Du Xinze, Ma Yongsu, Wang Kexin, Zhong Xiejian, Wang Jianxin, Tian Xiaodong, Wang Xiaoying, Yang Yinmo
Department of Hepatobiliary and Pancreatic Surgery, Peking University First Hospital, Beijing, China.
Department of Radiology, Peking University First Hospital, Beijing, China.
J Appl Clin Med Phys. 2025 Jun;26(6):e70092. doi: 10.1002/acm2.70092. Epub 2025 Apr 11.
To assess the predictive capability of CT radiomics features for early recurrence (ER) of pancreatic ductal adenocarcinoma (PDAC).
Postoperative PDAC patients were retrospectively selected, all of whom had undergone preoperative CT imaging and surgery. Both patients with resectable or borderline-resectable pancreatic cancer met the eligibility criteria in this study. However, owing to the differences in treatment strategies and such, this research mainly focused on patients with resectable pancreatic cancer. All patients were subject to follow-up assessments for a minimum of 9 months. A total of 250 cases meeting the inclusion criteria were included. A clinical model, a conventional radiomics model, and a deep-radiomics model were constructed for ER prediction (defined as occurring within 9 months) in the training set. A model based on the TNM staging was utilized as a baseline for comparison. Assessment of the models' performance was based on the area under the receiver operating characteristic curve (AUC). Additionally, precision-recall (PR) analysis and calibration assessments were conducted for model evaluation. Furthermore, the clinical utility of the models was evaluated through decision curve analysis (DCA), net reclassification improvement (NRI), and improvement of reclassification index (IRI).
In the test set, the AUC values for ER prediction were as follows: TNM staging, ROC-AUC = 0.673 (95% CI: 0.550, 0.795), PR-AUC = 0.362 (95% CI: 0.493, 0.710); clinical model, ROC-AUC = 0.640 (95% CI: 0.504, 0.775), PR-AUC = 0.481 (95% CI: 0.520, 0.735); radiomics model, ROC-AUC = 0.722 (95% CI: 0.604, 0.839), PR-AUC = 0.575 (95% CI: 0.466, 0.686); and deep-radiomics model, which exhibited the highest ROC-AUC of 0.895 (95% CI: 0.820, 0.970), PR-AUC = 0.834 (95% CI: 0.767, 0.923). The difference in both ROC-AUC and PR-AUC for the deep-radiomics model was statistically significant when compared to the other scores (all p < 0.05). The DCA curve of the deep-radiomics model outperformed the other models. NRI and IRI analyses demonstrated that the deep-radiomics model significantly enhances risk classification compared to the other prediction methods (all p < 0.05).
The predictive performance of deep features based on CT images exhibits favorable outcomes in predicting early recurrence.
评估CT影像组学特征对胰腺导管腺癌（PDAC）早期复发（ER）的预测能力。
回顾性选取接受过术前CT成像和手术的术后PDAC患者。可切除或边界可切除胰腺癌患者均符合本研究的纳入标准。然而，由于治疗策略等方面的差异，本研究主要聚焦于可切除胰腺癌患者。所有患者均接受至少9个月的随访评估。共纳入250例符合纳入标准的病例。在训练集中构建临床模型、传统影像组学模型和深度影像组学模型用于ER预测（定义为在9个月内发生）。将基于TNM分期的模型作为比较基线。基于受试者工作特征曲线（AUC）下面积评估模型性能。此外，进行精确召回率（PR）分析和校准评估以进行模型评价。进一步通过决策曲线分析（DCA）、净重新分类改善（NRI）和重新分类指数改善（IRI）评估模型的临床实用性。
在测试集中，ER预测的AUC值如下：TNM分期，ROC-AUC = 0.673（95%CI：0.550，0.795），PR-AUC = 0.362（95%CI：0.493，0.710）；临床模型，ROC-AUC = 0.640（95%CI：0.504，0.775），PR-AUC = 0.481（95%CI：0.520，0.735）；影像组学模型，ROC-AUC = 0.722（95%CI：0.604，0.839），PR-AUC = 0.575（95%CI：0.466，0.686）；深度影像组学模型的ROC-AUC最高，为0.895（95%CI：0.820，0.970），PR-AUC = 0.834（95%CI：0.767，0.923）。与其他评分相比，深度影像组学模型的ROC-AUC和PR-AUC差异均具有统计学意义（均p < 0.05）。深度影像组学模型的DCA曲线优于其他模型。NRI和IRI分析表明，与其他预测方法相比，深度影像组学模型显著提高了风险分类（均p < 0.05）。
基于CT图像的深度特征在预测早期复发方面表现出良好的预测性能。
