Background Resection of hepatocellular carcinoma (HCC) originating in the caudate lobe remains challenging, while the optimal extent of resection is debated. We aimed to evaluate the relative benefits of combined caudate lobectomy (CCL) versus isolated caudate lobectomy (ICL) for caudate HCC. Methods Patients who underwent curative-intent resection for caudate HCC between January 2010 and December 2018 were identified from a single-center database. Surgical outcomes of the two strategy groups were analyzed before and after propensity score matching. A systematic review with meta-analysis was also performed to compare outcomes of CCL versus ICL for caudate HCC. Results A total of 28 patients were included: 11 in the CCL and 17 in the ICL group. Compared with ICL, the CCL group contained patients with larger tumors and a higher incidence of vascular invasion. After propensity score matching, 6 pairs of patients were selected. In the well-matched cohort, CCL demonstrated significantly improved recurrence-free survival (RFS) ( P = 0.047) compared with ICL; no significant differences were noted for overall survival (OS), operation time, blood loss and morbidity rate. A total of 227 patients from nine eligible studies and ours were involved in the systematic review. Meta-analysis revealed that CCL provided better RFS (hazard ratio 0.54, 95% confidence interval 0.31–0.92) than ICL; no significant differences were observed in OS, operation time, blood loss and morbidity rate. Conclusion CCL confers superior RFS over ICL without compromise of perioperative outcomes and should be prioritized for patients with caudate HCC when feasible, especially for those with large-sized tumors.
Background Accurate prognosis assessment is essential for surgically resected intrahepatic cholangiocarcinoma (ICC) while published prognostic tools are limited by modest performance. We therefore aimed to establish a novel model to predict survival in resected ICC based on readily-available clinical parameters using machine learning technique. Methods A gradient boosting machine (GBM) was trained and validated to predict the likelihood of cancer-specific survival (CSS) on data from a Chinese hospital-based database using nested cross-validation, and then tested on the Surveillance, Epidemiology, and End Results (SEER) database. The performance of GBM model was compared with that of proposed prognostic score and staging system. Results A total of 1050 ICC patients (401 from China and 649 from SEER) treated with resection were included. Seven covariates were identified and entered into the GBM model: age, tumor size, tumor number, vascular invasion, number of regional lymph node metastasis, histological grade, and type of surgery. The GBM model predicted CSS with C-Statistics ≥ 0.72 and outperformed proposed prognostic score or system across study cohorts, even in sub-cohort with missing data. Calibration plots of predicted probabilities against observed survival rates indicated excellent concordance. Decision curve analysis demonstrated that the model had high clinical utility. The GBM model was able to stratify 5-year CSS ranging from over 54% in low-risk subset to 0% in high-risk subset. Conclusions We trained and validated a GBM model that allows a more accurate estimation of patient survival after resection compared with other prognostic indices. Such a model is readily integrated into a decision-support electronic health record system, and may improve therapeutic strategies for patients with resected ICC.
Background: Gallbladder cancer (GBC) is highly malignant, its early diagnosis is difficult, and the 5-year survival rate is less than 5%. For patients with advanced GBC (GBCa), combined chemotherapy, radiotherapy, targeted therapy, and immunotherapy are needed to improve the overall survival (OS) rate of patients. Methods: Data were collected from 53 patients with GBCa who had volunteered to receive programmed death protein-1 (PD-1)-based treatment at the First Affiliated Hospital of Nanjing Medical University from February 2018 to February 2021. Statistical analysis of the collected data, including Kaplan-Meier method, log-rank test, Cox proportional hazard regression model and other methods.Results: The objective response rates (ORRs) and disease control rates (DCRs) of 53 participants 3 months after receiving immunotherapy were 30.2% and 79.2%, respectively. The ORRs and DCRs of the combined treatment group were higher than those of the camrelizumab group (CG) (P<0.05). The DCRs of the camrelizumab plus apatinib group (CAG) at 3 and 6 months were 90.9% and 45.5% (P=0.003), respectively, while the DCRs at 3 and 6 months of the camrelizumab plus chemotherapy group (CCG) were 85.7% and 71.4% (P=0.450), respectively. After treatment, there were statistically significant differences before and after CA199 for each group (P<0.05). The median progression-free survival (mPFS) of the 53 participants was 7 months, and the median overall survival (mOS) was 12 months. The mPFS and mOS of the CAG and the CCG were greater than those in the CG (6 vs. 3 months, P<0.001, 12 vs. 8 months, P=0.019; 9 vs. 3 months, P<0.001, 13 vs. 8 months, P<0.001, respectively). A total of 16 cases had grade 1 or 2 adverse events, and 3 cases had grade 3 and higher adverse events.Conclusions: For GBCa patients, PD-1 combined with targeted therapy or chemotherapy is more effective than immunotherapy alone. The targeted therapy group has more obvious early effects on the disease control rate, and combined chemotherapy can achieve sustained effects, providing new ideas for the future GBCa application of immune, targeted, and chemotherapy sequential therapy.
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