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Objective <p>To develop and validate the chronic pancreatitis CT severity model (CATS), an artificial intelligence (AI)-based tool leveraging automated 3D segmentation and radiomics analysis of non-enhanced CT scans for objective severity stratification in chronic pancreatitis (CP).</p> Materials and methods <p>This retrospective study encompassed patients with recurrent acute pancreatitis (RAP) and CP from June 2016 to May 2020. A 3D convolutional neural network segmented non-enhanced CT scans, extracting 1843 radiomic features to calculate the radiomics score (Rad-score). The CATS was formulated using multivariable logistic regression and validated in a subsequent cohort from June 2020 to April 2023.</p> Results <p>Overall, 2054 patients with RAP and CP were included in the training (<i>n</i> = 927), validation set (<i>n</i> = 616), and external test (<i>n</i> = 511) sets. CP grade I and II patients accounted for 300 (14.61%) and 1754 (85.39%), respectively. The Rad-score significantly correlated with the acinus-to-stroma ratio (<i>p</i> = 0.023; OR, −2.44). The CATS model demonstrated high discriminatory performance in differentiating CP severity grades, achieving an area under the curve (AUC) of 0.96 (95% CI: 0.94–0.98) and 0.88 (95% CI: 0.81–0.90) in the validation and test cohorts. CATS-predicted grades correlated with exocrine insufficiency (all <i>p</i> &lt; 0.05) and showed significant prognostic differences (all <i>p</i> &lt; 0.05). CATS outperformed radiologists in detecting calcifications, identifying all minute calcifications missed by radiologists.</p> Conclusion <p>The CATS, developed using non-enhanced CT and AI, accurately predicts CP severity, reflects disease morphology, and forecasts short- to medium-term prognosis, offering a significant advancement in CP management.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Existing CP severity assessments rely on semi-quantitative CT evaluations and multi-modality imaging, leading to inconsistency and inaccuracy in early diagnosis and prognosis prediction</i>.</p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>The AI-driven CATS model, using non-enhanced CT, achieved high accuracy in grading CP severity, and correlated with histopathological fibrosis markers</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>CATS provides a cost-effective, widely accessible tool for precise CP severity stratification, enabling early intervention, personalized management, and improved outcomes without contrast agents or invasive biopsies</i>.</p> Graphical Abstract <p></p>

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Development and validation of an AI-driven radiomics model using non-enhanced CT for automated severity grading in chronic pancreatitis

  • 期刊论文

  • 发表日期：

    2025年6月19日

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  • Chengwei Chen,
  • Jian Zhou,
  • Shaojia Mo,
  • Jing Li,
  • Xu Fang,
  • Fang Liu,
  • Tiegong Wang,
  • Li Wang,
  • Jianping Lu,
  • Chengwei Shao,
  • Yun Bian

摘要

Objective

To develop and validate the chronic pancreatitis CT severity model (CATS), an artificial intelligence (AI)-based tool leveraging automated 3D segmentation and radiomics analysis of non-enhanced CT scans for objective severity stratification in chronic pancreatitis (CP).

Materials and methods

This retrospective study encompassed patients with recurrent acute pancreatitis (RAP) and CP from June 2016 to May 2020. A 3D convolutional neural network segmented non-enhanced CT scans, extracting 1843 radiomic features to calculate the radiomics score (Rad-score). The CATS was formulated using multivariable logistic regression and validated in a subsequent cohort from June 2020 to April 2023.

Results

Overall, 2054 patients with RAP and CP were included in the training (n = 927), validation set (n = 616), and external test (n = 511) sets. CP grade I and II patients accounted for 300 (14.61%) and 1754 (85.39%), respectively. The Rad-score significantly correlated with the acinus-to-stroma ratio (p = 0.023; OR, −2.44). The CATS model demonstrated high discriminatory performance in differentiating CP severity grades, achieving an area under the curve (AUC) of 0.96 (95% CI: 0.94–0.98) and 0.88 (95% CI: 0.81–0.90) in the validation and test cohorts. CATS-predicted grades correlated with exocrine insufficiency (all p < 0.05) and showed significant prognostic differences (all p < 0.05). CATS outperformed radiologists in detecting calcifications, identifying all minute calcifications missed by radiologists.

Conclusion

The CATS, developed using non-enhanced CT and AI, accurately predicts CP severity, reflects disease morphology, and forecasts short- to medium-term prognosis, offering a significant advancement in CP management.

Key Points

Question Existing CP severity assessments rely on semi-quantitative CT evaluations and multi-modality imaging, leading to inconsistency and inaccuracy in early diagnosis and prognosis prediction.

Findings The AI-driven CATS model, using non-enhanced CT, achieved high accuracy in grading CP severity, and correlated with histopathological fibrosis markers.

Clinical relevance CATS provides a cost-effective, widely accessible tool for precise CP severity stratification, enabling early intervention, personalized management, and improved outcomes without contrast agents or invasive biopsies.

Graphical Abstract