and genomics for cancer prognosis: A brief review C Lu, R Shiradkar, Z Liu Chinese Journal of Cancer Research 33 (5), 563, 2021 | 33 | 2021 |
| “Shortcuts” causing bias in radiology artificial intelligence: causes, evaluation and mitigation. I Banerjee, K Bhattacharjee, JL Burns, H Trivedi, S Purkayastha, ... Journal of the American College of Radiology, 2023 | 29 | 2023 |
| Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study P Leo, A Janowczyk, R Elliott, N Janaki, K Bera, R Shiradkar, X Farré, ... NPJ precision oncology 5 (1), 35, 2021 | 21 | 2021 |
| Test-retest repeatability of a deep learning architecture in detecting and segmenting clinically significant prostate cancer on apparent diffusion coefficient (ADC) maps A Hiremath, R Shiradkar, H Merisaari, P Prasanna, O Ettala, P Taimen, ... European radiology 31, 379-391, 2021 | 21 | 2021 |
| A new perspective on material classification and ink identification R Shiradkar, L Shen, G Landon, S Heng Ong, P Tan Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2014 | 16 | 2014 |
| Prostate shapes on pre-treatment MRI between prostate cancer patients who do and do not undergo biochemical recurrence are different: preliminary findings S Ghose, R Shiradkar, M Rusu, J Mitra, R Thawani, M Feldman, AC Gupta, ... Scientific Reports 7 (1), 15829, 2017 | 15 | 2017 |
| Prostate surface distension and tumor texture descriptors from pre-treatment MRI are associated with biochemical recurrence following radical prostatectomy: preliminary findings R Shiradkar, S Ghose, A Mahran, L Li, I Hubbard, P Fu, SH Tirumani, ... Frontiers in Oncology 12, 841801, 2022 | 13 | 2022 |
| Ten quick tips for computational analysis of medical images D Chicco, R Shiradkar PLoS computational biology 19 (1), e1010778, 2023 | 12 | 2023 |
| Predicting prostate cancer recurrence in pre-treatment prostate magnetic resonance imaging (MRI) with combined tumor induced organ distension and tumor radiomics A Madabhushi, R Shiradkar, S Ghose US Patent 10,540,570, 2020 | 11 | 2020 |
| Evaluating the sensitivity of deep learning to inter-reader variations in lesion delineations on bi-parametric MRI in identifying clinically significant prostate cancer A Roge, A Hiremath, M Sobota, SH Tirumani, LK Bittencourt, J Ream, ... Medical imaging 2022: computer-aided diagnosis 12033, 264-273, 2022 | 8 | 2022 |
| Predicting prostate cancer risk of progression with multiparametric magnetic resonance imaging using machine learning and peritumoral radiomics A Madabhushi, A Algohary, R Shiradkar US Patent 11,011,265, 2021 | 6 | 2021 |
| Auto-calibrating photometric stereo using ring light constraints R Shiradkar, P Tan, SH Ong Machine vision and applications 25, 801-809, 2014 | 6 | 2014 |
