Rakesh Shiradkar

Research Assistant Professor, Biomedical Engineering

Office:

Email: rakesh.shiradkar@case.edu

Publications

Hiremath, A., Shiradkar, R., Fu, P., Mahran, A., Rastinehad, A., Tewari, A., ... Madabhushi, A. (2021). An integrated nomogram combining deep learning, Prostate Imaging–Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study. The Lancet Digital Health, 3 (7), E445 - E454.
DOI: 10.1016/S2589-7500(21)00082-0

Leo, P., Janowczyk, A., Elliott, R., Janaki, N., Bera, K., Shiradkar, R., ... Madabhushi, A. (2021). Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study. NPJ Precision Oncology, 5 (1), 35.
DOI: 10.1038/s41698-021-00174-3

Hiremath, A., Shiradkar, R., Merisaari, H., Prasanna, P., Ettala, O., Taimen, P., ... 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.

Algohary, A., Shiradkar, R., Pahwa, S., Purysko, A., Verma, S., Moses, D., ... 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), 1-14.

Shiradkar, R., Panda, A., Leo, P., Janowczyk, A., Farre, X., Janaki, N., ... 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, ().

Merisaari, H., Taimen, P., Shiradkar, R., Ettala, O., Persola, M., Saunavaara, J., ... Jambor, I. (2019). Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer. Magnetic Resonance in Imaging, 83 (6), 2293 - 2309.

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. Journal of Magnetic Resonance Imaging, 48 (6), 1626-1636.

Algohary, A., Viswanath, S. E., Shiradkar, R., Ghose, S., Pahwa, S., Moses, D., ... Madabhushi, A. (2018). Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings. Journal of Magnetic Resonance Imaging, 48 (3), 818-828.

Ghose, S., Shiradkar, R., Rusu, M., Mitra, J., Thawani, R., Feldman, M., ... Madabhushi, A. (2017). Prostate shapes on pre-treatment MRI between prostate cancer patients who do and do not undergo biochemical recurrence are different: Preliminary Findings. Scientific Reports, 7 (1).

Shiradkar, R., Podder, T., Algohary, A., Viswanath, S. E., Ellis, R., & Madabhushi, A. (2016). Radiomics based targeted radiotherapy planning (Rad-TRaP): A computational framework for prostate cancer treatment planning with MRI. Radiation Oncology, 11 (1).