基于mp-MRI和临床特征构建前列腺癌根治术的切缘预测模型

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

摘要

目的: 基于多参数核磁共振（multiparametric magnetic resonance imaging，mp-MRI）联合其他具有潜在预测能力的临床特征筛选预测因子并构建前列腺癌根治术后患者的手术切缘情况的预测模型，为手术患者提供一个可靠的预测工具，从而辅助临床决策。方法: 回顾性分析2018年1月—2024年6月于南京医科大学第一附属医院接受前列腺癌根治术的927例患者的临床资料，包括基线特征、术前血清生化指标、mp-MRI影像资料、术前穿刺病理结果、根治术后病理结果等，按照7∶3的比例随机划分为训练集和验证集。对训练集数据进行单因素、多因素Logistic回归分析和向后法逐步回归分析筛选出能预测前列腺癌切缘情况的独立预测因子，建立预测模型并进行决策曲线分析。在验证集数据中检验预测模型的区分能力、准确性及临床实用性。结果: Logistic回归分析筛选出前列腺特异性抗原密度（prostate-specific antigen density，PSAD）、PI-RADS评分、病灶数量、包膜可疑侵犯、可疑淋巴结转移5个独立预测因子（P<0.05），以此构建预测模型，绘制列线图。训练集和验证集的受试者工作特征（receiver operating characteristic curve，ROC）曲线下面积分别为0.897（95%CI：0.874~0.921）和0.841（95%CI：0.793~0.888）。训练集和验证集的校准曲线均紧贴对角线，平均绝对误差（mean absolute error，MAE）分别为0.011、0.009，表明模型未出现明显过拟合且具有较好的泛化能力。决策曲线分析结果表明模型具有临床净获益。结论: mp-MRI在预测前列腺癌手术切缘方面具有显著价值，本研究构建的手术切缘预测模型具有较好的预测能力，可辅助临床决策。

Abstract

Objective: 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. Methods: 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. Results: 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. Conclusion: 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.

关键词

前列腺癌 / 多参数磁共振 / 前列腺癌根治术 / 手术切缘阳性 / 列线图 / 预测模型

Key words

prostate cancer / mp-MRI / radical prostatectomy / positive surgical margin / nomogram / predictive model

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基金资助

宿迁市科技计划项目(SY202424)