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Purpose <p>To develop a radiomics model to predict lymph nodes metastasis (LNM) in patients with pancreatic ductal adenocarcinoma (PDAC) and assess its value for clinical management.</p> Methods <p>Patients with pathologically confirmed PDAC from four centers were retrospectively enrolled and split into four cohorts: training (<i>n</i> = 192), validation (<i>n</i> = 82), testing (<i>n</i> = 100), and clinical utilization (<i>n</i> = 163). A radiomics model was constructed based on contrast-enhanced CT (CECT) to predict LNM, and its performance was evaluated using the areas under the curve (AUC). Kaplan–Meier analysis was used to assess the prognostic and therapeutic decision-assisting value of the radiomics model.</p> Results <p>A total of 437 patients (mean age: 63.1 years ± 9.2 standard deviation; 253 men) were included. The radiomics model outperformed other models with AUCs of 0.84, 0.82, and 0.78 in the training, validation, and testing cohorts (all <i>p</i> &lt; 0.05), respectively. LNM predicted by the radiomics model was significantly associated with overall survival (<i>p</i> &lt; 0.001). Kaplan–Meier analysis revealed that patients with a higher risk of LNM also had worse outcomes (all <i>p</i> &lt; 0.05). Additionally, among the high-risk subgroup identified by the radiomics model in the clinical utilization cohort, patients who underwent dissection of ≥ 15 lymph nodes exhibited better overall survival compared to those with fewer lymph nodes dissected (<i>p</i> = 0.002).</p> Conclusion <p>The radiomics model we constructed demonstrated impressive performance in predicting LNM and prognosis, suggesting its potential for optimizing the clinical management of PDAC.</p> Critical relevance statement <p>This radiomics model can predict the risk of lymph nodes metastasis and prognosis of patients in pancreatic ductal adenocarcinoma and has potential value in selecting patients who can benefit from different extents of lymph nodes dissection.</p> Key Points <p><UnorderedList Mark="Bullet"> <ItemContent> <p>Thorough lymph node dissection is important for achieving the best prognosis in pancreatic ductal adenocarcinoma (PDAC).</p> </ItemContent> <ItemContent> <p>The radiomics model can accurately predict lymph node status and stratify patients’ prognosis.</p> </ItemContent> <ItemContent> <p>This radiomics model enhances the clinical management of PDAC.</p> </ItemContent> </UnorderedList></p> Graphical Abstract <p></p>

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A radiomics-based model for predicting lymph nodes metastasis of pancreatic ductal adenocarcinoma: a multicenter study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月27日

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  • Liwen Zhu,
  • Ben Zhao,
  • Tianyi Xia,
  • Di Chang,
  • Cong Xia,
  • Mengqiu Liu,
  • Ridong Li,
  • Buyue Cao,
  • Yue Qiu,
  • Yaoyao Yu,
  • Shuwei Zhou,
  • Huayu Chen,
  • Wu Cai,
  • Zhimin Ding,
  • Chunqiang Lu,
  • Tianyu Tang,
  • Yang Song,
  • Yuancheng Wang,
  • Jing Ye,
  • Ying Liu,
  • Shenghong Ju

摘要

Purpose

To develop a radiomics model to predict lymph nodes metastasis (LNM) in patients with pancreatic ductal adenocarcinoma (PDAC) and assess its value for clinical management.

Methods

Patients with pathologically confirmed PDAC from four centers were retrospectively enrolled and split into four cohorts: training (n = 192), validation (n = 82), testing (n = 100), and clinical utilization (n = 163). A radiomics model was constructed based on contrast-enhanced CT (CECT) to predict LNM, and its performance was evaluated using the areas under the curve (AUC). Kaplan–Meier analysis was used to assess the prognostic and therapeutic decision-assisting value of the radiomics model.

Results

A total of 437 patients (mean age: 63.1 years ± 9.2 standard deviation; 253 men) were included. The radiomics model outperformed other models with AUCs of 0.84, 0.82, and 0.78 in the training, validation, and testing cohorts (all p < 0.05), respectively. LNM predicted by the radiomics model was significantly associated with overall survival (p < 0.001). Kaplan–Meier analysis revealed that patients with a higher risk of LNM also had worse outcomes (all p < 0.05). Additionally, among the high-risk subgroup identified by the radiomics model in the clinical utilization cohort, patients who underwent dissection of ≥ 15 lymph nodes exhibited better overall survival compared to those with fewer lymph nodes dissected (p = 0.002).

Conclusion

The radiomics model we constructed demonstrated impressive performance in predicting LNM and prognosis, suggesting its potential for optimizing the clinical management of PDAC.

Critical relevance statement

This radiomics model can predict the risk of lymph nodes metastasis and prognosis of patients in pancreatic ductal adenocarcinoma and has potential value in selecting patients who can benefit from different extents of lymph nodes dissection.

Key Points

Thorough lymph node dissection is important for achieving the best prognosis in pancreatic ductal adenocarcinoma (PDAC).

The radiomics model can accurately predict lymph node status and stratify patients’ prognosis.

This radiomics model enhances the clinical management of PDAC.

Graphical Abstract