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<p>Accurate detection of the histological grade of pancreatic neuroendocrine tumors (PanNETs) is important for patients’ prognoses and treatment. Traditional grading methods based on mitotic count and Ki-67 index are subjective and time-consuming. In this work, we developed and validated a pathomics model for accurate grading of PanNETs. Pathomics features were extracted from H&amp;E whole slide images (WSIs) using Lasso regression to create a pathomics score. Its performance was evaluated in three cohorts involving 2 centers and 272 patients. This score was significantly associated with PanNET grade and could differentiate between high- and low-risk groups. In the validation and test cohorts, the pathomics model, which combined the pathomics score with clinical features, achieved AUCs of 0.85 and 0.93, respectively. In conclusion, our model enhances data processing efficiency and provides a quantitative assessment of histological features, holding promise for guiding individualized treatment and risk stratification in PanNET management.</p><p>This study was registered at ClinicalTrials. Trial number: gov ChiCTR2400090898</p>

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Development and validation of a pathomics model for accurate grading of pancreatic neuroendocrine tumors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月10日

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  • Ying Li,
  • Chengwei Chen,
  • Yunshuo Zhang,
  • Jieyu Yu,
  • Xu Fang,
  • Fang Liu,
  • Tiegong Wang,
  • Jing Li,
  • Li Wang,
  • Jianping Lu,
  • Hui Jiang,
  • Chengwei Shao,
  • Yun Bian

摘要

Accurate detection of the histological grade of pancreatic neuroendocrine tumors (PanNETs) is important for patients’ prognoses and treatment. Traditional grading methods based on mitotic count and Ki-67 index are subjective and time-consuming. In this work, we developed and validated a pathomics model for accurate grading of PanNETs. Pathomics features were extracted from H&E whole slide images (WSIs) using Lasso regression to create a pathomics score. Its performance was evaluated in three cohorts involving 2 centers and 272 patients. This score was significantly associated with PanNET grade and could differentiate between high- and low-risk groups. In the validation and test cohorts, the pathomics model, which combined the pathomics score with clinical features, achieved AUCs of 0.85 and 0.93, respectively. In conclusion, our model enhances data processing efficiency and provides a quantitative assessment of histological features, holding promise for guiding individualized treatment and risk stratification in PanNET management.

This study was registered at ClinicalTrials. Trial number: gov ChiCTR2400090898