Diffusion Attention Expert Model for Predicting and Semi-automatic Localizing STAS in Lung Cancer Histopathological Images

• URL: http://arxiv.org/abs/2605.16444v1
• Date: Fri, 15 May 2026 01:01:35 GMT
• Title: Diffusion Attention Expert Model for Predicting and Semi-automatic Localizing STAS in Lung Cancer Histopathological Images
• Abstract summary: We propose a Diffusion Attention Expert Model (DAEM) to detect spread through air spaces (STAS) in frozen sections (FSs) and paraffin sections (PSs)<n>On an internal dataset, DAEM achieves AUCs of 0.8946 for FSs and 0.9112 for PSs.<n>Several quantitative TME metrics are identified as potential biomarkers for STAS, including micropapillary-type STAS.
• Score: 16.57584586447825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate intraoperative and postoperative diagnosis of spread through air spaces (STAS) is essential for guiding surgical decisions and postoperative management in lung cancer. However, histopathological assessment is labor-intensive and is prone to missed or incorrect diagnoses. We propose a Diffusion Attention Expert Model (DAEM) to detect STAS in frozen sections (FSs) and paraffin sections (PSs). Its diffusion attention expert module leverages full attention aggregation to learn multi-scale features from histopathological images, while a dual-branch architecture strengthens multi-scale feature representation. On an internal dataset, DAEM achieves AUCs of 0.8946 for FSs and 0.9112 for PSs. Validation on external multi-center datasets from eight institutions demonstrates strong generalizability and interpretability. Using tumor microenvironment (TME) features in PSs, we further enable semi-automatic measurement of STAS location and its distance from the primary tumor. Several quantitative TME metrics are identified as potential biomarkers for STAS, including micropapillary-type STAS. Overall, DAEM offers a clinically actionable framework for STAS assessment by enabling accurate and interpretable detection on FSs and PSs, supporting postoperative risk stratification through quantitative TME-based analysis.

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