肿瘤通过气腔扩散（STAS）是一种影响肺腺癌（LUAD）患者预后的独特转移模式。 STAS 检测存在一些挑战，包括误检测、观察者间一致性低以及缺乏定量分析。在这项研究中，总共收集了 489 张数字全玻片图像 (WSI)。基于深度学习的 STAS 检测模型（名为 STASNet）的构建是为了计算与 STAS 密度和距离相关的半定量参数。 STASNet 在图块级别上展示了 0.93 的 STAS 检测准确度，在 WSI 级别上确定 STAS 状态的 AUC 为 0.72-0.78。在半定量参数中，T10S 与空间位置信息相结合，对 I 期 LUAD 患者的无病生存率进行了显着分层。此外，STASNet 被部署到实时病理诊断环境中，提高了 STAS 检测率，并识别出了三种容易被误识别的隐匿性 STAS。© 2024。作者。

Tumor spread through air spaces (STAS) is a distinctive metastatic pattern affecting prognosis in lung adenocarcinoma (LUAD) patients. Several challenges are associated with STAS detection, including misdetection, low interobserver agreement, and lack of quantitative analysis. In this research, a total of 489 digital whole slide images (WSIs) were collected. The deep learning-based STAS detection model, named STASNet, was constructed to calculate semi-quantitative parameters associated with STAS density and distance. STASNet demonstrated an accuracy of 0.93 for STAS detection at the tiles level and had an AUC of 0.72-0.78 for determining the STAS status at the WSI level. Among the semi-quantitative parameters, T10S, combined with the spatial location information, significantly stratified stage I LUAD patients on disease-free survival. Additionally, STASNet was deployed into a real-time pathological diagnostic environment, which boosted the STAS detection rate and led to the identification of three easily misidentified types of occult STAS.© 2024. The Author(s).