Most immune-related adverse event (irAE) associated with immune checkpoint inhibitors (ICIs) resulted from excessive immune response against normal organs. The severity, timing, and organs affected by these events were often unpredictable. Adverse reactions could cause treatment delays or interruptions, in rare cases, pose a life-threatening risk. The mechanisms underlying irAE involved immune cell dysregulation, imbalances in inflammatory factor expression, alterations in autoantibodies and complement activation, even dysbiosis of intestinal microorganisms. However, the mechanisms of irAE occurrence might differ slightly among organs due to variations in their structures and the functions of resident immune cells. Future research should focus on the development of targeted drugs for the prevention or treatment of irAE based on the mechanisms by which irAE occurs in different organs. A deeper understanding of the mechanisms underlying irAE occurrence would aid clinicians in effectively utilizing ICIs and provide valuable guidance for their clinical application.
ObjectiveTo better understand artificial intelligence (AI) and its application in management of liver cancer.MethodThe relevant literatures about AI in the diagnosis and treatment of liver cancer in recent years were reviewed.ResultsIn terms of diagnosis, the deep learning could precisely and quickly complete the imaging localization and segmentation of the liver, which was helpful for the diagnosis, while radiomics had a high value in assisting the diagnosis of liver cancer, predicting the postoperative recurrence and long-term survival of patients with liver cancer. In regard of treatment, although it was still difficult for AI to intervene in liver surgery, it had significant advantages in formulating individualized operation scheme for patients with liver cancer, which enabled precise hepatectomy and was helpful for prediction of intraoperative bleeding. AI fusion imaging could provide assistance in operation plan making and realize the precise placement of ablation needle. AI was able to predict the tumor response or even tumor progression after interventional therapy and radiotherapy. Pathological analysis was also facilitated by AI and was able to identify some details and feature textures that were difficult to manually distinguish. For transplantation, guidance of AI on the allocation of donor livers based on hazards models helped make better use of limited organ resources. AI could be applied in prognosis prediction in almost all treatment modalities.ConclusionsAI provides more efficient and precise diagnosis, treatment support and prognosis than conventional medical process in liver cancer, generally by constructing a fully functional model based on a series of data mining methods combined with statistical analysis.
ObjectiveTo exclusively compare the short-and long-term outcomes of hepatic resection (HR) patients with multifocal tumors meeting the Milan criteria between locating in same and different sections. MethodsA total of 219 consecutive HR patients with multifocal tumors meeting the Milan criteria were divided into group SS (n=97, same section) and group DS (n=122, different sections) according to their anatomical location (Couinaud's segmentation). ResultsThe 1-, 3-, and 5-year overall survival (OS) and recurrence-free survival (RFS) rates were significantly higher in the group SS than those in the group DS (P < 0.05). The subgroup analysis showed that patients with 2 tumors and those undergoing en bloc resection were associated with better OS and RFS (P < 0.05). ConclusionsFor HCC patients with multifocal tumors meeting the Milan criteria, those with tumors locating in same hepatic section may have better longterm survival and lower HCC recurrence rates than those locating in different sections after HR.