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Objectives <p>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).</p> Methods <p>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.</p> Results <p>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) (<i>p</i> = 0.004 and <i>p</i> &lt; 0.001). Adjuvant therapy improved OS in the high-risk group (<i>p</i> = 0.004), but no significant benefit was observed in the low-risk group (<i>p</i> = 0.069).</p> Conclusion <p>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.</p> Critical relevance statement <p>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.</p> Key Points <p><UnorderedList Mark="Bullet"> <ItemContent> <p>MRI-based habitat models can predict LN metastasis in PDAC.</p> </ItemContent> <ItemContent> <p>Both the radiomics model and clinical characteristics were useful for predicting LN metastasis in PDAC.</p> </ItemContent> <ItemContent> <p>The RHC models have the potential to enhance predictive accuracy and inform personalized therapeutic decisions.</p> </ItemContent> </UnorderedList></p> Graphical Abstract <p></p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月30日

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  • 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

摘要

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.

Graphical Abstract