Rakesh Shiradkar

Publications

Hiremath, A., Shiradkar, R., Merisaari, H., Prasanna, P., Ettala, O., Taimen, P., Aronen, H., Boström, P., Jambor, I., & Madabhushi, A. (2021). Test-retest repeatability of a deep learning architecture in detecting and segmenting clinically significant prostate cancer on apparent diffusion coefficient (ADC) maps. European Radiology, 31 (1), 379-391.
Shiradkar, R., Panda, A., Leo, P., Janowczyk, A., Farre, X., Janaki, N., Li, L., Pahwa, S., Mahran, A., Buzzy, C., Fu, P., Elliott, R., MacLennan, G., Ponsky, L., Gulani, V., & Madabhushi, A. (2020). Correction to: T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning–derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology. European Radiology.
Shiradkar, R., Panda, A., Leo, P., Janowczyk, A., Farre, X., Janaki, N., Li, L., Pahwa, S., Mahran, A., Buzzy, C., Fu, P., Elliott, R., MacLennan, G., Ponsky, L., Gulani, V., & Madabhushi, A. (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 corresponding whole mount histopathology. European Radiology.
Algohary, A., Shiradkar, R., Pahwa, S., Purysko, A., Verma, S., Moses, D., Shnier, R., Haynes, A., Delprado, W., Thompson, J., Tirumani, S., Mahran, A., Rastinehad, A., Ponsky, L., Stricker, P., & Madabhushi, A. (2020). Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study. Cancers, 12 (8).
Merisaari, H., Taimen, P., Shiradkar, R., Ettala, O., Pesola, M., Saunavaara, J., Boström, P., Madabhushi, A., Aronen, H., & Jambor, I. (2020). Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer. Magnetic Resonance in Medicine, 83 (6), 2293-2309.
Shiradkar, R., Mahran, A., Sharma, S., Conroy, B., Tirumani, S., Ponsky, L., & Madabhushi, A. (2020). MP81-06 RADIOMIC FEATURES OF PROSTATE CANCER PATIENTS (GLEASON GRADE GROUP = 2) SHOW DIFFERENCES BETWEEN AFRICAN AMERICAN AND CAUCASIAN POPULATIONS ON BI-PARAMETRIC MRI: PRELIMINARY FINDINGS. The Journal of Urology, 203
Leo, P., Elliott, R., Janowczyk, A., Janaki, N., Bera, K., Shiradkar, R., El-Fahmawi, A., Kim, J., Shahait, M., Shah, A., Thulasidass, H., Tewari, A., Gupta, S., Shih, N., Feldman, M., Lal, P., Lee, D., & Madabhushi, A. (2020). PD52-02 COMPUTER-EXTRACTED FEATURES OF GLAND MORPHOLOGY FROM DIGITAL TISSUE IMAGES IS COMPARABLE TO DECIPHER FOR PROGNOSIS OF BIOCHEMICAL RECURRENCE RISK POST-SURGERY. The Journal of Urology, 203 , e1089-e1090.
Hiremath, A., Shiradkar, R., Merisaari, H., Li, L., Prasanna, P., Ettala, O., Taimen, P., Aronen, H., Boström, P., Pierce, J., Tirumani, S., Rastinehad, A., Jambor, I., Purysko, A., & Madabhushi, A. (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. The Journal of Urology, 203
Leo, P., Janowczyk, A., Elliott, R., Janaki, N., Shiradkar, R., Farr�, X., Yamoah, K., Rebbeck, T., Shih, N., Khani, F., Robinson, B., Eklund, L., Ettala, O., Taimen, P., Boström, P., Feldman, M., Gupta, S., Lal, P., & Madabhushi, A. (2019). Computerized histomorphometric features of glandular architecture predict risk of biochemical recurrence following radical prostatectomy: A multisite study.. Journal of Clinical Oncology, 37 (15_suppl), 5060-5060.
Li, L., Shiradkar, R., Leo, P., Purysko, A., Algohary, A., Klein, E., Magi-Galluzzi, C., & Madabhushi, A. (2019). Association of radiomic features from prostate bi-parametric MRI with Decipher risk categories to predict risk for biochemical recurrence post-prostatectomy.. Journal of Clinical Oncology, 37 (15_suppl), e16561-e16561.
Shiradkar, R., Ghose, S., Jambor, I., Taimen, P., Ettala, O., Purysko, A., & Madabhushi, A. (2018). Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings: Prostate Cancer Recurrence Prediction. Journal of Magnetic Resonance Imaging.
Algohary, A., Viswanath, S. E., Shiradkar, R. E., Ghose, S. E., Pahwa, S. E., Moses, D. E., Jambor, I. E., Shnier, R. E., B�hm, M. E., Haynes, A. E., Brenner, P. E., Delprado, W. E., Thompson, J. E., Pulbrock, M. E., Purysko, A. E., Verma, S. E., Ponsky, L. E., Stricker, P. E., & Madabhushi, A. E. (2018). Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings: Radiomics Categorizes PCa Patients on AS. Journal of Magnetic Resonance Imaging.
Shiradkar, R., Ghose, S., Villani, R., Ben-Levi, E., Rastinehad, A., & Madabhushi, A. (2017). PD65-08 DISTINGUISHING LOW VERSUS HIGH RISK PROSTATE CANCER LESIONS USING RADIOMIC FEATURES DERIVED FROM MULTI-PARAMETRIC MAGNETIC RESONANCE IMAGING (MRI). The Journal of Urology, 197 (4).