Research Progress on the Application of Radiomics and Deep Learning in Liver Fibrosis

TL;DR

This review explores how radiomics and deep learning can help diagnose and monitor liver fibrosis non-invasively, offering new possibilities for precision medicine.

Contribution

The paper provides a comprehensive review of recent advancements in using radiomics and deep learning for liver fibrosis diagnosis and management.

Findings

Radiomics and deep learning show significant potential as non-invasive tools for liver fibrosis diagnosis.

Multimodal imaging techniques like MRI, CT, and ultrasound are valuable for integrating with AI methods.

Challenges remain in model generalization and interpretability, but progress is promising for precision medicine.

Abstract

Liver fibrosis (LF) represents a crucial intermediate stage in the pathological progression from chronic liver disease to cirrhosis and hepatocellular carcinoma. Early and accurate diagnosis is of vital importance for the intervention treatment of diseases and the improvement of prognosis. Traditional liver biopsy, long regarded as the diagnostic gold standard, remains associated with several notable limitations such as invasiveness, sampling errors and inter-observer variability. Lately, as artificial intelligence (AI) technology progresses swiftly, radiomics and deep learning (DL) have risen to prominence as non-invasive diagnostic instruments, showing significant potential in the LF diagnostic evaluation. This review summarizes the latest advancements in radiomics and DL for LF diagnosis, staging, prognosis prediction and etiological differentiation. It also analyzes the application value of multimodal imaging modalities, including magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound in this field. Despite ongoing challenges in model generalization and standardization, improved model interpretability, technological integration and multimodal fusion, the continuous advancement of radiomics and DL technologies holds promise for AI-driven imaging analysis strategies. These approaches aim to integrate multiple clinical monitoring methods, overcome obstacles in the early LF diagnosis and treatment and provide new perspectives for precision medicine of this disease.

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