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AimThis study aims to improve the classification performance of the eighth American Joint Committee on Cancer (AJCC) staging system for perihilar cholangiocarcinoma (pCCA) by proposing the Khon Kaen University (KKU) staging system developed in cholangiocarcinoma-prevalent Northeast Thailand.MethodFour hundred eighty-eight patients with pCCA who underwent partial hepatectomy between 2002 and 2017 at the Srinagarind Hospital, Faculty of Medicine, Khon Kaen University, Thailand, were included. Overall survival (OS) related to clinicopathological features was analyzed using the Kaplan–Meier method. Logrank test was performed in univariate analysis to compare OS data of clinicopathological features to determine risk factors for poor survival. Significant features were further analyzed by multivariate analysis (Cox regression) to identify prognostic factors which were then employed to modify the eighth AJCC staging system.ResultsMultivariate analysis showed that growth pattern (HR = 4.67–19.72, p < 0.001), moderately and poorly differentiated histological grades (HR = 2.31–4.99, p < 0.05 and 0.001, respectively), lymph node metastasis N1 and N2 (HR = 1.37 and 2.18, p < 0.05 and 0.01, respectively), and distant metastasis (HR = 2.11, p < 0.001) were independent factors when compared to their respective reference groups. There was a clear separation of patients with pCCA into KKU stage: I [OS = 116 months (mo.)], II (OS = 46 mo.), IIIA (OS = 24 mo.), IIIB (11 mo.), IVA (OS = 7 mo.), and IVB (OS = 6 mo.).ConclusionThe new staging system was based on the incorporation of growth patterns to modify the eighth AJCC staging system. The classification performance demonstrated that the KKU staging system was able to classify and distinctly separate patients with pCCA into those with good and poor outcomes. It was also able to improve the stratification performance and discriminative ability of different stages of pCCA classification better than the eighth AJCC staging system. Hence, the KKU staging system is proposed as an alternative model to augment the accuracy of survival prognostication and treatment performance for patients with pCCA.