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Liver international : official journal of the International Association for the Study of the Liver

via Europe PMC

Primary Hepatic Neuroendocrine Neoplasms: Systematic Review and Expert Pathologist Reappraisal Using the WHO 6th Edition Framework.

Maestri M, Cicerone O, Gambella A, Grillo F, La Rosa S, Uccella S, Vanoli A.

Systematic ReviewOpen access

AI summary β€” for doctors, generated from the abstract only

After expert pathology reappraisal, only a minority of reported primary hepatic neuroendocrine neoplasms were convincingly primary, and most were well-differentiated tumors.

What was studied

A PRISMA-based systematic review with expert pathologist reappraisal under the WHO 6th edition framework, of 67 potentially eligible studies of primary hepatic neuroendocrine neoplasms.

Key findings

  • 28 studies (49.1%) were retained, with 57 likely cases, mainly case reports or small series
  • Most were well-differentiated NETs (78.9%), with NECs 8.8%, MiNENs 3.5% and amphicrine carcinomas 1.8%
  • 7% were high-grade NENs that could not be classified as well or poorly differentiated

Clinical relevance

Clinicians should interpret older literature cautiously and ensure thorough work-up to exclude extrahepatic primaries.

Who should care

Hepatology

Limitations

The evidence is small, with rarity, uncertain hepatic origin and historical misclassification. The abstract does not report treatment outcomes.

Primary hepatic NENs are extremely rare and the literature needs standardized terminology and registries.

This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates β€” verify against the original source before acting on it.

Abstract

Primary hepatic neuroendocrine neoplasms (PHNENs) are exceedingly rare. Published evidence often lacks adequate clinicopathological support for a primary hepatic origin and uses inconsistent historical terminology. Previous reviews have pooled well-differentiated neuroendocrine tumours (NETs), poorly differentiated neuroendocrine carcinomas (NECs), and mixed neuroendocrine-non-neuroendocrine neoplasms (MiNENs), despite differences in biology, prognosis, and management. We systematically reviewed reported PHNENs using PRISMA-based screening and expert pathology reappraisal within the WHO 6th edition morphology-first framework. A systematic literature search was conducted in PubMed/MEDLINE, Scopus, and Web of Science. Titles, abstracts, and full texts were screened according to predefined eligibility criteria; eligible articles underwent second-level expert assessment of pathology, including morphology (including NET G3 vs. NEC and MiNEN vs. amphicrine carcinoma), immunohistochemistry (expression of at least two NE markers), proliferation (Ki-67/mitotic activity), as well as clinical/imaging work-up to exclude metastases from extrahepatic primaries. Of the 67 potentially eligible studies submitted to expert pathology-based reappraisal, 28 studies (49.1%) were retained in the final dataset with 57 likely NEN cases (mainly case reports/small case series). Among the 57 cases, most were well-differentiated NETs (78.9%); NECs (8.8%), MiNENs (3.5%), and amphicrine carcinomas (1.8%) were less frequent. Some cases were high-grade NENs that could not be distinguished as well or poorly differentiated (7%). PHNEN literature is constrained by rarity, uncertainty of hepatic origin, and historical misclassification. Future studies and registries should report a minimum diagnostic dataset with standardised terminology according to the WHO 6th edition framework and in-depth clinical/imaging studies.

DOI

10.1111/liv.70921

PMID

42870503

Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.

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