Gross pathological classification of peripheral cholangiocarcinoma determines the efficacy of hepatectomy

Abstract

Peripheral cholangiocarcinoma (PCC) can be grossly classified as mass-forming (MF), periductal-infiltrating (PI), and intraductal papillary (IP) types. IP-PCC should be distinguished from other types of PCC because patients with IP-PCC have a more favorable prognosis. We hypothesized that gross pathological classification of non-IP-PCC could determine the efficacy of hepatectomy. We retrospectively reviewed 224 histologically proven PCCs (including 172 PCCs from patients having non-IP type tumors) from patients who underwent hepatectomy between 1977 and 2007. Non-IP-PCCs were further classified as MF, MF mixed with PI (MF-PI), and PI for comparison. Of the 224 patients with PCC, 52 had IP-PCC (23.2 %), and 172 had non-IP-PCC (76.8 %). One hundred one of the 172 non-IP-PCC patients had a curative resection (curative resection rate 58.7 %). The follow-up duration ranged from 1.1 to 193.1 months (median 13.4 months). Overall survival (OS) rates for the non-IP-PCC patients at 1 and 5 years were 58.4 and 15.1 %, respectively. Absence of symptoms, lower alkaline phosphatase levels, normal carcinoembryonic antigen (CEA), and presence of MF-type PCC independently and favorably affected OS for the non-IP-PCC patients following hepatectomy. Independent factors favorably predicting OS for the MF-PCC patients were the absence of symptoms, hepatolithiasis, normal CEA levels, successful curative hepatectomy, and negative lymph node metastasis, while for the MF-PI-PCC patients following hepatectomy, one independent factor, successful curative hepatectomy, favorably predicted OS. For the PI-PCC patients, the independent factors favorably predicting OS were normal albumin levels and negative lymph node metastasis. It is important to correctly differentiate between the gross pathological classifications of non-IP-PCC because of their distinct characteristics and outcomes following hepatectomy. Correct gross pathological classification is essential for further translational investigations.