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Cancers
Volume 16
Issue 11
10.3390/cancers16112132
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Open AccessArticle
by De-Xiang Ou SciProfilesScilitPreprints.orgGoogle Scholar Chao-Wen Lu SciProfilesScilitPreprints.orgGoogle Scholar Li-Wei Chen SciProfilesScilitPreprints.orgGoogle Scholar Wen-Yao Lee SciProfilesScilitPreprints.orgGoogle Scholar Hsiang-Wei Hu SciProfilesScilitPreprints.orgGoogle Scholar Jen-Hao Chuang SciProfilesScilitPreprints.orgGoogle Scholar Mong-Wei Lin SciProfilesScilitPreprints.orgGoogle Scholar Kuan-Yu Chen SciProfilesScilitPreprints.orgGoogle Scholar Ling-Ying Chiu SciProfilesScilitPreprints.orgGoogle Scholar Jin-Shing Chen SciProfilesScilitPreprints.orgGoogle Scholar Chung-Ming Chen SciProfilesScilitPreprints.orgGoogle Scholar Min-Shu Hsieh SciProfilesScilitPreprints.orgGoogle Scholar
De-Xiang Ou
, Chao-Wen LuLi-Wei Chen, Wen-Yao LeeHsiang-Wei HuJen-Hao ChuangMong-Wei Lin, Kuan-Yu ChenLing-Ying ChiuJin-Shing ChenChung-Ming Chen and Min-Shu Hsieh
1
Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei 10617, Taiwan
2
Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan
3
Graduate Institute of Pathology, National Taiwan University College of Medicine, Taipei 100, Taiwan
4
Division of Thoracic Surgery, Department of Surgery, Fu Jen Catholic University Hospital, No. 69, Guizi Road, Taishan District, New Taipei City 24352, Taiwan
5
Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan
6
Institute of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Cancers 2024, 16(11), 2132; https://doi.org/10.3390/cancers16112132
Submission received: 19 April 2024/Revised: 25 May 2024/Accepted: 1 June 2024/Published: 3 June 2024
(This article belongs to the Special Issue Applications of Machine and Deep Learning in Thoracic Malignancies)
Simple Summary
Simple Summary: This study included 227 patients, among whom 27.7% (63/227) were diagnosed with tumors spread through air spaces (STASs), which have been shown to be associated with shorter recurrence-free survival and poor prognosis. A prediction model was developed to forecast tumor STAS in early-stage lung adenocarcinoma pathology images. The radiomics prediction model demonstrated good performance, with an AUC value of 0.83. This prediction model can assist pathologists in the diagnostic processes of clinical practice.
Abstract
The presence of spread through air spaces (STASs) in early-stage lung adenocarcinoma is a significant prognostic factor associated with disease recurrence and poor outcomes. Although current STAS detection methods rely on pathological examinations, the advent of artificial intelligence (AI) offers opportunities for automated histopathological image analysis. This study developed a deep learning (DL) model for STAS prediction and investigated the correlation between the prediction results and patient outcomes. To develop the DL-based STAS prediction model, 1053 digital pathology whole-slide images (WSIs) from the competition dataset were enrolled in the training set, and 227 WSIs from the National Taiwan University Hospital were enrolled for external validation. A YOLOv5-based framework comprising preprocessing, candidate detection, false-positive reduction, and patient-based prediction was proposed for STAS prediction. The model achieved an area under the curve (AUC) of 0.83 in predicting STAS presence, with 72% accuracy, 81% sensitivity, and 63% specificity. Additionally, the DL model demonstrated a prognostic value in disease-free survival compared to that of pathological evaluation. These findings suggest that DL-based STAS prediction could serve as an adjunctive screening tool and facilitate clinical decision-making in patients with early-stage lung adenocarcinoma.
Keywords: deep learning; spread through air space; lung adenocarcinoma; digital histology; whole slide image; pathology
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MDPI and ACS Style
Ou, D.-X.; Lu, C.-W.; Chen, L.-W.; Lee, W.-Y.; Hu, H.-W.; Chuang, J.-H.; Lin, M.-W.; Chen, K.-Y.; Chiu, L.-Y.; Chen, J.-S.;et al. Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images. Cancers 2024, 16, 2132.https://doi.org/10.3390/cancers16112132
AMA Style
Ou D-X, Lu C-W, Chen L-W, Lee W-Y, Hu H-W, Chuang J-H, Lin M-W, Chen K-Y, Chiu L-Y, Chen J-S,et al. Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images. Cancers. 2024; 16(11):2132.https://doi.org/10.3390/cancers16112132
Chicago/Turabian Style
Ou, De-Xiang, Chao-Wen Lu, Li-Wei Chen, Wen-Yao Lee, Hsiang-Wei Hu, Jen-Hao Chuang, Mong-Wei Lin, Kuan-Yu Chen, Ling-Ying Chiu, Jin-Shing Chen,and et al. 2024. "Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images" Cancers 16, no. 11: 2132.https://doi.org/10.3390/cancers16112132
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
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MDPI and ACS Style
Ou, D.-X.; Lu, C.-W.; Chen, L.-W.; Lee, W.-Y.; Hu, H.-W.; Chuang, J.-H.; Lin, M.-W.; Chen, K.-Y.; Chiu, L.-Y.; Chen, J.-S.;et al. Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images. Cancers 2024, 16, 2132.https://doi.org/10.3390/cancers16112132
AMA Style
Ou D-X, Lu C-W, Chen L-W, Lee W-Y, Hu H-W, Chuang J-H, Lin M-W, Chen K-Y, Chiu L-Y, Chen J-S,et al. Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images. Cancers. 2024; 16(11):2132.https://doi.org/10.3390/cancers16112132
Chicago/Turabian Style
Ou, De-Xiang, Chao-Wen Lu, Li-Wei Chen, Wen-Yao Lee, Hsiang-Wei Hu, Jen-Hao Chuang, Mong-Wei Lin, Kuan-Yu Chen, Ling-Ying Chiu, Jin-Shing Chen,and et al. 2024. "Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images" Cancers 16, no. 11: 2132.https://doi.org/10.3390/cancers16112132
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
