Improving the stratification of intermediate risk prostate cancer
Recommended Citation
Nocera L, Collà Ruvolo C, Stolzenbach LF, Deuker M, Tian Z, Gandaglia G, Fossati N, Abdollah F, Suardi N, Mirone V, Graefen M, Chun FK, Saad F, Montorsi F, Briganti A, and Karakiewicz PI. Improving the stratification of intermediate risk prostate cancer. Minerva Urol Nephrol 2021.
Document Type
Article
Publication Date
4-22-2021
Publication Title
Minerva Urol Nephrol
Abstract
BACKGROUND: Intermediate risk prostate cancer (IR PCa) may exhibit a wide array of phenotypes, from favorable to unfavorable. NCCN criteria help distinguishing between favorable versus unfavorable subgroups. We studied and attempted to improve this classification.
METHODS: Within the SEER database 2010-2016, we identified 19,193 IR PCa patients treated with radical prostatectomy. A multivariable logistic regression model predicting unfavorable IR PCa was developed and externally validated, in addition to a head-to-head comparison with NCCN IR PCa stratification.
RESULTS: Model development (development cohort n=13,436: 3,585 unfavorable versus 9,851 favorable) rested on age, PSA, clinical T stage, biopsy Gleason Grade Group (GGG) and percentage of positive cores. All were independent predictors of unfavorable IR PCa. In external validation cohort (n=5,757: 1,652 unfavorable versus 4,105 favorable), NCCN stratification was 61.8% accurate in discriminating between favorable versus unfavorable, compared to 67.6% for nomogram, which exhibited excellent calibration, less pronounced departures from ideal prediction and greater net-benefit in decision curve analyses (DCA) than NCCN stratification. The optimal nomogram cutoff misclassified 312 of 1976 patients (15.8%) versus 598 of 2877 (20.8%) for NCCN stratification. Of NCCN misclassified patients, 90.0% harbored pT3-4 stages versus 84.6% of nomogram.
CONCLUSIONS: The newly developed, externally validated nomogram discriminates better between favorable versus unfavorable IR PCa, according to overall accuracy, calibration, DCA, and actual numbers and stage distribution of misclassified patients.
PubMed ID
33887893
ePublication
ePub ahead of print
