|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
|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), 1-14, 2016
|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
|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
|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
|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), 1-8, 2017
|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
|Auto-calibrating photometric stereo using ring light constraints|
R Shiradkar, P Tan, SH Ong
Machine vision and applications 25 (3), 801-809, 2014
|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
|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 App. 16/395,904, 2020
|Radiomic features derived from pre-operative multi-parametric MRI of prostate cancer are associated with Decipher risk score|
L Li, R Shiradkar, A Algohary, P Leo, C Magi-Galluzzi, E Klein, A Purysko, ...
Medical Imaging 2019: Computer-Aided Diagnosis 10950, 109503Y, 2019
|Computerized histomorphometric features of glandular architecture predict risk of biochemical recurrence following radical prostatectomy: A multisite study.|
P Leo, A Janowczyk, R Elliott, N Janaki, R Shiradkar, X Farrč, K Yamoah, ...
Journal of Clinical Oncology 37 (15_suppl), 5060-5060, 2019
|Association of radiomic features from prostate bi-parametric MRI with Decipher risk categories to predict risk for biochemical recurrence post-prostatectomy.|
L Li, R Shiradkar, P Leo, A Purysko, A Algohary, EA Klein, ...
Journal of Clinical Oncology 37 (15_suppl), e16561-e16561, 2019
|Pd65-08 Distinguishing Low Versus High Risk Prostate Cancer Lesions Using Radiomic Features Derived From Multi-Parametric Magnetic Resonance Imaging (Mri)|
R Shiradkar, S Ghose, R Villani, E Ben-Levi, A Rastinehad, ...
The Journal of Urology 197 (4S), e1269-e1269, 2017
|Surface reconstruction using isocontours of constant depth and gradient|
R Shiradkar, SH Ong
2013 IEEE International Conference on Image Processing, 360-363, 2013
|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 (3), 1336-1346, 2021
|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, 103163, 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 (1), 379-391, 2021
|MP81-06 RADIOMIC FEATURES OF PROSTATE CANCER PATIENTS (GLEASON GRADE GROUP= 2) SHOW DIFFERENCES BETWEEN AFRICAN AMERICAN AND CAUCASIAN POPULATIONS ON BI-PARAMETRIC MRI …|
R Shiradkar*, A Mahran, S Sharma, B Conroy, SH Tirumani, L Ponsky, ...
The Journal of Urology 203 (Supplement 4), e1238-e1238, 2020
|PD57-05 A DEEP LEARNING NETWORK ALONG WITH PIRADS CAN DISTINGUISH CLINICALLY SIGNIFICANT AND INSIGNIFICANT PROSTATE CANCER ON BI-PARAMETRIC MRI: A MULTI-CENTER STUDY|
A Hiremath*, R Shiradkar, H Merisaari, L Li, P Prasanna, O Ettala, ...
The Journal of Urology 203 (Supplement 4), e1195-e1195, 2020