MRI-based machine-learning radiomics of the liver to predict liver-related events in hepatitis B virus-associated fibrosis.
Authors
Affiliations (9)
Affiliations (9)
- Hepatobiliary and Pancreatic Tumor Diagnosis and Treatment Center, Yuebei People's Hospital, Shaoguan, China.
- Zhuhai Interventional Medical Center, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China.
- Department of Pain Management, Shaoguan Zhengtong Hospital, Shaoguan, China.
- Infection and Hepatology Department, Zhuhai Clinical Medical College of Jinan University (Zhuhai People's Hospital), Zhuhai, China.
- Department of Ultrasound Medicine, Zhanjiang Central People's Hospital, Zhanjiang, China.
- School of Medicine, Sun Yat-sen University, Shenzhen, China.
- Department of Radiology, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China.
- Hepatobiliary and Pancreatic Tumor Diagnosis and Treatment Center, Yuebei People's Hospital, Shaoguan, China. [email protected].
- Zhuhai Interventional Medical Center, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China. [email protected].
Abstract
The onset of liver-related events (LREs) in fibrosis indicates a poor prognosis and worsens patients' quality of life, making the prediction and early detection of LREs crucial. The aim of this study was to develop a radiomics model using liver magnetic resonance imaging (MRI) to predict LRE risk in patients undergoing antiviral treatment for chronic fibrosis caused by hepatitis B virus (HBV). Patients with HBV-associated liver fibrosis and liver stiffness measurements ≥ 10 kPa were included. Feature selection and dimensionality reduction techniques identified discriminative features from three MRI sequences. Radiomics models were built using eight machine learning techniques and evaluated for performance. Shapley additive explanation and permutation importance techniques were applied to interpret the model output. A total of 222 patients aged 49 ± 10 years (mean ± standard deviation), 175 males, were evaluated, with 41 experiencing LREs. The radiomics model, incorporating 58 selected features, outperformed traditional clinical tools in prediction accuracy. Developed using a support vector machine classifier, the model achieved optimal areas under the receiver operating characteristic curves of 0.94 and 0.93 in the training and test sets, respectively, demonstrating good calibration. Machine learning techniques effectively predicted LREs in patients with fibrosis and HBV, offering comparable accuracy across algorithms and supporting personalized care decisions for HBV-related liver disease. Radiomics models based on liver multisequence MRI can improve risk prediction and management of patients with HBV-associated chronic fibrosis. In addition, it offers valuable prognostic insights and aids in making informed clinical decisions. Liver-related events (LREs) are associated with poor prognosis in chronic fibrosis. Radiomics models could predict LREs in patients with hepatitis B-associated chronic fibrosis. Radiomics contributes to personalized care choices for patients with hepatitis B-associated fibrosis.