부산대학교 도서관

Simple Summary: Biochemical recurrence after radical prostatectomy is vitally important for long-term oncological control and subsequent treatment of these patients. We applied radiomic technique to extract features from MR images of prostate cancer patients, and used deep learning algorithm to establish a predictive model for biochemical recurrence with high accuracy. The model was validated in 2 indepented cohorts with superior predictive value than traditional stratification systems. With the aid of this model, we are able to distinghuish patients with higher risk of developing biochemical recurrence at early stage, thus providing a window to initiate neoadjuvant or adjuvant therapies for prostate cancer patients. Biochemical recurrence (BCR) occurs in up to 27% of patients after radical prostatectomy (RP) and often compromises oncologic survival. To determine whether imaging signatures on clinical prostate magnetic resonance imaging (MRI) could noninvasively characterize biochemical recurrence and optimize treatment. We retrospectively enrolled 485 patients underwent RP from 2010 to 2017 in three institutions. Quantitative and interpretable features were extracted from T2 delineated tumors. Deep learning-based survival analysis was then applied to develop the deep-radiomic signature (DRS-BCR). The model's performance was further evaluated, in comparison with conventional clinical models. The model achieved C-index of 0.802 in both primary and validating cohorts, outweighed the CAPRA-S score (0.677), NCCN model (0.586) and Gleason grade group systems (0.583). With application analysis, DRS-BCR model can significantly reduce false-positive predictions, so that nearly one-third of patients could benefit from the model by avoiding overtreatments. The deep learning-based survival analysis assisted quantitative image features from MRI performed well in prediction for BCR and has significant potential in optimizing systemic neoadjuvant or adjuvant therapies for prostate cancer patients. [ABSTRACT FROM AUTHOR]