肺
腺癌
计算机科学
人工智能
医学
内科学
癌症
作者
Yipeng Feng,Hanlin Ding,Xing Huang,Yijian Zhang,Mengyi Lu,Te Zhang,Hui Wang,Yuzhong Chen,Qixing Mao,Wenjie Xia,Bing Chen,Yi Zhang,Chen Chen,Tianhao Gu,Lin Xu,Gaochao Dong,Feng Jiang
标识
DOI:10.1038/s41698-024-00664-0
摘要
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.
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