作者
Andreas G. Wibmer,Michael W. Kattan,Francesco Alessandrino,Alexander Baur,Lars Boesen,Felipe Franco,David Bonekamp,Riccardo Campa,Hannes Cash,Violeta Catalá,Sébastien Crouzet,Sounil Dinnoo,James A. Eastham,Fiona M. Fennessy,Kamyar Ghabili,Markus Hohenfellner,Angelique W. Levi,Xinge Ji,Vibeke Løgager,Daniel Margolis,Paul C. Moldovan,Valeria Panebianco,Tobias Penzkofer,Philippe Puech,Jan Philipp Radtke,Olivier Rouvière,Heinz‐Peter Schlemmer,Preston Sprenkle,Clare M. Tempany,Joan C. Vilanova,Jeffrey C. Weinreb,Hedvig Hricak,Amita Shukla–Dave
摘要
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.