Evidence map›Paper›PMID 40536703›Full record

ArticleEuropean radiology2025

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

Chengwei Chen, Jian Zhou, Shaojia Mo, Jing Li, Xu Fang, Fang Liu, Tiegong Wang, Li Wang, Jianping Lu, Chengwei Shao and 1 more

Abstract readValidation Study
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In one paragraph

Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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2 citing papers in PubMed.

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5 · Who and what money

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11 authors.

Chengwei Chen *Department of Radiology, Changhai Hospital, Shanghai, China.
Jian Zhou *Department of Radiology, Changhai Hospital, Shanghai, China.
Shaojia MoDepartment of Radiology, Changhai Hospital, Shanghai, China.
Jing LiDepartment of Radiology, Changhai Hospital, Shanghai, China.
Xu FangDepartment of Radiology, Changhai Hospital, Shanghai, China.
Fang LiuDepartment of Radiology, Changhai Hospital, Shanghai, China.
Tiegong WangDepartment of Radiology, Changhai Hospital, Shanghai, China.
Li WangDepartment of Radiology, Changhai Hospital, Shanghai, China.
Jianping LuDepartment of Radiology, Changhai Hospital, Shanghai, China.
Chengwei ShaoDepartment of Radiology, Changhai Hospital, Shanghai, China. chengweishaoch@163.com.
Yun BianDepartment of Radiology, Changhai Hospital, Shanghai, China. bianyun2012@foxmail.com.ORCID http://orcid.org/0000-0002-4863-4956

Funding

Clinical Research Plan of SHDC SHDC2022CRD028National Science Foundation for Scientists of China 81871352, 82171915, 82171930, 82271972 and 82371955Natural Science Foundation of Shanghai Science and Technology Innovation Action Plan 21ZR1478500, 21Y11910300
6 · The paper itself

Abstract

objectiveTo 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

methodsThis 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.

resultsOverall, 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.

conclusionThe 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.

Indexed as

Artificial IntelligencePancreatitis, ChronicRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAdultAgedFemaleHumansMaleMiddle AgedRadiomicsRetrospective StudiesSeverity of Illness IndexArtificial intelligenceDisease progressionPancreatitis (Chronic)PathologyTomography (X-ray computed)

Identifiers

PMID40536703

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