Sorafenib improves overall survival (OS) of patients with hepatocellular carcinoma (HCC) in the absence of objective response. Thus, time to tumor progression (TTP) is used to capture benefits of novel molecular agents, but proof of its surrogacy with survival is lacking. Furthermore, survival predictors upon progression are not established and there is a need to characterize postprogression survival (PPS) and assess with timedependent covariates analysis if it is influenced by progression pattern, and not solely by simultaneous impairment of liver function and performance status. We prospectively followed HCC patients treated with sorafenib. Clinical and biochemical evaluation were done every 4 weeks. Radiologic assessment of progression was done at week 4 and then every 8 weeks using RECIST 1.1. The progression pattern was divided into: intrahepatic/extrahepatic increase in tumor size, new intrahepatic lesion, and new extrahepatic lesion (NEH). We included 147 patients (hepatitis C virus [HCV] 32-4.44] is also an independent predictor of OS and PPS in patients with radiologic progression. Conclusion: Tumor progression is a surrogate of survival but its impact varies according to progression pattern. Thus, PPS is influenced by progression pattern and this is key in prognostic prediction and second-line trial design and analysis. (HEPATOLOGY 2013;58:2023-2031 S orafenib improves the overall survival (OS) of hepatocellular carcinoma (HCC) patients in the absence of objective response.1 This has brought about the emergence of time to tumor progression (TTP) or progression-free survival (PFS) as better endpoints for detecting and capturing the benefits of novel molecular agents. However, the correlation between TTP and OS or PFS and OS has not been established in HCC.2-4 Indeed, a phase 3 trial comparing sorafenib versus sunitinib showed a similar PFS but OS was significantly better in sorafenib-treated patients. 4 As in other cancer types, it is necessary to study postprogression survival (PPS) and define if progression pattern and treatment upon progression emerge as major confounders in understanding the OS data. [5][6][7][8]
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