Scholarlink

Objectives <p>Hypertriglyceridemic acute pancreatitis (HTG-AP) carries a high risk of severe disease (HTG-SAP). Early recognition of patients likely to progress to HTG-SAP is crucial for timely intervention. This study aimed to develop and validate a combined T2-weighted MRI (magnetic resonance imaging) radiomics-clinical model for accurate, noninvasive early prediction of SAP in HTG-AP and to compare its performance with established clinical scores.</p> Methods <p>This retrospective analysis incorporated a derivation cohort of 207 patients with HTG-AP, who were classified as non-SAP (<i>n</i>=159) or SAP (<i>n</i>=48) based on the 2012 revised Atlanta criteria. These patients were randomly assigned to internal training and internal test sets at a 7:3 ratio. An additional 55 patients from an independent hospital campus within the same medical group were collected as an external validation cohort. The pancreas was manually delineated on T2-weighted images using 3D Slicer, while extraction of radiomic features was undertaken using PyRadiomics. After testing reproducibility and performing multistep feature selection, including LASSO regression, a random-forest model based on clinical and radiomic features was constructed. Predictive performance was evaluated using AUC values from ROC curves, DeLong’s test, calibration curves, and decision curve analysis. For further robust assessment in the imbalanced dataset, Precision-Recall (PR) curves and their corresponding PR-AUC were also computed. The model, trained on the internal training set of the derivation cohort, was then applied to predict outcomes in the external validation cohort, with performance assessed using the same metrics.</p> Results <p>The integrated radiomics-clinical model, which included six radiomic descriptors and four clinical indicators, yielded AUCs of 0.955 (95% CI: 0.914–0.997) in the training set and 0.954 (95% CI: 0.907–0.996) in the internal test set, and 0.930 (95% CI: 0.865–0.995) in the external validation cohort. These values were substantially higher than those obtained with radiomics-only, clinical-only, BISAP, or MRSI models. Additionally, the model demonstrated robust performance on PR analysis, with PR-AUCs of 0.906 (95% CI: 0.820–0.971) in the internal training set and 0.868 (95% CI: 0.685–0.977) in the internal test set. The model also demonstrated good calibration and achieved the highest net benefit over a wide range of decision thresholds.</p> Conclusions <p>A model combining MRI T2-weighted radiomics with clinical variables enables highly accurate early prediction of SAP in HTG-AP. This noninvasive approach holds promise for facilitating early risk stratification and supporting personalized treatment strategies in clinical practice.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Radiomics derived from MRI T2-weighted imaging combined with clinical variables for predicting disease severity in hypertriglyceridemic acute pancreatitis

  • 期刊论文

  • 发表日期：

    2026年4月2日

查看全文
  • Yanting Li,
  • Yuan Wang,
  • Xiyao Wan,
  • Xiaohua Huang

摘要

Objectives

Hypertriglyceridemic acute pancreatitis (HTG-AP) carries a high risk of severe disease (HTG-SAP). Early recognition of patients likely to progress to HTG-SAP is crucial for timely intervention. This study aimed to develop and validate a combined T2-weighted MRI (magnetic resonance imaging) radiomics-clinical model for accurate, noninvasive early prediction of SAP in HTG-AP and to compare its performance with established clinical scores.

Methods

This retrospective analysis incorporated a derivation cohort of 207 patients with HTG-AP, who were classified as non-SAP (n=159) or SAP (n=48) based on the 2012 revised Atlanta criteria. These patients were randomly assigned to internal training and internal test sets at a 7:3 ratio. An additional 55 patients from an independent hospital campus within the same medical group were collected as an external validation cohort. The pancreas was manually delineated on T2-weighted images using 3D Slicer, while extraction of radiomic features was undertaken using PyRadiomics. After testing reproducibility and performing multistep feature selection, including LASSO regression, a random-forest model based on clinical and radiomic features was constructed. Predictive performance was evaluated using AUC values from ROC curves, DeLong’s test, calibration curves, and decision curve analysis. For further robust assessment in the imbalanced dataset, Precision-Recall (PR) curves and their corresponding PR-AUC were also computed. The model, trained on the internal training set of the derivation cohort, was then applied to predict outcomes in the external validation cohort, with performance assessed using the same metrics.

Results

The integrated radiomics-clinical model, which included six radiomic descriptors and four clinical indicators, yielded AUCs of 0.955 (95% CI: 0.914–0.997) in the training set and 0.954 (95% CI: 0.907–0.996) in the internal test set, and 0.930 (95% CI: 0.865–0.995) in the external validation cohort. These values were substantially higher than those obtained with radiomics-only, clinical-only, BISAP, or MRSI models. Additionally, the model demonstrated robust performance on PR analysis, with PR-AUCs of 0.906 (95% CI: 0.820–0.971) in the internal training set and 0.868 (95% CI: 0.685–0.977) in the internal test set. The model also demonstrated good calibration and achieved the highest net benefit over a wide range of decision thresholds.

Conclusions

A model combining MRI T2-weighted radiomics with clinical variables enables highly accurate early prediction of SAP in HTG-AP. This noninvasive approach holds promise for facilitating early risk stratification and supporting personalized treatment strategies in clinical practice.