Deep learning-based detection and semi-quantitative model for spread through air spaces (STAS) in lung adenocarcinoma

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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Publication Details

Journal
npj Precision Oncology
Published
2024-08-05
DOI
https://doi.org/10.1038/s41698-024-00664-0
Citations
9
Primary Topic
Lung Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
3.77

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article

Deep learning-based detection and semi-quantitative model for spread through air spaces (STAS) in lung adenocarcinoma

Tianhao Gu, Yipeng Feng, Feng Jiang, Hanlin Ding et al.
9 citations
npj Precision Oncology
Lung Cancer Diagnosis and Treatment
3.77
article

Deep learning-based detection and semi-quantitative model for spread through air spaces (STAS) in lung adenocarcinoma

Tianhao Gu, Yipeng Feng, Feng Jiang, Hanlin Ding, Yi Zhang, Mengyi Lu, Bing Chen, Hui Wang, Lin Xu, Yuzhong Chen, Te Zhang, Xing Huang, Wenjie Xia, Yijian Zhang, Gaochao Dong, Chen Chen, Qixing Mao
article en
9 citations

Abstract

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.

npj Precision OncologyVol. 8(1)
Shandong University (CN), Jiangsu Cancer Hospital (CN), Second Affiliated Hospital of Nanjing Medical University (CN), Nanjing Medical University (CN)
National Natural Science Foundation of China, Government of Jiangsu Province, Jiangsu Cancer Hospital
Openalex Percentile: Top 6%
Lung Cancer Diagnosis and Treatment
3.77
DOIOpen Access PDFOpenAlex
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