A literature-based treatment algorithm for low-grade neuroendocrine liver metastases.

Journal Article (Journal Article)

BACKGROUND: The optimal timing of treatment of liver metastases from low-grade neuroendocrine tumors (LG-NELM) varies significantly due to numerous treatment modalities and the literature supporting various treatment(s). This study sought to create and validate a literature-based treatment algorithm for LG-NELM. METHODS: A treatment algorithm to maximize overall survival (OS) was designed using peer-reviewed articles evaluating treatment of LG-NELM. This algorithm was retrospectively applied to patients treated for LG-NELM at our institution. Deviation was determined based on whether or not a patient received treatment consistent with that recommended by the algorithm. Patients who did and did not deviate from the algorithm were compared with respect to OS and number of treatments. RESULTS: Applying our algorithm to a 149-patient cohort, 57 (38%) deviated from recommended treatment. Deviation occurred in the form of alternative (28, 49%) versus additional procedures (29, 51%). Algorithm deviators underwent significantly more procedures than non-deviators (median 1 vs. 2, p < 0.001). Cox model indicated no difference in OS associated with algorithm deviation (HR 1.19, p = 0.58) when controlling for age and tumor characteristics. CONCLUSION: This literature-based algorithm helps standardize treatment protocols in patients with LG-NELM and can reduce cost and risk by minimizing unnecessary procedures. Prospective implementation and validation is required.

Full Text

Duke Authors

Cited Authors

  • Bhutiani, N; Bruenderman, EH; Jones, JM; Wehry, JH; Egger, ME; Philips, P; Scoggins, CR; McMasters, KM; Martin, RCG

Published Date

  • January 2021

Published In

Volume / Issue

  • 23 / 1

Start / End Page

  • 63 - 70

PubMed ID

  • 32448647

Electronic International Standard Serial Number (EISSN)

  • 1477-2574

Digital Object Identifier (DOI)

  • 10.1016/j.hpb.2020.04.012


  • eng

Conference Location

  • England