Development and validation of a clinical prediction model for fatty pancreas disease: from CT-based indicators to an easy-to-use prediction application

Fatty pancreas disease (FPD) is closely associated with the pathogenesis of pancreatic cancer (PC) and diabetes mellitus (DM). However, current imaging modalities for assessing intra-pancreatic fat deposition (IPFD) have limitations and are not widely used in clinical practice. Developing a practical prediction tool for FPD would facilitate the evaluation of pancreatic health. A total of 852 subjects were included in the model construction cohort, and 202 PC patients were included to investigate the association between fat deposition and PC. Quantitative assessment of IPFD and diagnosis of FPD were based on the pancreas-to-spleen attenuation ratio (P/S ratio) from CT scans. Multivariate logistic regression was used to identify risk factors associated with FPD, and a web-based application was deployed based on these factors. The performance of the model was validated in an internal cohort with respect to discrimination, calibration and clinical benefit. The incidence of PC and DM significantly increased as the P/S ratio decreased, particularly when P/S ratio fell below 0.8. Multivariate analysis identified seven independent risk factors: age > 65 years, abnormal waist circumference, abnormal gamma-glutamyl transferase, fasting plasma glucose > 6.1mmol/L, NLR > 1.97, fatty liver index > 24.7, and mFIB-4 > 3.05. A web application was deployed based on these risk factors. The model demonstrated good discrimination, with an AUC of 0.750 (95% CI: 0.707–0.793) in the development cohort and 0.723 (95% CI: 0.652–0.795) in the validation cohort, along with satisfactory calibration and clinical net benefit. Furthermore, regression analysis revealed significant correlations between model-predicted risk values and IPFD severity, indicating that the FPD model effectively captures the severity of fat deposition in the pancreas. This multidimensional predictive model enables comprehensive evaluation of FPD risk using routinely available clinical parameters, which can serve as an effective preliminary screening tool to guide subsequent examinations and interventions.

Authors

Institutions

Publication Details

Journal
Nutrition Journal
Published
2026-02-27
DOI
https://doi.org/10.1186/s12937-026-01299-6
Primary Topic
Pancreatitis Pathology and Treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development and validation of a clinical prediction model for fatty pancreas disease: from CT-based indicators to an easy-to-use prediction application

Haoteng Luo, Fangsheng Chen, Fengchun Lü, Tianhong Teng et al.
Nutrition Journal
Pancreatitis Pathology and Treatment
article

Development and validation of a clinical prediction model for fatty pancreas disease: from CT-based indicators to an easy-to-use prediction application

Haoteng Luo, Fangsheng Chen, Fengchun Lü, Tianhong Teng, Heguang Huang, Cong Chen, Weiwei Xue, Yuan Zhou, Hanting Dai
article en

Abstract

Fatty pancreas disease (FPD) is closely associated with the pathogenesis of pancreatic cancer (PC) and diabetes mellitus (DM). However, current imaging modalities for assessing intra-pancreatic fat deposition (IPFD) have limitations and are not widely used in clinical practice. Developing a practical prediction tool for FPD would facilitate the evaluation of pancreatic health. A total of 852 subjects were included in the model construction cohort, and 202 PC patients were included to investigate the association between fat deposition and PC. Quantitative assessment of IPFD and diagnosis of FPD were based on the pancreas-to-spleen attenuation ratio (P/S ratio) from CT scans. Multivariate logistic regression was used to identify risk factors associated with FPD, and a web-based application was deployed based on these factors. The performance of the model was validated in an internal cohort with respect to discrimination, calibration and clinical benefit. The incidence of PC and DM significantly increased as the P/S ratio decreased, particularly when P/S ratio fell below 0.8. Multivariate analysis identified seven independent risk factors: age > 65 years, abnormal waist circumference, abnormal gamma-glutamyl transferase, fasting plasma glucose > 6.1mmol/L, NLR > 1.97, fatty liver index > 24.7, and mFIB-4 > 3.05. A web application was deployed based on these risk factors. The model demonstrated good discrimination, with an AUC of 0.750 (95% CI: 0.707–0.793) in the development cohort and 0.723 (95% CI: 0.652–0.795) in the validation cohort, along with satisfactory calibration and clinical net benefit. Furthermore, regression analysis revealed significant correlations between model-predicted risk values and IPFD severity, indicating that the FPD model effectively captures the severity of fat deposition in the pancreas. This multidimensional predictive model enables comprehensive evaluation of FPD risk using routinely available clinical parameters, which can serve as an effective preliminary screening tool to guide subsequent examinations and interventions.

Nutrition Journal
Fujian Medical University (CN), Qinghai University Affiliated Hospital (CN), Union Hospital (CN)
Peace, Justice and strong institutions, Reduced inequalities
Openalex Percentile: Top 41%
Pancreatitis Pathology and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.