Abstract C124: An effective and affordable blood test for lung cancer early detection using four protein markers and artificial intelligence

Abstract Background: Lung cancer is the leading cause of cancer incidence and mortality worldwide. While low-dose computed tomography (LDCT) reduces mortality in high-risk populations, its high false-positive rate and the required specialized infrastructure and radiologists limit its application. This study assesses LungCanSeek, a novel blood-based protein test for lung cancer early detection. Methods: This retrospective study enrolled 1,814 participants (1,095 lung cancer, 719 non-cancer) from three independent cohorts. Blood samples were analyzed for four protein tumor markers (PTMs) using Roche cobas. Artificial intelligence (AI) algorithms were developed for lung cancer detection and subtype classification: lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and small cell lung cancer (SCLC). A two-step lung cancer screening approach was modeled, using LungCanSeek for initial screening, followed by LDCT for LungCanSeek's positive cases. Results: LungCanSeek demonstrated 83.5% sensitivity, 90.3% specificity, and 86.2% accuracy overall. Sensitivities of LUAD, LUSC, and SCLC were 83.3%, 81.4%, and 91.9%. Sensitivity increased with clinical stage in non-small cell lung cancer (NSCLC): 59.5% (I), 69.8% (II), 86.5% (III), and 91.3% (IV). For limited- and extensive-stage SCLC, sensitivities were 91.3% and 93.0%. The subtype classification accuracy was 77.4%. Compared with other blood-based lung cancer early detection tests like OncImmune’s EarlyCDT-Lung (41.0% sensitivity, 91.0% specificity) and DELFI’s FirstLook-Lung (84.0% sensitivity, 50.9% specificity), LungCanSeek showed superior performance. A screening was modeled for 9 million high-risk adults, based on the number of 15 million eligible individuals in the USA in 2024 at a 60% rate, with a 2.5% lung cancer incidence. LungCanSeek reduced false positives by 2.4-fold to 851,175 compared to 2,062,125 with LDCT. The two-step approach further cut false positives by 10.3-fold to just 200,026. LDCT’s total cost was $2,493 million, exceeding LungCanSeek’s $720 million and two-step’s $996.5 million. Conclusions: LungCanSeek is a non-invasive, easy to perform, cost-effective (reagent cost $15) and robust test for lung cancer early detection. The two-step approach offers a cost-effective strategy for population-wide lung cancer screening. Citation Format: Mao Mao, Wei Bing, Wang Wen. jian, Geng Shuai. peng, Wu Wei, Ding Chen. yu, Zhu Dan. dan, Cheng Shuo. yao, Zhao Qiu. rong, Luan Yi, Li Shi. yong. An effective and affordable blood test for lung cancer early detection using four protein markers and artificial intelligence [abstract]. In: Proceedings of the 18th AACR Conference on the Science of Cancer Health Disparities; 2025 Sep 18-21; Baltimore, MD. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2025;34(9 Suppl):Abstract nr C124.

Authors

Institutions

Publication Details

Journal
Cancer Epidemiology Biomarkers & Prevention
Published
2025-09-18
DOI
https://doi.org/10.1158/1538-7755.disp25-c124
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract C124: An effective and affordable blood test for lung cancer early detection using four protein markers and artificial intelligence

Christie Yao, Yi Luan, Wei Wu, Rong Zhao et al.
Cancer Epidemiology Biomarkers & Prevention
Machine Learning in Bioinformatics
article

Abstract C124: An effective and affordable blood test for lung cancer early detection using four protein markers and artificial intelligence

Christie Yao, Yi Luan, Wei Wu, Rong Zhao, Bing Wei, Dan Zhu, Yong Li, Wang Jian, Mao Mao, G. Peng, D. Yu
article en

Abstract

Abstract Background: Lung cancer is the leading cause of cancer incidence and mortality worldwide. While low-dose computed tomography (LDCT) reduces mortality in high-risk populations, its high false-positive rate and the required specialized infrastructure and radiologists limit its application. This study assesses LungCanSeek, a novel blood-based protein test for lung cancer early detection. Methods: This retrospective study enrolled 1,814 participants (1,095 lung cancer, 719 non-cancer) from three independent cohorts. Blood samples were analyzed for four protein tumor markers (PTMs) using Roche cobas. Artificial intelligence (AI) algorithms were developed for lung cancer detection and subtype classification: lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and small cell lung cancer (SCLC). A two-step lung cancer screening approach was modeled, using LungCanSeek for initial screening, followed by LDCT for LungCanSeek's positive cases. Results: LungCanSeek demonstrated 83.5% sensitivity, 90.3% specificity, and 86.2% accuracy overall. Sensitivities of LUAD, LUSC, and SCLC were 83.3%, 81.4%, and 91.9%. Sensitivity increased with clinical stage in non-small cell lung cancer (NSCLC): 59.5% (I), 69.8% (II), 86.5% (III), and 91.3% (IV). For limited- and extensive-stage SCLC, sensitivities were 91.3% and 93.0%. The subtype classification accuracy was 77.4%. Compared with other blood-based lung cancer early detection tests like OncImmune’s EarlyCDT-Lung (41.0% sensitivity, 91.0% specificity) and DELFI’s FirstLook-Lung (84.0% sensitivity, 50.9% specificity), LungCanSeek showed superior performance. A screening was modeled for 9 million high-risk adults, based on the number of 15 million eligible individuals in the USA in 2024 at a 60% rate, with a 2.5% lung cancer incidence. LungCanSeek reduced false positives by 2.4-fold to 851,175 compared to 2,062,125 with LDCT. The two-step approach further cut false positives by 10.3-fold to just 200,026. LDCT’s total cost was $2,493 million, exceeding LungCanSeek’s $720 million and two-step’s $996.5 million. Conclusions: LungCanSeek is a non-invasive, easy to perform, cost-effective (reagent cost $15) and robust test for lung cancer early detection. The two-step approach offers a cost-effective strategy for population-wide lung cancer screening. Citation Format: Mao Mao, Wei Bing, Wang Wen. jian, Geng Shuai. peng, Wu Wei, Ding Chen. yu, Zhu Dan. dan, Cheng Shuo. yao, Zhao Qiu. rong, Luan Yi, Li Shi. yong. An effective and affordable blood test for lung cancer early detection using four protein markers and artificial intelligence [abstract]. In: Proceedings of the 18th AACR Conference on the Science of Cancer Health Disparities; 2025 Sep 18-21; Baltimore, MD. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2025;34(9 Suppl):Abstract nr C124.

Cancer Epidemiology Biomarkers & PreventionVol. 34(9_Supplement)
Sun Yat-sen University (CN), Yonsei University (KR), BioClinica (United States) (US), Sun Yat-sen Memorial Hospital (CN), Henan Cancer Hospital (CN)
Openalex Percentile: Top 78%
Machine Learning in Bioinformatics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.