Biopsy-free prediction of prostate cancer aggressiveness using deep learning and radiology imaging

Abstract

Magnetic Resonance Imaging (MRI) is routinely used to visualize the prostate gland and manage prostate cancer. The Prostate Imaging Reporting And Data System (PI-RADS) is used to evaluate the clinical risk associated with a potential tumor. However the PI-RADS score is subjective and its assessment varies between physicians. As a result, a definite diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis. A prostate biopsy is an invasive procedure and is associated with complications, including hematospermia, hematuria, and rectal bleeding. We hypothesized that an Artificial Intelligence (AI) can be trained on prostate cases where both imaging and biopsy are available to distinguish aggressive prostate cancer from non-aggressive lesions using MRI imaging only, that is, without the need for a biopsy. Our computational method, named AI-biopsy, can distinguish aggressive prostate cancer from non-aggressive disease with an AUC of 0.855 and a 79.02% accuracy. We used Class Activation Maps (CAM) to highlight which regions of MRI images are being used by our algorithm for classification, and found that AI-biopsy generally focuses on the same regions that trained uro-radiolosts focus on, with a few exceptions. In conclusion, AI-biopsy provides a data-driven and reproducible way to assess cancer aggressiveness from MRI images and a personalized strategy to reduce the number of unnecessary biopsies.

Application of artificial intelligence in the detection and stratification of prostate cancer: Literature review

“…P. Khosravi и соавт. [31] использовали архитектуру Google Inception-V128 (GoogLeNet). Авторы преследовали две цели: дифференциацию опухолей от здоровых тканей путем формирования Глисон группы (GG) 3, 4 и 5, а также определение только клинически значимых очагов (путем объединения GG 1, 2 и 3, 4, 5 в разные группы соответственно).…”