使用基于临床病理学和 MRI 的风险因素制定术前列线图，以预测根治性前列腺切除术 (RP) 后手术切缘阳性 (PSM) 的风险。本研究回顾性纳入 2015 年 1 月至 2022 年 11 月期间在我们中心接受 RP 前前列腺 MRI 的患者记录术前临床病理因素和基于 MRI 的特征进行分析。评估了病理学中 PSM（总体 PSM [oPSM]）的存在以及 PSM 的多灶性（mPSM）。采用LASSO回归进行变量选择。最终的模型构建采用逻辑回归结合Bootstrap方法进行内部验证。使用列线图可视化个体患者的风险概率。 本研究总共纳入 259 名患者，其中 76 名 (29.3%) 患者患有 PSM，其中 40 名患者患有 mPSM。最终的多变量逻辑回归显示，oPSM 的独立危险因素是肿瘤直径、前列腺外扩展和年度手术量（所有 p<0.05），并且 oPSM 的列线图在开发和实施过程中达到了 0.717 的曲线下面积 (AUC)。内部验证为0.716。 mPSM的独立危险因素包括阳性核心百分比、肿瘤直径、根尖深度和年手术量（均p＜0.05），mPSM列线图在开发和内部验证中的AUC均为0.790。校准曲线分析表明，这些列线图对于 oPSM 和 mPSM 都进行了良好的校准。所提出的列线图在预测 oPSM 和 mPSM 方面表现出良好的性能，并且是可行的，这可能有助于对候选手术的前列腺癌患者进行更个性化的管理。© 2024。作者。

To develop preoperative nomograms using risk factors based on clinicopathological and MRI for predicting the risk of positive surgical margin (PSM) after radical prostatectomy (RP).This study retrospectively enrolled patients who underwent prostate MRI before RP at our center between January 2015 and November 2022. Preoperative clinicopathological factors and MRI-based features were recorded for analysis. The presence of PSM (overall PSM [oPSM]) at pathology and the multifocality of PSM (mPSM) were evaluated. LASSO regression was employed for variable selection. For the final model construction, logistic regression was applied combined with the bootstrap method for internal verification. The risk probability of individual patients was visualized using a nomogram.In all, 259 patients were included in this study, and 76 (29.3%) patients had PSM, including 40 patients with mPSM. Final multivariate logistic regression revealed that the independent risk factors for oPSM were tumor diameter, frank extraprostatic extension, and annual surgery volume (all p < 0.05), and the nomogram for oPSM reached an area under the curve (AUC) of 0.717 in development and 0.716 in internal verification. The independent risk factors for mPSM included the percentage of positive cores, tumor diameter, apex depth, and annual surgery volume (all p < 0.05), and the AUC of the nomogram for mPSM was 0.790 in both development and internal verification. The calibration curve analysis showed that these nomograms were well-calibrated for both oPSM and mPSM.The proposed nomograms showed good performance and were feasible in predicting oPSM and mPSM, which might facilitate more individualized management of prostate cancer patients who are candidates for surgery.© 2024. The Author(s).