CT-based habitat analysis combined with multi-channel deep learning for predicting early recurrence after pancreatic cancer resection: a multicenter study.
Authors
Affiliations (6)
Affiliations (6)
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China.
- Department of General Surgery (Hepatobiliary Surgery), The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China. [email protected].
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China. [email protected].
Abstract
Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, with high early recurrence rates after curative resection. Current prediction methods, based on clinicopathological features or conventional radiomics, often fail to capture intratumoral heterogeneity (ITH), a key driver of recurrence. Computed tomography (CT)-based habitat analysis quantifies ITH by identifying phenotypically distinct tumor subregions, while deep learning (DL) can extract complex imaging patterns. Their integration may improve recurrence risk assessment. This study aimed to develop and validate a fusion model that integrates CT-based habitat analysis, a 2.5D convolutional neural network (CNN)-Transformer DL framework, and clinicopathological features to noninvasively predict early recurrence (within one year) risk after PDAC resection. In this multicenter retrospective study, 346 patients with resected PDAC were included from four institutions. Tumors were segmented into three habitat subregions via unsupervised K‑means clustering. Radiomic features from these subregions constructed the HabitatAll model. In parallel, a DL model was built using a 2.5D CNN-Transformer architecture. Predictive scores from both models were integrated with key clinicopathological variables through ridge regression to develop the fusion model (HADLC). Model interpretability was examined using SHAP (SHapley Additive exPlanations) and Grad‑CAM (Gradient‑weighted Class Activation Mapping). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, calibration curves, and decision curve analysis (DCA). The HADLC model showed superior predictive ability, achieving AUCs of 0.977 (training), 0.916 (internal test), and 0.838-0.866 (external validation), outperforming the standalone HabitatAll, DL, and Clinic models. It demonstrated good calibration and provided higher net clinical benefit across most decision thresholds. Interpretability analyses revealed key imaging phenotypes linked to aggressive tumor biology. The HADLC model effectively integrates multimodal information to accurately assess early postoperative recurrence risk in PDAC, providing a robust, non-invasive imaging biomarker to potentially guide personalized treatment.