A Novel Prediction Tool Based on Multiparametric Magnetic Resonance Imaging to Determine the Biopsy Strategy for Clinically Significant Prostate Cancer in Patients with PSA Levels Less than 50 ng/ml

• Published in: Annals of surgical oncology Vol. 27; no. 4; pp. 1284 - 1295
• Main Authors: He, Bi-Ming, Shi, Zhen-Kai, Li, Hu-Sheng, Lin, Heng-Zhi, Yang, Qing-Song, Lu, Jian-Ping, Sun, Ying-Hao, Wang, Hai-Feng
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.04.2020; Springer Nature B.V
• Subjects: Aged; Biopsy; Calibration; Clinical significance; Decision Support Techniques; Humans; Image-Guided Biopsy; Lesions; Logistic Models; Magnetic resonance imaging; Male; Medicine; Medicine & Public Health; Middle Aged; Multiparametric Magnetic Resonance Imaging; Nomograms; Oncology; Prediction models; Prostate cancer; Prostate-specific antigen; Prostate-Specific Antigen - analysis; Prostatic Neoplasms - diagnostic imaging; Retrospective Studies; ROC Curve; Surgery; Surgical Oncology; Urologic Oncology
• ISSN: 1068-9265; 1534-4681
• DOI: 10.1245/s10434-019-08111-2

Purpose To develop and internally validate nomograms to help choose the optimal biopsy strategy among no biopsy, targeted biopsy (TB) only, or TB plus systematic biopsy (SB). Patients and Methods This retrospective study included a total of 385 patients who underwent magnetic resonance imaging (MRI)-guided TB and/or SB at our institute after undergoing multiparametric MRI (mpMRI) between 2015 and 2018. We developed models to predict clinically significant prostate cancer (csPCa) based on suspicious lesions from a TB result and based on the whole prostate gland from the results of TB plus SB or SB only. Nomograms were generated using logistic regression and evaluated using receiver-operating characteristic (ROC) curve analysis, calibration curves and decision analysis. The results were validated using ROC curve and calibration on 177 patients from 2018 to 2019 at the same institute. Results In the multivariate analyses, prostate-specific antigen level, prostate volume, and the Prostate Imaging Reporting and Data System score were predictors of csPCa in both nomograms. Age was also included in the model for suspicious lesions, while obesity was included in the model for the whole gland. The area under the curve (AUC) in the ROC analyses of the prediction models was 0.755 for suspicious lesions and 0.887 for the whole gland. Both models performed well in the calibration and decision analyses. In the validation cohort, the ROC curve described the AUCs of 0.723 and 0.917 for the nomogram of suspicious lesions and nomogram of the whole gland, respectively. Also, the calibration curve detected low error rates for both models. Conclusion Nomograms with excellent discriminative ability were developed and validated. These nomograms can be used to select the optimal biopsy strategy for individual patients in the future.