Deep Learning for Liver Disease and Hepatocellular Carcinoma: A Comprehensive Review of Segmentation, Diagnosis, Microvascular Invasion Prediction, and Survival Modelling

Liver disease is a major worldwide health issue as hepatocellular carcinoma (HCC) which is the most common form of primary liver cancer which frequently occurs in the livers that are impaired by severe illnesses like cirrhosis, fibrosis, and persistent inflammation. As a result of this combination of imaging methods such as ultrasound, CT and MRI together with tissue based studies such as histology and the whole slide imaging would be required to make a precise diagnosis and determine microvascular invasion and also prognosis. By providing systematic segmentation and lesion description, MVI prediction, and survival modelling, recent developments in deep learning have greatly enhanced performance in each area. This is a thorough analysis regarding the deep learning techniques used in HCC and liver disease. Such methods are transformer models, weakly supervised WSI frameworks, radiomics DL hybrids, classical convolutional neural networks, and multimodal fusion plans, which integrate clinical, pathology, radiology, and genomic data. Based on large clinical datasets, we identify tremendous improvements in tumour segmentation, tumour detection and classification, preoperative and postoperative MVI assessment, and recurrence or survival prediction. Some of the current challenges include limited diversity of datasets, unbalanced external validation, no annotations, domain change among scanners and universities, interpretability concerns, and barriers to workflow integration. On the whole, the findings indicate that deep learning can revolutionize liver disease and HCC treatment; however, the most important step is to create AI systems that are clinically applicable, generalisable, and transparent.