Objective To summarize advances in the application of machine learning in the diagnosis and treatment of liver disease. Method The recent literatures on the progress of machine learning in the diagnosis, treatment and prognosis of liver diseases were reviewed. Results Machine learning could be used to diagnose and categorize substantial liver lesions, tumourous lesions and rare liver diseases at an early stage, which could facilitate clinicians to take timely and appropriate treatment measures. Machine learning was helpful in informing clinicians in choosing the best treatment decision, which was conducive to reducing medical risks. It could also help to determine the prognosis of patients in a comprehensive manner, and provide assistance in formulating early rehabilitation treatment plans, adjusting follow-up strategies and improving future prognosis. Conclusions Multiple types of machine learning algorithms have achieved positive results in the clinical application of liver diseases by constructing different prediction models, and have great potential and excellent prospects in multiple aspects such as diagnosis, treatment and prognosis of liver diseases.
This article briefly introduces the multifactorial and multistep pathogenesis of hepatocellular carcinoma (HCC), as well as the synergistic mechanisms underlying targeted therapy and immunotherapy. It systematically reviews advances in drug therapy for intermediate to advanced HCC, covering the efficacy and limitations of targeted agents (e.g., multi-kinase inhibitors) and immune checkpoint inhibitor monotherapies, and highlights how combination strategies (such as the integration of targeted and immunotherapeutic drugs and dual immunotherapy) have evolved into new first-line treatment standards. Meanwhile, the article summarizes the potential applications and recent progress of targeted and immunotherapeutic drugs in perioperative adjuvant therapy. Furthermore, it outlines the spectrum of treatment-related adverse events, particularly immune-related adverse events and common toxicities associated with targeted agents, along with summarized principles for risk management. It points out that future efforts should focus on exploring biomarkers to precisely identify patient populations likely to benefit, optimizing treatment sequences and combination regimens, and strengthening comprehensive safety management throughout the entire treatment process, all while maintaining focus on enhancing therapeutic efficacy.
ObjectiveTo explore the clinical protocols of neoadjuvant therapy for hepatocellular carcinoma and to provide a perspective on its future prospects. MethodLiterature search and review were conducted in CNKI, Wanfang, VIP, PubMed and other databases using keywords such as “hepatocellular carcinoma”, “neoadjuvant therapy”, “interventional therapy”, “radiotherapy”, “targeted therapy”, “immunotherapy”, etc in recent five years. ResultsNeoadjuvant therapy for hepatocellular carcinoma included neoadjuvant interventional therapy, radiotherapy, targeted therapy, and immunotherapy. Neoadjuvant interventional therapy and radiotherapy had significant advantages for hepatocellular carcinoma patients with portal vein tumor thrombus, while neoadjuvant targeted therapy and immunotherapy had achieved initial results in tumor pathological remission rate, providing more ideas for the diagnosis and treatment of patients with resectable hepatocellular carcinoma. ConclusionsNeoadjuvant therapy is an emerging treatment for hepatocellular carcinoma, which has shown great potential in clinical applications and is moving towards individualization, precision, and systematization. We believe that with in-depth research on the mechanism of immunotherapy for hepatocellular carcinoma and continuous clinical practice, a comprehensive treatment strategy based on immunotherapy will become the key to neoadjuvant therapy for hepatocellular carcinoma in the future.
