Narrative review of management of cirrhosis during the treatment of hepatocellular carcinoma.

BACKGROUND AND OBJECTIVE: The management of hepatocellular carcinoma (HCC) and cirrhosis are closely linked. HCC most often occurs in the background of cirrhosis and can also lead to decompensation of underlying liver disease. The treatment of complications of cirrhosis is important to help reduce morbidity and mortality and allow for expanded treatment options of HCC. METHODS: We searched PubMed using search terms for cirrhosis and HCC. From this search, we selected references which appeared to be primary studies preferentially within the last 5 years, although also included select landmark studies which have shaped guidelines and recommendations. KEY CONTENT AND FINDINGS: The development of HCC and treatment of HCC can both cause decompensation of liver disease and worsening of liver function. For most patients, the development of HCC or progression of disease are the drivers of morbidity and mortality. However, it is important to closely monitor patients for complications of liver disease that develop either as a result of HCC or as a complication of HCC treatment, and this can have important implications on treatment options. Multidisciplinary team involvement including hepatologists, surgeons, radiologists, interventional radiologists, medical oncologists, and palliative care is essential in the care of patients with cirrhosis and HCC to help guide management decisions and treatment. CONCLUSIONS: The management of cirrhosis and HCC are both complex and interrelated. Through a multidisciplinary team approach we can best treat the complications of cirrhosis, allow for expanded treatment options, and improve quality of life through symptom management.

A machine learning clinic scoring system for hepatocellular carcinoma based on the Surveillance, Epidemiology, and End Results database.

Background: Hepatocellular carcinoma (HCC) poses a global threat to life; however, numerical tools to predict the clinical prognosis of these patients remain scarce. The primary objective of this study is to establish a clinical scoring system for evaluating the overall survival (OS) rate and cancer-specific survival (CSS) rate in HCC patients. Methods: From the Surveillance, Epidemiology, and End Results (SEER) Program, we identified 45,827 primary HCC patients. These cases were randomly allocated to a training cohort (22,914 patients) and a validation cohort (22,913 patients). Univariate and multivariate Cox regression analyses, coupled with Kaplan-Meier methods, were employed to evaluate prognosis-related clinical and demographic features. Factors demonstrating prognostic significance were used to construct the model. The model's stability and accuracy were assessed through C-index, receiver operating characteristic (ROC) curves, calibration curves, and clinical decision curve analysis (DCA), while comparisons were made with the American Joint Committee on Cancer (AJCC) staging. Ultimately, machine learning (ML) quantified the variables in the model to establish a clinical scoring system. Results: Univariate and multivariate Cox regression analyses identified 11 demographic and clinical-pathological features as independent prognostic indicators for both CSS and OS using. Two models, each incorporating the 11 features, were developed, both of which demonstrated significant prognostic relevance. The C-index for predicting CSS and OS surpassed that of the AJCC staging system. The area under the curve (AUC) in time-dependent ROC consistently exceeded 0.74 in both the training and validation sets. Furthermore, internal and external calibration plots indicated that the model predictions aligned closely with observed outcomes. Additionally, DCA demonstrated the superiority of the model over the AJCC staging system, yielding greater clinical net benefit. Ultimately, the quantified clinical scoring system could efficiently discriminate between high and low-risk patients. Conclusions: A ML clinical scoring system trained on a large-scale dataset exhibits good predictive and risk stratification performance in the cohorts. Such a clinical scoring system is readily integrable into clinical practice and will be valuable in enhancing the accuracy and efficiency of HCC management.