Association between artificial intelligence–derived tumor volume and oncologic outcomes in localized prostate cancer.
Authors: Yang David Dewei , Lee Katie Nadine , Lee Leslie K , Tsui James M , Leeman Jonathan E , McClure Heather , Sudhyadhom Atchar , Guthier Christian V. , Taplin Mary-Ellen , Trinh Quoc-Dien , Mouw Kent William , Martin Neil E , Orio Peter F , Nguyen Paul L. , D'Amico Anthony Victor , Shin Kee-Young , King Martin T. 
Published: January 2024

280 Background: Characteristics of the intraprostatic tumor (e.g., PI-RADS scores) from multi-parametric magnetic resonance imaging (mpMRI) are prognostic but exhibit significant inter-observer variability. An artificial intelligence (AI)-based method for measuring intraprostatic tumor volume from mpMRI may provide prognostic information in a systematic manner. We sought to evaluate whether intraprostatic tumor volume (VAI) from AI-segmented lesions provides prognostic information independent of clinical risk groups for contemporary patients with localized prostate cancer (PCa) treated with radiation therapy (RT) or radical prostatectomy (RP). Methods: We retrospectively identified 732 patients with cT1-3N0M0 PCa who underwent high B-value, 3-Tesla mpMRI and were treated with RT (n=438) in 2010-2017 or RP (n=294) in 2015-2017 at a single academic institution. The RT cohort was randomly divided into cross-validation RT (CVRT, n=288) and TestRT (n=150) cohorts. A deep learning model (nnU-Net) was trained to delineate PI-RADS 3-5 lesions from the CVRT cohort using 5-fold cross validation before providing delineations for TestRT and RP cohorts. The F1 score (geometric mean of precision and recall) for identifying patients with PI-RADS 3-5 lesions was calculated for each cohort. Multivariable Cox regression models were used to evaluate the association between VAI and time to biochemical failure and metastasis, while adjusting for 4-tiered National Cancer Center Network risk groups. The RT (CVRT and TestRT) and RP cohorts were analyzed separately. Results: F1-scores for the CVRT, TestRT, and RP datasets were 87.4%, 83.8%, and 85.3%, respectively. With a median follow-up of 6.9 years for the RT cohort, VAI was significantly associated with biochemical failure (adjusted hazard ratio [AHR] 1.58, 95% confidence interval [CI] 1.29-1.94, p<0.001) and metastasis (AHR 1.70, 95% CI 1.24-2.34, p=0.001). With a median follow-up of 5.5 years for the RP cohort, VAI was similarly significantly associated with biochemical failure (AHR 1.33, 95% CI 1.12-1.59, p=0.001) and metastasis (AHR 1.93, 95% CI 1.19-3.13, p=0.008). The areas under the receiver operating characteristic curves for 7-year metastasis for the RT cohort were 0.867 for VAI versus 0.821 for NCCN (p=0.03). Corresponding values at 5 years for the RP cohort were 0.888 vs 0.789 (p=0.25). Conclusions: An AI model can delineate the intraprostatic tumor with excellent performance. The intraprostatic tumor volume of AI-segmented lesions is an independent, highly prognostic factor that can be derived from standard mpMRI, even prior to biopsy. If further validated, this noninvasive biomarker may enable further treatment personalization for localized PCa.