A nomogram for accurately predicting the pathological upgrading of prostate cancer, based on 68Ga‐PSMA PET/CT. Issue 11 (25th April 2022) - Hu, Qiang; Hong, Xi; Xu, Luwei; Jia, Ruipeng
Abstract: Purpose: To develop and validate a nomogram for preoperative predicting the pathological upgrading of prostate cancer (PCa). Methods: The prediction model was developed in a primary cohort that consisted of 208 PCa patients. All patients included in the study possessed both biopsy pathology specimens and radical prostatectomy pathology specimens, and completed the ( 68 Ga‐prostate‐specific membrane antigen [PSMA]) positron emission tomography/computed tomography (PET/CT) detection. The R function "createDataPartition" was used in a 7:3 ratio to randomly divide the patients into training and validation cohorts. In the training cohort, the independent predictors of pathological upgrading of PCa were determined by univariate analysis, univariate regression analysis and multivariate regression analysis. Based on these independent predictors, a nomogram was developed, and its performance was evaluated by receiver operating characteristic (ROC) curve, area under the curve (AUC) and calibration curve of training cohort and validation cohort. Results: The nomogram incorporated five independent predictors including prostate volume (PV), SUVmax of the 68 Ga‐PSMA PET/CT examination on prostate lesions (SUVmax ), body mass index (BMI); percentage of cancer positive biopsy cores (PPC) and biopsy International Society of Urological Pathology (ISUP) grade. The nomogram showed good diagnostic accuracy for the pathological upgrading of both the training cohort and the validation cohort (AUC = 0.818 and 0.806, respectively). The calibration curves for the two cohorts both showed optimal agreement between nomogram prediction and actual observation. Conclusions: We developed and validated a nomogram to accurately predict the risk of pathological upgrading after radical PCa surgery, which can provide accurate basis for therapeutic schedule and prognostic data of PCa patients.