Deep learning based semi-automated model can predict lineage in patients with pituitary neuroendocrine tumors.

ABSTRACT: Pituitary neuroendocrine tumors (PitNETs) represent the most prevalent category of neuroendocrine neoplasms. Contemporary classification paradigms emphasize transcription factor immunohistochemistry (IHC) as a cornerstone for molecular subtyping and risk stratification. However, the clinical adoption of this approach is hindered by the lack of standardized interpretative thresholds for antibody staining and limited global availability of specialized reagents, particularly in resource-limited settings. To address these challenges, we developed a semi-automated computational framework that predicts PitNET lineages directly from hematoxylin and eosin (H&E)-stained histology slides. The pipeline employs a dynamic confidence threshold: samples below this threshold undergo confirmatory IHC stain

SUBMITTER: Wu G 

PROVIDER: S-EPMC12509416 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature