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Updated: Aug 15, 2025
Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
Published on: July 21, 2023
Development of an MRI-Based Radiomics-Clinical Model to Diagnose Liver Fibrosis Secondary to Pancreaticobiliary
Yang Yang1, Xinxian Zhang2, Lian Zhao1
1Department of Radiology, Children's Hospital of Soochow University, Suzhou, China.
Insights
This study developed a noninvasive MR-based radiomics-clinical nomogram to accurately detect liver fibrosis in children with pancreaticobiliary maljunction (PBM). The combined model demonstrated excellent performance across training and validation sets, improving preoperative diagnosis.
Area of Science:
- Medical Imaging
- Radiology
- Pediatric Gastroenterology
Background:
- Accurate preoperative diagnosis of liver fibrosis in children with pancreaticobiliary maljunction (PBM) is crucial for guiding treatment and improving outcomes.
- Current diagnostic methods may be invasive or lack sufficient accuracy for this pediatric population.
Purpose of the Study:
- To develop and validate a magnetic resonance (MR)-based radiomics-clinical nomogram for the noninvasive identification of liver fibrosis in pediatric patients with PBM.
- To enhance the accuracy of preoperative diagnosis and risk stratification for liver fibrosis in this cohort.
Main Methods:
- A retrospective study included 136 pediatric patients with PBM from two centers.
- Radiomics features were extracted from liver parenchyma MR images, and feature selection was performed using Maximum Relevance Minimum Redundancy and Least Absolute Shrinkage and Selection Operator.
- A multivariate logistic regression model was constructed incorporating selected radiomics features and clinical factors, visualized as a nomogram.
Main Results:
- The combined radiomics-clinical nomogram achieved high diagnostic performance, with AUCs of 0.977 (training), 0.921 (internal validation), and 0.878 (external validation).
- The nomogram demonstrated good calibration, indicating reliable predictions.
- Four radiomics features and two clinical factors were identified as significant predictors of liver fibrosis.
Conclusions:
- The developed MR-based radiomics-clinical nomogram serves as a noninvasive, accurate, and effective preoperative tool for diagnosing liver fibrosis in children with PBM.
- This approach has the potential to significantly improve clinical decision-making and patient management.
Background:
Preoperative diagnosis of liver fibrosis in children with pancreaticobiliary maljunction (PBM) is needed to guide clinical decision-making and improve patient prognosis.
Purpose:
To develop and validate an MR-based radiomics-clinical nomogram for identifying liver fibrosis in children with PBM.
Study Type:
Retrospective.
Population:
A total of 136 patients with PBM from two centers (center A: 111 patients; center B: 25 patients). Cases from center A were randomly divided into training (74 patients) and internal validation (37 patients) sets. Cases from center B were assigned to the external validation set. Liver fibrosis was determined by histopathological examination.
Field Strength/Sequence:
A 3.0 T (two vendors)/T1-weighted imaging and T2-weighted imaging.
Assessment:
Clinical factors associated with liver fibrosis were evaluated. A total of 3562 radiomics features were extracted from segmented liver parenchyma. Maximum relevance minimum redundancy and least absolute shrinkage and selection operator were recruited to screen radiomics features. Based on the selected variables, multivariate logistic regression was used to construct the clinical model, radiomics model, and combined model. The combined model was visualized as a nomogram to show the impact of the radiomics signature and key clinical factors on the individual risk of developing liver fibrosis.
Statistical Tests:
Mann-Whitney U and chi-squared tests were used to compare clinical factors. P < 0.05 was considered statistically significant in the final models.
Results:
Two clinical factors and four radiomics features were selected as they were associated with liver fibrosis in the training (AUC, 0.723, 0.927), internal validation (AUC, 0.718, 0.885), and external validation (AUC, 0.737, 0.865) sets. The radiomics-clinical nomogram yielded the best performance in the training (AUC, 0.977), internal validation (AUC, 0.921), and external validation (AUC, 0.878) sets, with good calibration (P > 0.05).
Data Conclusion:
Our radiomic-based nomogram is a noninvasive, accurate, and preoperative diagnostic tool that is able to detect liver fibrosis in PBM children.
Evidence Level:
3.
Technical Efficacy:
Stage 2.
