Construction of web-based prediction nomogram models for cancer-specific survival in patients at stage IV of hepatocellular carcinoma depending on SEER database. / 基于SEER数据库构建IVæœŸè‚ç»†èƒžç™Œæ‚£è€ ç™Œç—‡ç‰¹å¼‚æ€§ç”Ÿå­˜æœŸçš„ç½‘ç»œé¢„æµ‹åˆ—çº¿å›¾æ¨¡åž‹ï¼ˆè‹±æ–‡ï¼‰.

OBJECTIVES: Hepatocellular carcinoma (HCC) prognosis involves multiple clinical factors. Although nomogram models targeting various clinical factors have been reported in early and locally advanced HCC, there are currently few studies on complete and effective prognostic nomogram models for stage IV HCC patients. This study aims to creat nomograms for cancer-specific survival (CSS) in patients at stage IV of HCC and developing a web predictive nomogram model to predict patient prognosis and guide individualized treatment. METHODS: Clinicopathological information on stage IV of HCC between January, 2010 and December, 2015 was collected from the Surveillance, Epidemiology, and End Results (SEER) database. The patients at stage IV of HCC were categorized into IVA (without distant metastases) and IVB (with distant metastases) subgroups based on the presence of distant metastasis, and then the patients from both IVA and IVB subgroups were randomly divided into the training and validation cohorts in a 7꞉3 ratio. Univariate and multivariate Cox regression analyses were used to analyze the independent risk factors that significantly affected CSS in the training cohort, and constructed nomogram models separately for stage IVA and stage IVB patients based on relevant independent risk factors. Two nomogram's accuracy and discrimination were evaluated by receiver operator characteristic (ROC) curves and calibration curves. Furthermore, web-based nomogram models were developed specifically for stage IVA and stage IVB HCC patients by R software. A decision analysis curve (DCA) was used to evaluate the clinical utility of the web-based nomogram models. RESULTS: A total of 3 060 patients were included in this study, of which 883 were in stage IVA, and 2 177 were in stage IVB. Based on multivariate analysis results, tumor size, alpha-fetoprotein (AFP), T stage, histological grade, surgery, radiotherapy, and chemotherapy were independent prognostic factors for patients with stage IVA of HCC; and tumor size, AFP, T stage, N stage, histological grade, lung metastasis, surgery, radiotherapy, and chemotherapy were independent prognostic factors for patients with stage IVB HCC. In stage IVA patients, the 3-, 6-, 9-, 12-, 15-, and 18-month areas under the ROC curves for the training cohort were 0.823, 0.800, 0.772, 0.784, 0.784, and 0.786, respectively; and the 3-, 6-, 9-, 12-, 15-, and 18-month areas under the ROC curves for the validation cohort were 0.793, 0.764, 0.739, 0.773, 0.798, and 0.799, respectively. In stage IVB patients, the 3-, 6-, 9-, and 12-month areas under the ROC curves for the training cohort were 0.756, 0.750, 0.755, and 0.743, respectively; and the 3-, 6-, 9-, and 12-month areas under the ROC curves for the validation cohort were 0.744, 0.747, 0.775, and 0.779, respectively; showing that the nomograms had an excellent predictive ability. The calibration curves showed a good consistency between the predictions and actual observations. CONCLUSIONS: Predictive nomogram models for CSS in stage IVA and IVB HCC patients are developed and validated based on the SEER database, which might be used for clinicians to predict the prognosis, implement individualized treatment, and follow up those patients.

A web-based nomogram model for predicting the overall survival of hepatocellular carcinoma patients with external beam radiation therapy: A population study based on SEER database and a Chinese cohort.

Background: External beam radiation therapy (EBRT) for hepatocellular carcinoma (HCC) is rarely used in clinical practice. This study aims to develop and validate a prognostic nomogram model to predict overall survival (OS) in HCC patients treated with EBRT. Method: We extracted eligible data of HCC patients between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Those patients were randomly divided into a training cohort (n=1004) and an internal validation cohort (n=429), and an external validation cohort composed of a Chinese cohort (n=95). A nomogram was established based on the independent prognostic variables identified from univariate and multivariate Cox regression analyses. The effective performance of the nomogram was evaluated using the concordance index (C-index), receiver operating characteristic curve (ROC), and calibration curves. The clinical practicability was evaluated using decision curve analysis (DCA). Results: T stage, N stage, M stage, AFP, tumor size, surgery, and chemotherapy were independent prognostic risk factors that were all included in the nomogram to predict OS in HCC patients with EBRT. In the training cohort, internal validation cohort, and external validation cohort, the C-index of the prediction model was 0.728 (95% confidence interval (CI): 0.716-0.740), 0.725 (95% CI:0.701-0.750), and 0.696 (95% CI:0.629-0.763), respectively. The 6-, 12-,18- and 24- month areas under the curves (AUC) of ROC in the training cohort were 0.835 、0.823 、0.810, and 0.801, respectively; and 0.821 、0.809 、0.813 and 0.804 in the internal validation cohort, respectively; and 0.749 、0.754 、0.791 and 0.798 in the external validation cohort, respectively. The calibration curves indicated that the predicted value of the prediction model performed well. The DCA curves showed better clinical practicability. In addition, based on the nomogram, we established a web-based nomogram to predict the OS of these patients visually. Conclusion: Based on the SEER database and an independent external cohort from China, we established and validated a nomogram to predict OS in HCC patients treated with EBRT. In addition, for the first time, a web-based nomogram model can help clinicians judge the prognoses of these patients and make better clinical decisions.

A real-world study of anlotinib combined with GS regimen as first-line treatment for advanced pancreatic cancer.

Background: Anlotinib may boost the efficacy of pancreatic cancer (PC) treatment if timely added to the GS regimen (Gemcitabine, Tegafur-gimeracil-oteracil potassium); however, no data has been published. This study evaluated the safety and efficacy of anlotinib in combination with the GS regimen(hereafter referred to as the A+GS regimen) in the first-line treatment of patients with unresectable or metastatic PC. Methods: Patients with unresectable or metastatic PC treated at Yueyang Central Hospital and Yueyang People's Hospital between October 2018 and June 2022 were enrolled in this retrospective real-world investigation. Treatment efficacy was evaluated based on the overall survival (OS), progression-free survival (PFS), disease control rate (DCR), and objective response rate (ORR), while the treatment safety was assessed by the frequency of major adverse events (AEs). Results: Seventy-one patients were included in this study, 41 in the GS group and 30 in the A+GS group. The A+GS group had a longer mPFS than the GS group (12.0 months (95% CI, 6.0-18.0) and 6.0 months (95% CI, 3.0-8.1)), respectively (P = 0.005). mOS was longer in the GS+A group) when compared with the GS group (17.0 months (95%CI, 14.0-20.0) and 10.0 months (95% CI, 7.5-12.5)), respectively (P = 0.018). The GS+A group had higher ORR (50.0% vs 26.8%, P = 0.045) and DCR (83.3% vs 58.5%, P = 0.026). Furthermore, there were no grade 4-5 AEs and no treatment-related deaths, and no discernible increase in AEs in the GS+A group when compared with the GS group. Conclusion: The A+GS regimen therapy holds great promise in managing treatment-naive advanced PC, except that future prospective studies with larger sample sizes and multiple centers are required to determine its efficacy and safety.