A panel of four protein tumor markers for effective and affordable lung cancer early detection by artificial intelligence.

8028 Background: Lung cancer is the most common and deadly malignancy 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 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 showed 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). Sensitivities of limited- and extensive-stage SCLC were 91.3% and 93.0%. The subtype classification accuracy was 77.4%. Compared with the other blood-based lung cancer early detection tests like OncImmune’s EarlyCDT-Lung (41.0% sensitivity, 91.0% specificity) and DELFI’s FirstLook-Lung (84.1% sensitivity, 50.9% specificity), LungCanSeek's performance was superior. LDCT had 93.1% sensitivity and 76.5% specificity in NLST study. 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 1.2% lung cancer incidence. While LungCanSeek reduced false positives by 2.4-fold to 862,524 compared to 2,089,620 with LDCT, the two-step approach further lowered false positives by 10.3-fold to just 202,693. Additionally, LDCT’s total cost was $2,493 million, exceeding LungCanSeek’s $720 million by 3.5-fold and two-step’s $978.5 million by 2.5-fold. Conclusions: LungCanSeek is a non-invasive, easy to perform, cost-effective (reagent cost $15) and robust test for lung cancer early detection. It also provides accurate subtype prediction that may guide patients’ clinical management and monitor subtype switching during treatment. The two-step approach not only effectively reduces LDCT’s high false positives but also yields substantial economic benefits, making it a cost-effective strategy for population-wide lung cancer screening.

Authors

Institutions

Publication Details

Journal
Journal of Clinical Oncology
Published
2025-05-28
DOI
https://doi.org/10.1200/jco.2025.43.16_suppl.8028
Primary Topic
Genetics, Bioinformatics, and Biomedical Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A panel of four protein tumor markers for effective and affordable lung cancer early detection by artificial intelligence.

Yi Luan, Mao Mao, Dandan Zhu, Qin Zhao et al.
Journal of Clinical Oncology
Genetics, Bioinformatics, and Biomedical Research
article

A panel of four protein tumor markers for effective and affordable lung cancer early detection by artificial intelligence.

Yi Luan, Mao Mao, Dandan Zhu, Qin Zhao, Wei Wu, Wenjian Wang, Shiyong Li, Bing Wei, Yuxin Cheng, Yang Ding, Peiliang Geng
article en

Abstract

8028 Background: Lung cancer is the most common and deadly malignancy 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 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 showed 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). Sensitivities of limited- and extensive-stage SCLC were 91.3% and 93.0%. The subtype classification accuracy was 77.4%. Compared with the other blood-based lung cancer early detection tests like OncImmune’s EarlyCDT-Lung (41.0% sensitivity, 91.0% specificity) and DELFI’s FirstLook-Lung (84.1% sensitivity, 50.9% specificity), LungCanSeek's performance was superior. LDCT had 93.1% sensitivity and 76.5% specificity in NLST study. 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 1.2% lung cancer incidence. While LungCanSeek reduced false positives by 2.4-fold to 862,524 compared to 2,089,620 with LDCT, the two-step approach further lowered false positives by 10.3-fold to just 202,693. Additionally, LDCT’s total cost was $2,493 million, exceeding LungCanSeek’s $720 million by 3.5-fold and two-step’s $978.5 million by 2.5-fold. Conclusions: LungCanSeek is a non-invasive, easy to perform, cost-effective (reagent cost $15) and robust test for lung cancer early detection. It also provides accurate subtype prediction that may guide patients’ clinical management and monitor subtype switching during treatment. The two-step approach not only effectively reduces LDCT’s high false positives but also yields substantial economic benefits, making it a cost-effective strategy for population-wide lung cancer screening.

Journal of Clinical OncologyVol. 43(16_suppl)
Sun Yat-sen University (CN), Zhengzhou University (CN), Sun Yat-sen Memorial Hospital (CN), Henan Cancer Hospital (CN)
Openalex Percentile: Top 89%
Genetics, Bioinformatics, and Biomedical Research
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.