Contrast-enhanced MRI-based intratumoral heterogeneity assessment for predicting lymph node metastasis in resectable pancreatic ductal adenocarcinoma

Junjian Shen; Qing Li; Lei Li; Tianyu Lu; Jun Han; Zongyu Xie; Peng Wang; Zirui Cao; Mengsu Zeng; Jianjun Zhou; Tianzhu Yu; Yaolin Xu; Haitao Sun

doi:10.1186/s13244-025-01956-0

Contrast-enhanced MRI-based intratumoral heterogeneity assessment for predicting lymph node metastasis in resectable pancreatic ductal adenocarcinoma

Insights Imaging. 2025 Mar 30;16(1):76. doi: 10.1186/s13244-025-01956-0.

Authors

Junjian Shen^#¹, Qing Li^#¹, Lei Li^#², Tianyu Lu³, Jun Han³, Zongyu Xie⁴, Peng Wang⁵, Zirui Cao⁶, Mengsu Zeng¹, Jianjun Zhou⁷, Tianzhu Yu⁸, Yaolin Xu⁹, Haitao Sun¹⁰

Affiliations

¹ Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China.
² Department of Radiology, Fengyang County People's Hospital, Chuzhou, China.
³ Department of Radiology, The First Hospital of Jiaxing, Affiliated Hospital of Jiaxing University, Jiaxing, China.
⁴ Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
⁵ Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, P.R. China.
⁶ Department of Research Center, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
⁷ Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen Municipal Clinical Research Center for Medical Imaging, Fujian Province Key Clinical Specialty for Medical Imaging, Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence, Xiamen, China.
⁸ Department of Interventional Radiology, Zhongshan Hospital, Shanghai, China.
⁹ Department of Pancreatic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
¹⁰ Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China. sht1720@163.com.

^# Contributed equally.

Abstract

Objectives: To develop and validate a contrast-enhanced MRI-based intratumoral heterogeneity (ITH) model for predicting lymph node (LN) metastasis in resectable pancreatic ductal adenocarcinoma (PDAC).

Methods: Lesions were encoded into different habitats based on enhancement ratios at arterial, venous, and delayed phases of contrast-enhanced MRI. Habitat models on enhanced ratio mapping and single sequences, radiomic models, and clinical models were developed for evaluating LN metastasis. The performance of the models was evaluated via different metrics. Additionally, patients were stratified into high-risk and low-risk groups based on an ensembled model to assess prognosis after adjuvant therapy.

Results: We developed an ensembled radiomics-habitat-clinical (RHC) model that integrates radiomics, habitat, and clinical data for precise prediction of LN metastasis in PDAC. The RHC model showed strong predictive performance, with area under the curve (AUC) values of 0.805, 0.779, and 0.615 in the derivation, internal validation, and external validation cohorts, respectively. Using an optimal threshold of 0.46, the model effectively stratified patients, revealing significant differences in recurrence-free survival and overall survival (OS) (p = 0.004 and p < 0.001). Adjuvant therapy improved OS in the high-risk group (p = 0.004), but no significant benefit was observed in the low-risk group (p = 0.069).

Conclusion: We developed an MRI-based ITH model that provides reliable estimates of LN metastasis for resectable PDAC and may offer additional value in guiding clinical decision-making.

Critical relevance statement: This ensemble RHC model facilitates preoperative prediction of LN metastasis in resectable PDAC using contrast-enhanced MRI. This offers a foundation for enhanced prognostic assessment and supports the management of personalized adjuvant treatment strategies.

Key points: MRI-based habitat models can predict LN metastasis in PDAC. Both the radiomics model and clinical characteristics were useful for predicting LN metastasis in PDAC. The RHC models have the potential to enhance predictive accuracy and inform personalized therapeutic decisions.

Keywords: Habitat; Lymph node metastasis; Magnetic resonance imaging; Pancreatic ductal adenocarcinoma.

Abstract

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