Predictive model for surgical margin status in radical prostatectomy using mp-MRI and clinical characteristics

Predictors were selected and a predictive model for surgical margin status in patients after radical prostatectomy for prostate cancer was constructed based on multiparametric magnetic resonance imaging (mp-MRI) combined with other clinical features with potential predictive capabilities. This provides a reliable predictive tool for surgical patients, thereby assisting clinical decision-making.

A retrospective analysis was conducted on clinical data from 927 patients who underwent radical prostatectomy at the First Affiliated Hospital of Nanjing Medical University between January 2018 and June 2024. The data included baseline characteristics, preoperative serum biochemical indicators, mp-MRI imaging data, preoperative biopsy pathology results, and postoperative pathology results. The patients were randomly divided into a training set and a validation set in a 7:3 ratio. Univariate logistic regression analysis, multivariate logistic regression analysis, and backward stepwise regression analysis were performed on the training set data to identify independent predictors of prostate cancer margin status and to establish a predictive model. The predictive model was tested in the validation set for its discriminative ability, accuracy, and clinical utility.

Logistic regression analysis identified five independent predictors (P < 0.05): prostate-specific antigen density (PSAD), estradiol, PI-RADS score, number of lesions, suspected capsular invasion, and suspected lymph node metastasis. These predictors were used to construct a predictive model, and a nomogram was developed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were plotted for both the training and validation sets. The areas under the curve (AUC) were 0.897 (95%CI: 0.874-0.921) for the training set and 0.841 (95%CI: 0.793-0.888) for the validation set. The calibration curves for both sets closely aligned with the diagonal, with mean absolute errors (MAE) of 0.011 and 0.009, respectively, indicating no significant overfitting and demonstrating good generalization ability of the model. Decision curve analysis revealed that the model provides clinical net benefit.

mp-MRI has significant predictive value for assessing surgical margin status in prostate cancer. The predictive model constructed in this study demonstrates strong predictive performance and can assist in clinical decision-making.