Diagnostic performance of a nomogram incorporating cribriform morphology for the prediction of adverse pathology in prostate cancer at radical prostatectomy

Abstract

The aim of the present study was to develop a novel nomogram that incorporated clinical factors, imaging parameters and biopsy pathological factors (including cribriform morphology) to predict adverse pathology in prostate cancer (PCa). A total of 223 patients with PCa, who had undergone preoperative multi-parametric magnetic resonance imaging and had a biopsy of Gleason pattern (GP) 4, absence of GP 5 and pure Grade Group (GG) 3 [Gleason score (GS) 3+4, GS 4+3, GS 4+4], were retrospectively enrolled onto the study. The contribution of GG to the biopsy and Prostate Imaging Reporting and Data System (PI-RADS) score for PCa harboring adverse pathology were analyzed. Univariate and multivariate logistic regression analyses were performed to determine significant pathology predictors of adverse pathology for nomogram development. The nomogram was internally validated using bootstrapping with 1,000 iterations. The diagnostic performance of the nomogram was analyzed by receiver operating characteristics (ROC) analysis and decision curve analysis (DCA). A higher biopsy GG and PI-RADS score were associated with an increased likelihood of adverse pathology. Prostate specific antigen density (PSAD), biopsy GG, cribriform morphology on biopsy and PI-RADS score were significant predictors and were included in the nomogram. The ROC area under the curve of the nomogram was 0.88 (95% confidence interval, 0.84-0.91), with a high specificity (0.91) and moderate sensitivity (0.72). The novel nomogram was shown to have a higher net benefit for the prediction of adverse pathology in PCa, compared with any individual factors determined by DCA. Overall, a novel nomogram incorporating PSAD, PI-RADS score, biopsy GG and cribriform morphology on biopsy was shown to perform well in the prediction of PCa harboring adverse pathology at the time of radical prostatectomy.

Development and Validation of a Contrast-Enhanced CT-Based Radiomics Nomogram for Prediction of Therapeutic Efficacy of Anti-PD-1 Antibodies in Advanced HCC Patients

“…Moreover, radiomics has recently been recognized as a newly emerging form of imaging technology in oncology using a series of statistical analysis tools or data-mining algorithms on high-throughput imaging features to obtain predictive or prognostic information (25). Its application has achieved successful prediction abilities in various tumors by building appropriate models with refined features and clinical data (26)(27)(28)(29)(30). For instance, radiomic features extracted from contrast-enhanced CT (CECT) have been proved to be useful in predicting microvascular invasion (MVI) and the long-term clinical outcomes in patients with HCC (31).…”