Improving the prediction of Spreading Through Air Spaces (STAS) in primary lung cancer with a dynamic dual-delta hybrid machine learning model : a multicenter cohort study
© 2023. The Author(s)..
BACKGROUND: Reliable pre-surgical prediction of spreading through air spaces (STAS) in primary lung cancer is essential for precision treatment and surgical decision-making. We aimed to develop and validate a dual-delta deep-learning and radiomics model based on pretreatment computed tomography (CT) image series to predict the STAS in patients with lung cancer.
METHOD: Six hundred seventy-four patients with pre-surgery CT follow-up scans (with a minimum interval of two weeks) and primary lung cancer diagnosed by surgery were retrospectively recruited from three Chinese hospitals. The training cohort and internal validation cohort, comprising 509 and 76 patients respectively, were selected from Shanghai Chest Hospital; the external validation cohorts comprised 36 and 53 patients from two other centers, respectively. Four imaging signatures (classic radiomics features and deep learning [DL] features, delta-radiomics and delta-DL features) reflecting the STAS status were constructed from the pretreatment CT images by comprehensive methods including handcrafting, 3D views extraction, image registration and subtraction. A stepwise optimized three-step procedure, including feature extraction (by DL and time-base radiomics slope), feature selection (by reproducibility check and 45 selection algorithms), and classification (32 classifiers considered), was applied for signature building and methodology optimization. The interpretability of the proposed model was further assessed with Grad-CAM for DL-features and feature ranking for radiomics features.
RESULTS: The dual-delta model showed satisfactory discrimination between STAS and non-STAS and yielded the areas under the receiver operating curve (AUCs) of 0.94 (95% CI, 0.92-0.96), 0.84 (95% CI, 0.82-0.86), and 0.84 (95% CI, 0.83-0.85) in the internal and two external validation cohorts, respectively, with interpretable core feature sets and feature maps.
CONCLUSION: The coupling of delta-DL model with delta-radiomics features enriches information such as anisotropy of tumor growth and heterogeneous changes within the tumor during the radiological follow-up, which could provide valuable information for STAS prediction in primary lung cancer.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:11 |
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| Enthalten in: |
Biomarker research - 11(2023), 1 vom: 23. Nov., Seite 102 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jin, Weiqiu [VerfasserIn] |
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| Links: |
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| Themen: |
Deep learning |
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| Anmerkungen: |
Date Revised 26.11.2023 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1186/s40364-023-00539-9 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM36488147X |
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| 100 | 1 | |a Jin, Weiqiu |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Improving the prediction of Spreading Through Air Spaces (STAS) in primary lung cancer with a dynamic dual-delta hybrid machine learning model |b a multicenter cohort study |
| 264 | 1 | |c 2023 | |
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| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2023. The Author(s). | ||
| 520 | |a BACKGROUND: Reliable pre-surgical prediction of spreading through air spaces (STAS) in primary lung cancer is essential for precision treatment and surgical decision-making. We aimed to develop and validate a dual-delta deep-learning and radiomics model based on pretreatment computed tomography (CT) image series to predict the STAS in patients with lung cancer | ||
| 520 | |a METHOD: Six hundred seventy-four patients with pre-surgery CT follow-up scans (with a minimum interval of two weeks) and primary lung cancer diagnosed by surgery were retrospectively recruited from three Chinese hospitals. The training cohort and internal validation cohort, comprising 509 and 76 patients respectively, were selected from Shanghai Chest Hospital; the external validation cohorts comprised 36 and 53 patients from two other centers, respectively. Four imaging signatures (classic radiomics features and deep learning [DL] features, delta-radiomics and delta-DL features) reflecting the STAS status were constructed from the pretreatment CT images by comprehensive methods including handcrafting, 3D views extraction, image registration and subtraction. A stepwise optimized three-step procedure, including feature extraction (by DL and time-base radiomics slope), feature selection (by reproducibility check and 45 selection algorithms), and classification (32 classifiers considered), was applied for signature building and methodology optimization. The interpretability of the proposed model was further assessed with Grad-CAM for DL-features and feature ranking for radiomics features | ||
| 520 | |a RESULTS: The dual-delta model showed satisfactory discrimination between STAS and non-STAS and yielded the areas under the receiver operating curve (AUCs) of 0.94 (95% CI, 0.92-0.96), 0.84 (95% CI, 0.82-0.86), and 0.84 (95% CI, 0.83-0.85) in the internal and two external validation cohorts, respectively, with interpretable core feature sets and feature maps | ||
| 520 | |a CONCLUSION: The coupling of delta-DL model with delta-radiomics features enriches information such as anisotropy of tumor growth and heterogeneous changes within the tumor during the radiological follow-up, which could provide valuable information for STAS prediction in primary lung cancer | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Lung cancer | |
| 650 | 4 | |a Radiomics | |
| 650 | 4 | |a Spreading through air spaces (STAS) | |
| 700 | 1 | |a Shen, Leilei |e verfasserin |4 aut | |
| 700 | 1 | |a Tian, Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Hongda |e verfasserin |4 aut | |
| 700 | 1 | |a Zou, Ningyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Mengwei |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Qian |e verfasserin |4 aut | |
| 700 | 1 | |a Dong, Changzi |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Qisheng |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Long |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Jia |e verfasserin |4 aut | |
| 700 | 1 | |a Yuan, Zheng |e verfasserin |4 aut | |
| 700 | 1 | |a Ye, Xiaodan |e verfasserin |4 aut | |
| 700 | 1 | |a Luo, Qingquan |e verfasserin |4 aut | |
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