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Rakesh Shiradkar

Research Assistant Professor, Biomedical Engineering
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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
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.