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CT-Based Deep-Learning Model for Spread-Through-Air-Spaces Prediction in Ground Glass-Predominant Lung Adenocarcinoma

  • Thoracic Oncology
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Background

Sublobar resection is strongly associated with poor prognosis in early-stage lung adenocarcinoma, with the presence of tumor spread through air spaces (STAS). Thus, preoperative prediction of STAS is important for surgical planning. This study aimed to develop a STAS deep-learning (STAS-DL) prediction model in lung adenocarcinoma with tumor smaller than 3 cm and a consolidation-to-tumor (C/T) ratio less than 0.5.

Methods

The study retrospectively enrolled of 581 patients from two institutions between 2015 and 2019. The STAS-DL model was developed to extract the feature of solid components through solid components gated (SCG) for predicting STAS. The STAS-DL model was assessed with external validation in the testing sets and compared with the deep-learning model without SCG (STAS-DLwoSCG), the radiomics-based model, the C/T ratio, and five thoracic surgeons. The performance of the models was evaluated using area under the curve (AUC), accuracy and standardized net benefit of the decision curve analysis.

Results

The study evaluated 458 patients (institute 1) in the training set and 123 patients (institute 2) in the testing set. The proposed STAS-DL yielded the best performance compared with the other methods in the testing set, with an AUC of 0.82 and an accuracy of 74%, outperformed the STAS-DLwoSCG with an accuracy of 70%, and was superior to the physicians with an AUC of 0.68. Moreover, STAS-DL achieved the highest standardized net benefit compared with the other methods.

Conclusion

The proposed STAS-DL model has great potential for the preoperative prediction of STAS and may support decision-making for surgical planning in early-stage, ground glass-predominant lung adenocarcinoma.

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Acknowledgment

We would like to thank Ms. Yu-Hsuan Hu and Ms. Wei-Ying Huang for data collection, analysis and project administration. We also would like to thank Dr. Xu-Heng Chiang, Dr. Chao-Wen Lu, Dr. Tzu-Ning Kao and Dr. Hsin-Ying Lee for their interpretation of CT Images. This work was supported by grants from the Ministry of Science and Technology, Taipei, Taiwan (Grant Nos. MOST 111-2221-E-002-070 and 111-2811-E-002-004-MY2) and by the National Taiwan University Hospital, Taipei, Taiwan (Grant No. NTUH111-S0199). We thank Ms. Yu-Hsuan Hu and Ms. Wei-Ying Huang for data collection and analysis as well as project administration. We also thank Dr. Xu-Heng Chiang, Dr. Chao-Wen Lu, Dr. Tzu-Ning Kao, and Dr. Hsin-Ying Lee for their interpretation of CT images.

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Author notes

  1. Mong-Wei Lin and Li-Wei Chen have contributed equally as co-first authors.

Authors and Affiliations

  1. Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan

    Mong-Wei Lin MD & Jin-Shing Chen MD, PhD

  2. Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan

    Li-Wei Chen PhD, De-Xiang Ou MS & Chung-Ming Chen PhD

  3. Department of Surgery, National Taiwan University Hospital Biomedical Park Hospital, Zhubei City, Hsinchu County, Taiwan

    Shun-Mao Yang MD & Yi-Hsuan Lee MD

  4. Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan

    Min-Shu Hsieh MD

  5. Department of Surgical Oncology, National Taiwan University Cancer Center, Taipei, Taiwan

    Jin-Shing Chen MD, PhD

  6. Department of Radiology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan

    Yeun-Chung Chang MD, PhD

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  1. Mong-Wei Lin MD
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Correspondence to Chung-Ming Chen PhD.

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Ethical Approval

This study was approved by the Institutional Review Board of the National Taiwan University Hospital (approval number: 202207035RIND) and National Taiwan University Hospital Hsin-Chu Branch (Approval Number: 107-033-E).

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Lin, MW., Chen, LW., Yang, SM. et al. CT-Based Deep-Learning Model for Spread-Through-Air-Spaces Prediction in Ground Glass-Predominant Lung Adenocarcinoma. Ann Surg Oncol 31, 1536–1545 (2024). https://doi.org/10.1245/s10434-023-14565-2

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  • DOI: https://doi.org/10.1245/s10434-023-14565-2

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