Authors
Zeming Yan, Yi Lin, Qingjun Cen, Wanming Hu, Zongming Wang, Zize Feng, Jing Guo, Xiaobing Jiang
Published in
BMC endocrine disorders. Jul 11, 2026. Epub Jul 11, 2026.
Abstract
Accurate classification of granulation patterns in growth hormone-secreting pituitary neuroendocrine tumors (GH-PitNETs) is clinically essential but traditionally dependent on labor-intensive immunohistochemistry (IHC). To address this, we developed the Cross-Attention Pituitary Neuroendocrine Tumor Network (CA-PITNET), a deep learning model for automated subtyping directly from routine H&E-stained whole-slide images (WSIs). Utilizing 95 GH-PitNETs WSIs (54,610 patches) and 36 nonfunctioning PitNET WSIs (39,590 patches), the model was evaluated on two tasks: distinguishing GH-PitNETs from nonfunctioning PitNETs (Task I) and differentiating densely from sparsely granulated GH-PitNETs (Task II). Following staining normalization and rigorous validation, CA-PITNET demonstrated exceptional diagnostic accuracy. For Task I, CA-PITNET achieved patient-level AUCs of 0.948 (95% CI: 0.854-1.000; internal validation, n = 48) and 0.948 (95% CI: 0.837-1.000; external test, n = 33), with accuracy of 0.938 and 0.909, respectively. For Task II, patient-level AUCs were 0.935 (95% CI: 0.890-1.000; internal validation, n = 38) and 0.875 (95% CI: 0.670-1.000; external test, n = 25), with accuracy of 0.895 and 0.840, respectively. Generated heatmaps precisely localized diagnostic regions, highlighting diagnostically relevant regions consistent with pathologist-reviewed areas. This model is designed to augment the pathological workflow by improving the subtyping efficiency of GH-PitNETs, serving as a valuable adjunct to the standard IHC-based classification.
PMID:
42436482
Bibliographic data and abstract were imported from PubMed on 12 Jul 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 10
- Comments 0