A Transformer-Based Multi-Scale Deep Learning Model for Lung Cancer Surgery Optimization
Lung cancer surgery presents significant challenges due to its complexity and the need for precise risk stratification to improve patient outcomes. This study presents a Transformer-based multi-scale deep learning framework that integrates imaging, clinical, and genomic data to optimize decision-mak...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10967497/ |
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| author | Pengfei Zhu Tingmin Wang Fan Yang Meng Wang Yunjie Zhang |
| author_facet | Pengfei Zhu Tingmin Wang Fan Yang Meng Wang Yunjie Zhang |
| author_sort | Pengfei Zhu |
| collection | DOAJ |
| description | Lung cancer surgery presents significant challenges due to its complexity and the need for precise risk stratification to improve patient outcomes. This study presents a Transformer-based multi-scale deep learning framework that integrates imaging, clinical, and genomic data to optimize decision-making surrounding surgery. By using the self-attention mechanism in Transformers and multi-scale feature extraction, the model expertly explores different data modalities. Therefore, it enables a precise prediction of surgical risks, such as delayed extubation and mortality; besides, it further performs risk stratification, having the model improve resource utilization by identifying high- and low-risk patients, ensuring that intervention is matched accordingly and resources are not wasted on unnecessary measures. Thorough evaluations, including ablation experiments, case analyses, and error analyses, prove the model’s robustness and practical applicability in a clinical setting. This study demonstrates the game-changing potential of advanced deep learning techniques in the field of precision medicine and provides a concrete framework for personalized treatment in lung cancer surgery, laying the foundation for broader healthcare applications. |
| format | Article |
| id | doaj-art-c3c8461027a8494f8a3624e69b97c6d4 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c3c8461027a8494f8a3624e69b97c6d42025-08-20T02:20:23ZengIEEEIEEE Access2169-35362025-01-0113700447005410.1109/ACCESS.2025.356194810967497A Transformer-Based Multi-Scale Deep Learning Model for Lung Cancer Surgery OptimizationPengfei Zhu0Tingmin Wang1Fan Yang2Meng Wang3Yunjie Zhang4https://orcid.org/0009-0009-2984-3865Thoracic Surgery, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, ChinaThoracic Surgery, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, ChinaDepartment of Anesthesiology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, ChinaGeneral Surgery, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, ChinaGeneral Surgery, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, ChinaLung cancer surgery presents significant challenges due to its complexity and the need for precise risk stratification to improve patient outcomes. This study presents a Transformer-based multi-scale deep learning framework that integrates imaging, clinical, and genomic data to optimize decision-making surrounding surgery. By using the self-attention mechanism in Transformers and multi-scale feature extraction, the model expertly explores different data modalities. Therefore, it enables a precise prediction of surgical risks, such as delayed extubation and mortality; besides, it further performs risk stratification, having the model improve resource utilization by identifying high- and low-risk patients, ensuring that intervention is matched accordingly and resources are not wasted on unnecessary measures. Thorough evaluations, including ablation experiments, case analyses, and error analyses, prove the model’s robustness and practical applicability in a clinical setting. This study demonstrates the game-changing potential of advanced deep learning techniques in the field of precision medicine and provides a concrete framework for personalized treatment in lung cancer surgery, laying the foundation for broader healthcare applications.https://ieeexplore.ieee.org/document/10967497/Transformer-based modelmulti-scale deep learninglung cancer surgeryrisk assessmentmulti-modal data integrationpersonalized medicine |
| spellingShingle | Pengfei Zhu Tingmin Wang Fan Yang Meng Wang Yunjie Zhang A Transformer-Based Multi-Scale Deep Learning Model for Lung Cancer Surgery Optimization IEEE Access Transformer-based model multi-scale deep learning lung cancer surgery risk assessment multi-modal data integration personalized medicine |
| title | A Transformer-Based Multi-Scale Deep Learning Model for Lung Cancer Surgery Optimization |
| title_full | A Transformer-Based Multi-Scale Deep Learning Model for Lung Cancer Surgery Optimization |
| title_fullStr | A Transformer-Based Multi-Scale Deep Learning Model for Lung Cancer Surgery Optimization |
| title_full_unstemmed | A Transformer-Based Multi-Scale Deep Learning Model for Lung Cancer Surgery Optimization |
| title_short | A Transformer-Based Multi-Scale Deep Learning Model for Lung Cancer Surgery Optimization |
| title_sort | transformer based multi scale deep learning model for lung cancer surgery optimization |
| topic | Transformer-based model multi-scale deep learning lung cancer surgery risk assessment multi-modal data integration personalized medicine |
| url | https://ieeexplore.ieee.org/document/10967497/ |
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