Liver is the most common solid organ for metastasis from various primary tumors. For decades, hepatectomy is regarded as a curative treatment for liver-only colorectal liver metastasis (CLM) and neuroendocrine liver metastasis. Favorable median overall survival (up to 72 months), 5-year survival rate (up to 58.3 ​%) and 10-year actual survival rate (up to 16 ​%) were reported after hepatectomy for CLM [1,2]. Similarly, excellent long-term outcome (5-year survival rate: 74 ​%–90 ​%) was documented for surgically resectable neuroendocrine liver metastasis [3,4]. Nonetheless, the role of hepatectomy for non-colorectal non-neuroendocrine liver metastasis (NCNNLM) is less clearly defined. This could be related to the heterogeneity in tumor biological behavior within this group of patients.

With the current stringent patient selection, advancement in surgical techniques, improvement in perioperative patient care and the emergence of effective systemic therapy, there is a growing interest in hepatectomy for surgically resectable NCNNLM. There were numerous large-scale studies revealing favorable long-term outcomes (5-year survival up to 42 ​%) after hepatectomy for NCNNLM [[5], [6], [7]]. In addition, a recent systemic review and meta-analysis has confirmed the efficacy and safety of hepatectomy for selected patients with NCNNLM [8]. To highlight its importance, it is logical to compare the long-term outcome after hepatectomy between this patient group and surgically resectable CLM, for which hepatectomy is the gold standard in the treatment algorithm. Studies on this issue were limited in the literature and the results were conflicting [9,10]. Owing to the small patient number and heterogeneity in patients and tumor characteristics in these studies, the solid conclusion can hardly be drawn. Therefore, a general population database and data-matching analysis might overcome these shortcomings. Thus, the present study aims to compare long-term survival outcomes after hepatectomy between NCNNLM and CLM, using propensity-score (PS) matching analysis of a local population database. Independent prognostic factors influencing survival will be determined to provide guidance for patient selection.