Hepatocellular carcinoma is a major malignant tumor with high incidence and mortality rates in China. Traditional single-modality imaging is limited by information dimensions and subjective variations in early diagnosis and recurrence prediction. In recent years, intelligent analysis strategies integrating multi-modality imaging such as CT, MRI, CEUS, and PET, combined with radiomics and deep learning technologies, have significantly improved lesion detection rates, pathological classification ...更多
Hepatocellular carcinoma is a major malignant tumor with high incidence and mortality rates in China. Traditional single-modality imaging is limited by information dimensions and subjective variations in early diagnosis and recurrence prediction. In recent years, intelligent analysis strategies integrating multi-modality imaging such as CT, MRI, CEUS, and PET, combined with radiomics and deep learning technologies, have significantly improved lesion detection rates, pathological classification accuracy, and preoperative risk stratification. Multimodal models, achieving complementary information across data, feature, and decision layers, have been widely applied in predicting microvascular invasion, evaluating immunotherapy response, and personalized prognosis management. Despite ongoing challenges in multicenter heterogeneity, privacy protection, and interpretability, multimodal frameworks based on federated learning and self-supervised pretraining are laying the foundation for establishing a practical intelligent decision support system for HCC. Moving forward, standardized data collection, clinical validation, and deep integration with guideline workflows will be critical pathways for translating multimodal imaging into clinical practice.收起
发文期刊《Recent Advances in Multimodal Medical Imaging Fusion Strategies for Precision Diagnosis and Prognostic Prediction of Hepatocellular Carcinoma》历年引证文献趋势图
