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

TL;DR

This paper introduces DAEM, a diffusion attention expert model that accurately detects STAS in lung cancer histopathology images, with strong generalizability and interpretability, aiding clinical decision-making.

Contribution

The paper presents a novel diffusion attention expert model that improves STAS detection accuracy and interpretability across multiple datasets, including semi-automatic localization using TME features.

Findings

DAEM achieves AUCs of 0.8946 for FSs and 0.9112 for PSs.

Validation on eight external datasets demonstrates strong generalizability.

Quantitative TME metrics identified as potential biomarkers for STAS.

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.