Improved survival prediction and comparison of prognostic models for patients with hepatocellular carcinoma treated with sorafenib.

BACKGROUND: The 'Prediction Of Survival in Advanced Sorafenib-treated HCC' (PROSASH) model addressed the heterogeneous survival of patients with hepatocellular carcinoma (HCC) treated with sorafenib in clinical trials but requires validation in daily clinical practice. This study aimed to validate, compare and optimize this model for survival prediction. METHODS: Patients treated with sorafenib for HCC at five tertiary European centres were retrospectively staged according to the PROSASH model. In addition, the optimized PROSASH-II model was developed using the data of four centres (training set) and tested in an independent dataset. These models for overall survival (OS) were then compared with existing prognostic models. RESULTS: The PROSASH model was validated in 445 patients, showing clear differences between the four risk groups (OS 16.9-4.6 months). A total of 920 patients (n = 615 in training set, n = 305 in validation set) were available to develop PROSASH-II. This optimized model incorporated fewer and less subjective parameters: the serum albumin, bilirubin and alpha-foetoprotein, and macrovascular invasion, extrahepatic spread and largest tumour size on imaging. Both PROSASH and PROSASH-II showed improved discrimination (C-index 0.62 and 0.63, respectively) compared with existing prognostic scores (C-index ≤0.59). CONCLUSIONS: In HCC patients treated with sorafenib, individualized prediction of survival and risk group stratification using baseline prognostic and predictive parameters with the PROSASH model was validated. The refined PROSASH-II model performed at least as good with fewer and more objective parameters. PROSASH-II can be used as a tool for tailored treatment of HCC in daily practice and to define pre-planned subgroups for future studies.

Using prognostic and predictive clinical features to make personalised survival prediction in advanced hepatocellular carcinoma patients undergoing sorafenib treatment.

BACKGROUND: Sorafenib is the current standard of care for patients with advanced hepatocellular carcinoma (aHCC) and has been shown to improve survival by about 3 months compared to placebo. However, survival varies widely from under three months to over two years. The aim of this study was to build a statistical model that allows personalised survival prediction following sorafenib treatment. METHODS: We had access to 1130 patients undergoing sorafenib treatment for aHCC as part of the control arm for two phase III randomised clinical trials (RCTs). A multivariable model was built that predicts survival based on baseline clinical features. The statistical approach permits both group-level risk stratification and individual-level survival prediction at any given time point. The model was calibrated, and its discrimination assessed through Harrell's c-index and Royston-Sauerbrei's R2D. RESULTS: The variables influencing overall survival were vascular invasion, age, ECOG score, AFP, albumin, creatinine, AST, extra-hepatic spread and aetiology. The model-predicted survival very similar to that observed. The Harrell's c-indices for training and validation sets were 0.72 and 0.70, respectively indicating good prediction. CONCLUSIONS: Our model ('PROSASH') predicts patient survival using baseline clinical features. However, it will require further validation in a routine clinical practice setting.

Development of pre and post-operative models to predict early recurrence of hepatocellular carcinoma after surgical resection.

BACKGROUND & AIMS: Resection is the most widely used potentially curative treatment for patients with early hepatocellular carcinoma (HCC). However, recurrence within 2 years occurs in 30-50% of patients, being the major cause of mortality. Herein, we describe 2 models, both based on widely available clinical data, which permit risk of early recurrence to be assessed before and after resection. METHODS: A total of 3,903 patients undergoing surgical resection with curative intent were recruited from 6 different centres. We built 2 models for early recurrence, 1 using preoperative and 1 using pre and post-operative data, which were internally validated in the Hong Kong cohort. The models were then externally validated in European, Chinese and US cohorts. We developed 2 online calculators to permit easy clinical application. RESULTS: Multivariable analysis identified male gender, large tumour size, multinodular tumour, high albumin-bilirubin (ALBI) grade and high serum alpha-fetoprotein as the key parameters related to early recurrence. Using these variables, a preoperative model (ERASL-pre) gave 3 risk strata for recurrence-free survival (RFS) in the entire cohort - low risk: 2-year RFS 64.8%, intermediate risk: 2-year RFS 42.5% and high risk: 2-year RFS 20.7%. Median survival in each stratum was similar between centres and the discrimination between the 3 strata was enhanced in the post-operative model (ERASL-post) which included 'microvascular invasion'. CONCLUSIONS: Statistical models that can predict the risk of early HCC recurrence after resection have been developed, extensively validated and shown to be applicable in the international setting. Such models will be valuable in guiding surveillance follow-up and in the design of post-resection adjuvant therapy trials. LAY SUMMARY: The most effective treatment of hepatocellular carcinoma is surgical removal of the tumour but there is often recurrence. In this large international study, we develop a statistical method that allows clinicians to estimate the risk of recurrence in an individual patient. This facility enhances communication with the patient about the likely success of the treatment and will help in designing clinical trials that aim to find drugs that decrease the risk of recurrence.

Impact of Viral Status on Survival in Patients Receiving Sorafenib for Advanced Hepatocellular Cancer: A Meta-Analysis of Randomized Phase III Trials.

Purpose Following the Sorafenib Hepatocellular Carcinoma Assessment Randomized Protocol (SHARP) trial, sorafenib has become the standard of care for patients with advanced unresectable hepatocellular carcinoma, but the relation between survival advantage and disease etiology remains unclear. To address this, we undertook an individual patient data meta-analysis of three large prospective randomized trials in which sorafenib was the control arm. Methods Of a total of 3,256 patients, 1,643 (50%) who received sorafenib were available. The primary end point was overall survival (OS). A Bayesian hierarchical approach for individual patient data meta-analyses was applied using a piecewise exponential model. Results are presented in terms of hazard ratios comparing sorafenib with alternative therapies according to hepatitis C virus (HCV) or hepatitis B virus (HBV) status. Results Hazard ratios show improved OS for sorafenib in patients who are both HBV negative and HCV positive (log [hazard ratio], -0.27; 95% CI, -0.46 to -0.06). Median unadjusted survival is 12.6 (11.15 to 13.8) months for sorafenib and 10.2 (8.88 to 12.2) months for "other" treatments in this subgroup. There was no evidence of improvement in OS for any other patient subgroups defined by HBV and HCV. Results were consistent across all trials with heterogeneity assessed using Cochran's Q statistic. Conclusion There is consistent evidence that the effect of sorafenib on OS is dependent on patients' hepatitis status. There is an improved OS for patients negative for HBV and positive for HCV when treated with sorafenib. There was no evidence of any improvement in OS attributable to sorafenib for patients positive for HBV and negative for HCV.