Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuaifuyuan No.1, Dongcheng District, Beijing 100730, China.
CT Collaboration, Siemens Healthineers, Beijing, China.
Acad Radiol. 2023 Apr;30(4):680-688. doi: 10.1016/j.acra.2022.05.019. Epub 2022 Jul 27.
To develop and validate an effective model for identifying patients with postoperative local disease recurrence of pancreatic ductal adenocarcinoma (PDAC).
A total of 153 patients who had undergone surgical resection of PDAC with regular postoperative follow-up were consecutively enrolled and randomly divided into training (n = 108) and validation (n = 45) cohorts. The postoperative soft-tissue biopsy results or clinical follow-up results served as the reference diagnostic criteria. Radiomics analysis of the postoperative soft-tissue was performed on a commercially available prototype software using portal vein phase image. Three models were built to characterize postoperative soft tissue: computed tomography (CT)-based radiomics, clinicoradiological, and their combination. The area under the receiver operating characteristic curves (AUC) was used to evaluate the differential diagnostic performance. A nomogram was used to select the final model with best performance. One radiologist's diagnostic choices that were made with and without the nomogram's assistance were evaluated.
A seven-feature-combined radiomics signature was constructed as a predictor of postoperative local recurrence. The nomogram model combining the radiomics signature with postoperative CA 19-9 elevation showed the best performance (training cohort, AUC = 0.791 [95%CI: 0.707, 0.876]; validation cohort, AUC = 0.742 [95%CI: 0.590, 0.894]). In the validation cohort, the AUC for differential diagnosis was significantly improved for the combined model relative to that for postoperative CA 19-9 elevation (AUC = 0.742 vs. 0.533, p < 0.001). The calibration curve and decision curve analysis demonstrated the clinical usefulness of the proposed nomogram. The diagnostic performance of the radiologist was not significantly improve by using the proposed nomogram (AUC = 0.742 vs. 0.670, p = 0.17).
The combined model using CT radiomic features and CA 19-9 elevation effectively characterized postoperative soft tissue and potentially may improve treatment strategies and facilitate personalized treatment for PDAC after surgical resection.
开发并验证一种有效模型，用于识别胰腺导管腺癌（PDAC）术后局部疾病复发的患者。
连续纳入 153 例接受 PDAC 手术切除且术后定期随访的患者，并将其随机分为训练队列（n=108）和验证队列（n=45）。术后软组织活检结果或临床随访结果作为参考诊断标准。使用商业可用的原型软件，基于门静脉期图像对术后软组织进行放射组学分析。构建了三种模型来描述术后软组织：基于 CT 的放射组学、临床放射学及其组合。采用受试者工作特征曲线下面积（AUC）评估鉴别诊断性能。采用列线图选择性能最佳的最终模型。评估了一位放射科医生在有和没有列线图辅助的情况下的诊断选择。
构建了一个由七个特征组成的放射组学特征组合，作为术后局部复发的预测因子。联合放射组学特征和术后 CA 19-9 升高的列线图模型表现最佳（训练队列，AUC=0.791[95%CI：0.707，0.876]；验证队列，AUC=0.742[95%CI：0.590，0.894]）。在验证队列中，与术后 CA 19-9 升高相比，联合模型的鉴别诊断 AUC 显著提高（AUC=0.742 比 0.533，p<0.001）。校准曲线和决策曲线分析表明了所提出的列线图的临床实用性。使用所提出的列线图并没有显著提高放射科医生的诊断性能（AUC=0.742 比 0.670，p=0.17）。
联合使用 CT 放射组学特征和 CA 19-9 升高的模型可有效描述术后软组织，并可能改善治疗策略，为 PDAC 术后的个体化治疗提供便利。
