Wu Linxia, Cen Chunyuan, Ouyang Die, Zhang Licai, Li Xin, Wu Heshui, He Ming, Han Ping, Tan Wei, Chen Lei, Zheng Chuansheng
Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, Hubei, China.
Int J Surg. 2025 Jul 15. doi: 10.1097/JS9.0000000000003078.
Pancreatic ductal adenocarcinoma (PDAC) is associated with a high rate of early recurrence (ER) after radical resection, which significantly affects long-term survival. Currently, no reliable system exists for predicting ER in these patients. This study aimed to develop a machine learning (ML) model combining intratumoral and peritumoral radiomic features with body composition to predict the ER risk in patients with PDAC following radical resection.
This study included patients with PDAC who underwent upfront surgery at four hospitals between June 2014 and December 2023. Preoperative clinical information, CT images, and postoperative pathological data were collected. CT-quantified body composition was measured; radiomic features were extracted from the intratumoral and peritumoral regions. Six ML algorithms were used to develop predictive models, including radiomics, clinical, clinical-radiomics, and clinicopathological-radiomics models. The SHapley Additive exPlanations (SHAP) method was applied for model interpretability.
A total of 589 patients were evaluated, including 320 patients (mean age: 60.4 ± 8.3 years; 191 men) in the training cohort, 138 patients (mean age: 60.7 ± 8.9 years; 84 men) in the internal validation cohort, and 131 patients (mean age: 61.7 ± 10.9 years; 76 men) in the external validation cohort. The intra-peri-radiomics model, based on the random forest algorithm, achieved the best performance, with AUCs of 0.865, 0.849, and 0.839 in the training, internal validation, and external validation cohorts, respectively. Incorporating clinicopathological factors, the combined model showed superior performance, with AUCs of 0.936, 0.899, and 0.884 in the training, internal validation, and external validation cohorts, respectively. SHAP analysis revealed that radiomic features, adjuvant therapy, CA199, lymphovascular invasion, platelet-lymphocyte ratio, visceral fat index, CA125, visceral-subcutaneous fat tissue ratio, tumor size, and TNM stage significantly contributed to the prediction of ER.
The developed ML model, integrating radiomic features and clinicopathological factors, offered superior predictive accuracy for ER in patients with PDAC post-surgery. SHAP visualization enhanced the model's interpretability and facilitated clinical applications.
胰腺导管腺癌（PDAC）在根治性切除术后早期复发（ER）率较高，这显著影响长期生存。目前，尚无可靠系统可预测这些患者的早期复发。本研究旨在开发一种机器学习（ML）模型，将肿瘤内和肿瘤周围的放射组学特征与身体成分相结合，以预测PDAC患者根治性切除术后的早期复发风险。
本研究纳入了2014年6月至2023年12月期间在四家医院接受初次手术的PDAC患者。收集术前临床信息、CT图像和术后病理数据。测量CT定量的身体成分；从肿瘤内和肿瘤周围区域提取放射组学特征。使用六种ML算法开发预测模型，包括放射组学、临床、临床-放射组学和临床病理-放射组学模型。采用SHapley加性解释（SHAP）方法进行模型解释。
共评估了589例患者，其中训练队列320例（平均年龄：60.4±8.3岁；男性191例），内部验证队列138例（平均年龄：60.7±8.9岁；男性84例），外部验证队列131例（平均年龄：61.7±10.9岁；男性76例）。基于随机森林算法的肿瘤内-肿瘤周围放射组学模型表现最佳，在训练、内部验证和外部验证队列中的AUC分别为0.865、0.849和0.839。纳入临床病理因素后，联合模型表现更优，在训练、内部验证和外部验证队列中的AUC分别为0.936、0.899和0.884。SHAP分析显示，放射组学特征、辅助治疗、CA199、淋巴管侵犯、血小板-淋巴细胞比值、内脏脂肪指数、CA125、内脏-皮下脂肪组织比值、肿瘤大小和TNM分期对早期复发的预测有显著贡献。
所开发的整合放射组学特征和临床病理因素的ML模型，对PDAC术后患者的早期复发具有较高的预测准确性。SHAP可视化增强了模型的可解释性，便于临床应用。
