Abstract
Simple Summary
For patients with newly diagnosed prostate cancer, it is important to detect tumor growth beyond the prostate, as this can affect a patient's prognosis, influence management decisions, and alter treatment strategies. It is recognized that on prostate MRI, some instances of extraprostatic tumor growth can be missed. In this study, we merged patient data from multiple hospitals in different countries and developed a type of mathematical formula called "nomogram" that combines MRI findings with other available patient data. The results of our study allow physicians to more accurately diagnose extraprostatic tumor growth by combining clinical, biopsy, and MRI-derived information according to their relative statistical importance.
Background: To develop an international, multi-site nomogram for side-specific prediction of extraprostatic extension (EPE) of prostate cancer based on clinical, biopsy, and magnetic resonance imaging- (MRI) derived data. Methods: Ten institutions from the USA and Europe contributed clinical and side-specific biopsy and MRI variables of consecutive patients who underwent prostatectomy. A logistic regression model was used to develop a nomogram for predicting side-specific EPE on prostatectomy specimens. The performance of the statistical model was evaluated by bootstrap resampling and cross validation and compared with the performance of benchmark models that do not incorporate MRI findings. Results: Data from 840 patients were analyzed; pathologic EPE was found in 320/840 (31.8%). The nomogram model included patient age, prostate-specific antigen density, side-specific biopsy data (i.e., Gleason grade group, percent positive cores, tumor extent), and side-specific MRI features (i.e., presence of a PI-RADSv2 4 or 5 lesion, level of suspicion for EPE, length of capsular contact). The area under the receiver operating characteristic curve of the new, MRI-inclusive model (0.828, 95% confidence limits: 0.805, 0.852) was significantly higher than that of any of the benchmark models (p < 0.001 for all). Conclusions: In an international, multi-site study, we developed an MRI-inclusive nomogram for the side-specific prediction of EPE of prostate cancer that demonstrated significantly greater accuracy than clinical benchmark models.
