Authors
Shun Liu, Jun Li, Yixiang Lian, Jie Lan, Tingyu Zhang, Huiru He, Meng Du, Haijun Luo, Zhiyi Chen
Published in
The American journal of pathology. Oct 05, 2026. Epub Oct 05, 2026.
Abstract
Prediction of breast cancer biomarkers from routine formalin-fixed, paraffin-embedded (FFPE) hematoxylin and eosin (H&E) images has been explored previously, but whether biomarker-associated morphologic signals can be reliably captured from artifact-prone intraoperative frozen-section (FS) specimens remains insufficiently studied. This retrospective proof-of-concept study developed a foundation model-based multiple-instance learning (MIL) framework to predict estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki-67 status directly from H&E-stained intraoperative FS whole-slide images (WSIs) of 248 breast cancer patients, using postoperative immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) as ground truth. Exploratory feature evaluation identified UNI2-h as a suitable encoder for downstream modeling. In five-fold cross-validation, areas under the receiver operating characteristic (ROC) curve were 0.847 for ER, 0.906 for PR, 0.893 for HER2, and 0.799 for Ki-67, with moderate agreement for ER, PR, and HER2 and fair agreement for Ki-67. An exploratory within-cohort cross-preparation analysis showed limited FFPE-to-FS transferability, whereas the FS-trained model retained relatively favorable performance on FFPE images. Attention heatmaps highlighted tumor regions with biologically plausible biomarker-associated morphologic patterns. These findings provide proof-of-concept that intraoperative FS H&E images retain learnable biomarker-associated morphologic signals despite preparation-related alterations.
PMID:
42833414
Bibliographic data and abstract were imported from PubMed on 06 Oct 2026.
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