Risk prediction in intrahepatic cholangiocarcinoma: Direct comparison of the MEGNA score and the 8(th) edition of the UICC/AJCC Cancer staging system

BACKGROUND: External validation of prognostic risk models is essential before they are implemented in clinical practice. This study evaluated the recently developed MEGNA score for survival prediction after resection of intrahepatic cholangiocarcinoma (ICC), with a focus on the direct comparison of its prognostic value to that of the current International Union Against Cancer (UICC)/American Joint Committee on Cancer (AJCC) Cancer staging system. MATERIAL AND METHODS: Between 1997 and 2018, 417 consecutive patients with ICC were referred to our tertiary care centre and were retrospectively identified out of a dedicated clinical database. Of this group, 203 patients underwent surgical resection and met the inclusion criteria. Multivariate analysis was performed to assess the predictors of the recently proposed MEGNA score regarding overall survival (OS). Concordance indices (C-indices) and integrated Brier scores (IBS) were calculated to assess the ability of both the MEGNA score and the current (8(th)) edition of the UICC/AJCC Cancer staging system to predict individual patient outcome. RESULTS: Stratification according to the MEGNA score resulted in a median OS of 34.5 months, 26.1 months, 21.5 months, and 16.6 months for MEGNA scores 0, 1, 2, and ≥3, respectively (log rank p < 0.001). However, of the five factors that contribute to the MEGNA score, age > 60 years was not a predictor for poor OS in our cohort. The C-index for the MEGNA score was 0.58, the IBS was 0.193. The 8(th) edition of the UICC/AJCC system performed slightly better, with a C-index of 0.61 and an IBS of 0.186. CONCLUSION: The ability of the MEGNA score to predict individual patient outcome was only moderate in this external validation. Its prognostic value did not reach that of the more widely known and used UICC/AJCC system. However, neither scoring system performed well enough to support clear-cut clinical decisions.