Rapid and Noninvasive Early Detection of Lung Cancer by Integration of Machine Learning and Salivary Metabolic Fingerprints Using MS LOC Platform: A Large‐Scale Multicenter Study

Most lung cancer (LC) patients are diagnosed at advanced stages due to the lack of effective screening tools. This multicenter study analyzes 1043 saliva samples (334 LC cases and 709 non-LC cases) using a novel high-throughput platform for metabolic fingerprint acquisition. Machine learning identifies 35 metabolic features distinguishing LC from non-LC subjects, enabling the development of a classification model named SalivaMLD. In the validation set and test set, SalivaMLD demonstrates strong diagnostic performance, achieving an area under the curve of 0.849-0.850, a sensitivity of 81.69-83.33%, and a specificity of 74.23-74.39%, outperforming conventional tumor biomarkers. Notably, SalivaMLD exhibits superior accuracy in distinguishing early stage LC patients. Hence, this rapid and noninvasive screening method may be widely applied in clinical practice for LC detection.

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Publication Details

Journal
Advanced Science
Published
2025-05-14
DOI
https://doi.org/10.1002/advs.202416719
Citations
4
Primary Topic
Head and Neck Cancer Studies
Type
article
Field-Weighted Citation Impact
7.39

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article

Rapid and Noninvasive Early Detection of Lung Cancer by Integration of Machine Learning and Salivary Metabolic Fingerprints Using MS LOC Platform: A Large‐Scale Multicenter Study

Aiwu Pan, Jianmin Wu, Junqi Zhu, Wenhua Liang et al.
4 citations
Advanced Science
Head and Neck Cancer Studies
7.39
article

Rapid and Noninvasive Early Detection of Lung Cancer by Integration of Machine Learning and Salivary Metabolic Fingerprints Using MS LOC Platform: A Large‐Scale Multicenter Study

Aiwu Pan, Jianmin Wu, Junqi Zhu, Wenhua Liang, S. Han, Yuchun Zhong, Ruofeng Yan, Xuqing Huang, Sheng Cao, Yu Jiang, Xiaoming Chen, Peng Ye, Shuang Lin, Peng‐Peng Zhu, Chen Zhao, Yue Gao, Bei Li, Huiting Wang
article en
4 citations

Abstract

Most lung cancer (LC) patients are diagnosed at advanced stages due to the lack of effective screening tools. This multicenter study analyzes 1043 saliva samples (334 LC cases and 709 non-LC cases) using a novel high-throughput platform for metabolic fingerprint acquisition. Machine learning identifies 35 metabolic features distinguishing LC from non-LC subjects, enabling the development of a classification model named SalivaMLD. In the validation set and test set, SalivaMLD demonstrates strong diagnostic performance, achieving an area under the curve of 0.849-0.850, a sensitivity of 81.69-83.33%, and a specificity of 74.23-74.39%, outperforming conventional tumor biomarkers. Notably, SalivaMLD exhibits superior accuracy in distinguishing early stage LC patients. Hence, this rapid and noninvasive screening method may be widely applied in clinical practice for LC detection.

Advanced ScienceVol. 12(22)
Sir Run Run Shaw Hospital (CN), Westlake University (CN), Tongde Hospital of Zhejiang Province (CN), First Affiliated Hospital of Guangzhou Medical University (CN), Affiliated Hospital of Hangzhou Normal University (CN), Community Health Center (US), Affiliated Hangzhou First People's Hospital, Westlake University, School of Medicine (CN), State Key Laboratory of Respiratory Disease (CN), Second Affiliated Hospital of Zhejiang University (CN), First Affiliated Hospital Zhejiang University (CN), Zhejiang University (CN), Guangzhou Medical University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, Science and Technology Program of Zhejiang Province
Good health and well-being
Openalex Percentile: Top 3%
Head and Neck Cancer Studies
7.39
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