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Advanced hepatocellular carcinoma (HCC) is a severe malignancy that poses a serious threat to human health. Owing to challenges in early diagnosis, most patients lose the opportunity for radical treatment when diagnosed. Nonetheless, recent advancements in cancer immunotherapy provide new directions for the treatment of HCC. For instance, monoclonal antibodies against immune checkpoint inhibitors (ICIs) such as programmed cell death protein 1/death ligand-1 inhibitors and cytotoxic t-lymphocyte associated antigen-4 significantly improved the prognosis of patients with HCC. However, tumor cells can evade the immune system through various mechanisms. With the rapid development of genetic engineering and molecular biology, various new immunotherapies have been used to treat HCC, including ICIs, chimeric antigen receptor T cells, engineered cytokines, and certain cancer vaccines. This review summarizes the current status, research progress, and future directions of different immunotherapy strategies in the treatment of HCC.
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BACKGROUND: Gastric cancer is one of the most common malignant tumors in the digestive system, ranking sixth in incidence and fourth in mortality worldwide. Since 42.5% of metastatic lymph nodes in gastric cancer belong to nodule type and peripheral type, the application of imaging diagnosis is restricted. AIM: To establish models for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning (ML) algorithms and to evaluate their predictive performance in clinical practice. METHODS: Data of a total of 369 patients who underwent radical gastrectomy at the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) from March 2016 to November 2019 were collected and retrospectively analyzed as the training group. In addition, data of 123 patients who underwent radical gastrectomy at the Department of General Surgery of Jining First People's Hospital (Jining, China) were collected and analyzed as the verification group. Seven ML models, including decision tree, random forest, support vector machine (SVM), gradient boosting machine, naive Bayes, neural network, and logistic regression, were developed to evaluate the occurrence of lymph node metastasis in patients with gastric cancer. The ML models were established following ten cross-validation iterations using the training dataset, and subsequently, each model was assessed using the test dataset. The models' performance was evaluated by comparing the area under the receiver operating characteristic curve of each model. RESULTS: Among the seven ML models, except for SVM, the other ones exhibited higher accuracy and reliability, and the influences of various risk factors on the models are intuitive. CONCLUSION: The ML models developed exhibit strong predictive capabilities for lymph node metastasis in gastric cancer, which can aid in personalized clinical diagnosis and treatment.
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BACKGROUND: Although the combination of lenvatinib and PD-1 inhibitors has become the standard regimen for the treatment of advanced hepatocellular carcinoma (HCC), real data on the impact of baseline hepatitis B virus (HBV)-DNA levels on the clinical efficacy of this regimen is still limited. AIM: To evaluate the effectiveness of camrelizumab combined with lenvatinib in patients with HCC at varying levels of HBV-DNA. METHODS: One hundred and twenty patients with HCC who received camrelizumab and lenvatinib treatment were categorized into two cohorts: HBV-DNA ≤ 2000 (n = 66) and HBV-DNA > 2000 (n = 54). The main outcomes measured were overall survival (OS) and progression-free survival (PFS), while additional outcomes included the rate of objective response rate (ORR), disease control rate (DCR), and any negative events. Cox proportional hazards regression analysis revealed independent predictors of OS, leading to the creation of a nomogram incorporating these variables. RESULTS: The median PFS was 8.32 months for the HBV-DNA ≤ 2000 group, which was similar to the 7.80 months observed for the HBV DNA > 2000 group (P = 0.88). Likewise, there was no notable variation in the median OS between the two groups, with durations of 13.30 and 14.20 months respectively (P = 0.14). The ORR and DCR were compared between the two groups, showing ORR of 19.70% vs 33.33% (P = 0.09) and DCR of 72.73% vs 74.07% (P = 0.87). The nomogram emphasized the importance of antiviral treatment as the main predictor of patient results, with portal vein tumor thrombus and Barcelona Clinic Liver Cancer staging following closely behind. CONCLUSION: The clinical outcomes of patients with HBV-associated HCC treated with camrelizumab in combination with lenvatinib are not significantly affected by HBV viral load.