Deng Yan, Yu Haopeng, Duan Xiuping, Liu Li, Huang Zixing, Song Bin
Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.
Department of Radiology, Sichuan Key Laboratory of Medical Imaging, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.
Front Oncol. 2025 Mar 4;15:1525835. doi: 10.3389/fonc.2025.1525835. eCollection 2025.
To develop a nomogram based on CT radiomics features for preoperative prediction of perineural invasion (PNI) in pancreatic ductal adenocarcinoma (PDAC) patients.
A total of 217 patients with histologically confirmed PDAC were enrolled in this retrospective study. Radiomics features were extracted from the whole tumor. Univariate analysis, least absolute shrinkage and selection operator and logistic regression were applied for feature selection and radiomics model construction. Finally, a nomogram combining the radiomics score (Rad-score) and clinical characteristics was established. Receiver operating characteristic curve analysis, calibration curve analysis and decision curve analysis (DCA) were used to evaluate the predictive performance of the nomogram.
According to multivariate analysis, CT features, including the radiologists evaluated PNI status based on CECT (CTPNI) (OR=1.971 [95% CI: 1.165, 3.332], P=0.01), the lymph node status determined on CECT (CTLN) (OR=2.506 [95%: 1.416, 4.333], P=0.001) and the Rad-score (OR=3.666 [95% CI: 2.069, 6.494], P<0.001), were significantly associated with PNI. The area under the receiver operating characteristic curve (AUC) for the nomogram combined with the Rad-score, CTLN and CTPNI achieved favorable discrimination of PNI status, with AUCs of 0.846 and 0.778 in the training and testing cohorts, respectively, which were superior to those of the Rad-score (AUC of 0.720 in the training cohort and 0.640 in the testing cohort) and CTPNI (AUC of 0.610 in the training cohort and 0.675 in the testing cohort). The calibration plot and decision curve showed good results.
The CT-based radiomics nomogram has the potential to accurately predict PNI in patients with PDAC.
基于CT影像组学特征开发一种列线图，用于术前预测胰腺导管腺癌（PDAC）患者的神经周围侵犯（PNI）。
本回顾性研究共纳入217例经组织学证实的PDAC患者。从整个肿瘤中提取影像组学特征。采用单因素分析、最小绝对收缩和选择算子以及逻辑回归进行特征选择和影像组学模型构建。最后，建立了一个结合影像组学评分（Rad-score）和临床特征的列线图。采用受试者操作特征曲线分析、校准曲线分析和决策曲线分析（DCA）来评估列线图的预测性能。
多因素分析显示，CT特征，包括放射科医生根据增强CT（CECT）评估的PNI状态（CTPNI）（OR=1.971[95%CI：1.165，3.332]，P=0.01）、CECT确定的淋巴结状态（CTLN）（OR=2.506[95%：1.416，4.333]，P=0.001）和Rad-score（OR=3.666[95%CI：2.069，6.494]，P<0.001），与PNI显著相关。结合Rad-score、CTLN和CTPNI的列线图的受试者操作特征曲线下面积（AUC）对PNI状态具有良好的区分能力，训练队列和测试队列的AUC分别为0.846和0.778，优于Rad-score（训练队列AUC为0.720，测试队列AUC为0.640）和CTPNI（训练队列AUC为0.610，测试队列AUC为0.675）。校准图和决策曲线显示结果良好。
基于CT的影像组学列线图有可能准确预测PDAC患者的PNI。
