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Nomogram for individualized prediction of recurrence after postoperative adjuvant TACE for hepatitis B virus-related hepatocellular carcinoma.

ABSTRACT: This study sought to develop an effective and reliable nomogram for predictions of recurrence for postoperative adjuvant transarterial chemoembolization (PA-TACE) in patients with hepatitis B virus-related (HBV) hepatocellular carcinoma (HCC).The nomogram was established based on data obtained from a retrospective study on 235 consecutive patients with HBV HCC who received PA-TACE as an initial therapy from 2006 to 2010 in our center. Eighty-four patients who were collected at another institution between 01/2008 and 12/2010 served as an external validation set. Recurrence-free survival (RFS) was collected. The nomogram for tumor recurrence was developed based on the data obtained before the PA-TACE procedure. Predictive accuracy and discriminative ability of the nomogram were assessed by concordance index (C-index), calibration curves, and validation set.The 1, 2, 3-year RFS rates were 55.5%, 27.0%, and 14.1%, respectively, in the patients from the derivation set and 60.7%, 33.2%, and 23.8% in those from the validation set. Four risk factors (HBV-DNA level, vascular invasion, change of Child-Pugh score, and tumor diameter) in the multivariate analysis were significantly associated with RFS. The statistical nomogram incorporated these 4 factors achieved good calibration and discriminatory abilities with the c-index of 0.74 (95% CI 0.66-0.82). The findings were supported by the independent external validation set (c-index, 0.70; 95% CI 0.58-0.83). The area under the receiver operating characteristic curve in our model was greater than those of conventional staging systems in the validation patients (corresponding c-indices, 0.56-0.64).The novel nomogram may achieve an optimal prediction for recurrence outcome in HBV-related HCC with PA-TACE.

SUBMITTER: Jiang J

PROVIDER: S-EPMC5556198 | biostudies-other | 2017 Aug

REPOSITORIES: biostudies-other

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Project description:Background and Aims: The tumor microenvironment can be divided into inflamed, immune-excluded and immune-desert phenotypes according to CD8+ T cell categories with differential programmed cell death protein 1 (PD-L1) expression. The study aims to construct a novel immunotype-based risk stratification model to predict postsurgical survival and adjuvant trans-arterial chemoembolization (TACE) response in patients with hepatocellular carcinoma (HCC). Methods: A total of 220 eligible HCC patients participated in this study. CD8 + T cell infiltration and PD-L1 expression mode were estimated by immunohistochemical staining. A risk stratification model was developed and virtualized by a nomogram that integrated these independent prognostic factors. The postoperative prognosis and adjuvant TACE benefits were evaluated with a novel immunotype-based risk stratification model. Results: A total of 220 patients were finally identified. Immune-desert, immune-excluded, and inflamed immunotypes represented 45%, 24%, and 31% of HCC, respectively. Univariate and multivariate analyses identified immunotype and PD-L1 expression mode as independent prognostic factors for overall survival time (OS) and recurrence-free survival time (RFS). The nomogram was constructed by integrating immunotype, PD-L1 expression, Barcelona Clinic Liver Cancer (BCLC) stage and tumor grade. The C-index was 0.794 in the training cohort and 0.813 in the validation cohort. A risk stratification system was constructed based on the nomogram classifying HCC patients into 3 risk groups. The average OS times in the low-risk, intermediate-risk and high-risk groups in all cohorts were 77.1 months (95% CI 71.4-82.9), 53.7 months (95% CI 48.2-59.2), and 25.6 months (95% CI 21.4-29.7), respectively. Further analysis showed that OS was significantly improved by adjuvant TACE in the low- and intermediate-risk groups (P=0.041 and P=0.010, respectively) but not in the high-risk group (P=0.398). Conclusion: A novel immunotype-based risk stratification model was built to predict postoperative prognosis and adjuvant TACE benefit in HCC patients. These tools can assist in building a more customized method of HCC treatment.

Project description:BackgroundThis study aimed to investigate the role of the alpha fetoprotein (AFP) ratio before and after curative resection in the prognosis of patients with hepatocellular carcinoma (HCC) and to develop a novel pre- to postoperative AFP ratio nomogram to predict recurrence free survival (RFS) for HCC patients after curative resection.MethodsA total of 485 pathologically confirmed HCC patients who underwent radical hepatectomy from January 2010 to December 2018 were retrospectively analyzed. The independent prognostic factors of hepatocellular carcinoma were identified by multivariate COX proportional model analysis, and the nomogram model was constructed. The receiver operating characteristic and the C-index were used to evaluate the accuracy and efficacy of the model prediction, the correction curve was used to assess the calibration of the prediction model, and decision curve analysis was used to evaluate the clinical application value of the nomogram model.ResultsA total of 485 HCC patients were divided into the training cohort (n = 340) and the validation cohort (n = 145) by random sampling at a ratio of 7:3. Using X-tile software, it was found that the optimal cut-off value of the AFP ratio in the training cohort was 0.8. In both cohorts, the relapse-free survival of patients with an AFP ratio <0.8 (high-risk group) was significantly shorter than in those with an AFP ratio ≥0.8 (low-risk group) (P < 0.05). An AFP ratio <0.8 was an independent risk factor for recurrence of HCC after curative resection. Based on the AFP ratio, BCLC stage and cirrhosis diagnosis, a satisfactory nomogram was developed. The AUC of our nomogram for predicting 1-, 3-, and 5-year RFS was 0.719, 0.690, and 0.708 in the training cohort and 0.721, 0.682, and 0.681 in the validation cohort, respectively. Furthermore, our model demonstrated excellent stratification as well as clinical applicability.ConclusionThe AFP ratio was a reliable biomarker for tumor recurrence. This easy-to-use AFP ratio-based nomogram precisely predicted tumor recurrence in HCC patients after curative resection.

Project description:BackgroundEarly recurrence of hepatocellular carcinoma (HCC) is a major obstacle to improving the prognosis, and no widely accepted adjuvant therapy guideline for patients post-liver resection is available. Currently, all available methods and biomarkers are insufficient to accurately predict post-operation HCC patients' risk of early recurrence and their response to adjuvant therapy.MethodsIn this study, we downloaded four gene expression datasets (GSE14520, GSE54236, GSE87630, and GSE109211) from the Gene Expression Omnibus database and identified 34 common differentially expressed genes associated with HCC dysregulation and response to adjuvant sorafenib. Then, we constructed a novel 11-messenger RNA predictive model by using ROC curves analysis, univariate Cox regression analysis, and LASSO Cox regression analysis. Furthermore, we validated the predictive values of the risk model in GSE14520 and TCGA-LIHC cohorts by using Kaplan-Meier survival analysis, multivariable Cox regression analysis, and decision curve analysis, respectively.ResultsThe risk score model could identify patients with a high risk of HCC recurrence at the early stage and could predict the response of patients to adjuvant sorafenib. Patients with a high risk score had a worse recurrence rate in training cohorts (2-year: p < 0.0001, hazard ratio (HR): 4.658, confidence interval 95% CI [2.895-7.495]; 5-year: p < 0.0001, HR: 3.251, 95% CI [2.155-4.904]) and external validation cohorts (2-year: p < 0.001, HR: 3.65, 95% CI [2.001-6.658]; 5-year: p < 0.001, HR: 3.156, 95% CI [1.78-5.596]). The AUC values of the risk score model for predicting tumor early recurrence were 0.746 and 0.618, and that of the risk score model for predicting the response to adjuvant sorafenib were 0.722 and 0.708 in the different cohort, respectively. Multivariable Cox regression analysis and decision curve analysis also showed that the risk score model was superior to and independent of other clinicopathologic characteristics. Moreover, the risk score model had excellent abilities to predict the overall survival and HCC recurrence of patients with the same tumor stage category.ConclusionsOur risk model is a reliable and superior predictive tool. With this model, we could optimize the risk stratification based on early tumor recurrence and could evaluate the response of patients to adjuvant sorafenib after liver resection.

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Nomogram for individualized prediction of recurrence after postoperative adjuvant TACE for hepatitis B virus-related hepatocellular carcinoma.

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