Biomarker-Based Eligibility for Lung Cancer Screening
Year: 2026
Type: article
Study design: Observational Study
Abstract: Importance: Screening by low-dose computed tomography can reduce lung cancer mortality among high-risk individuals, but many lung cancers occur among individuals with a smoking history who are not eligible for screening. Objective: To develop and validate the protein-based Integrative Analysis of Lung Cancer Risk and Etiology (INTEGRAL)-Risk model in individuals with a smoking history from the general population. Design, Setting, and Participants: Cohorts in the Lung Cancer Cohort Consortium recruited research participants in the US, Europe, Asia, and Australia between 1985 and 2009, who were followed up for lung cancer and other health outcomes until 2021. Fourteen case cohorts of 3695 participants with a smoking history within the Lung Cancer Cohort Consortium, including 2305 randomly sampled participants and 1390 patients diagnosed with lung cancer within 3 years after blood sample collection, were designed. Plasma or serum samples from each participant were assayed using the INTEGRAL protein panel in 2022. The INTEGRAL-Risk model was trained using 7 predefined case cohorts (training set; n = 1951) to estimate absolute risk of being diagnosed with lung cancer based on age, smoking history, and 13 proteins. The validity of the INTEGRAL-Risk model was assessed in 7 independent case cohorts (testing set; n = 1744) at 1, 2, and 3 years after blood collection. Exposure: Absolute risk estimates from the protein-based INTEGRAL-Risk model. Main Outcomes and Measures: The primary outcome was the validity of the INTEGRAL-Risk model in the testing set with respect to discrimination (area under the curve [AUC]) and calibration (ratio of expected-to-observed cases [E/O]). Results: A total of 3695 participants were included, with 1951 participants (including 807 with lung cancer) in the training set and 1744 participants (including 583 with lung cancer) in the testing set. In the combined 14 training and testing sets, after application of statistical weights, 323 570 participants were represented (185 016 [57%] female; median [IQR] age, 60 [51-67] years). In the independent testing set, discrimination of the INTEGRAL-Risk model was highest at 1 year of follow-up and exceeded that of the questionnaire-based PLCOm2012 (Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial) model (INTEGRAL-Risk AUC of 0.88 [95% CI, 0.85-0.91] vs PLCOm2012 AUC of 0.79 [95% CI, 0.75-0.83]; P value for difference <.001). Using a risk threshold to achieve the same specificity as US Preventive Services Task Force (USPSTF) 2021 criteria, the INTEGRAL-Risk model captured 85% of lung cancer cases compared with 63% by USPSTF 2021 and 70% by PLCOm2012. Discrimination of the INTEGRAL-Risk model decreased with longer prediction horizons, with a 2-year AUC of 0.84 (95% CI, 0.81-0.86) and 3-year AUC of 0.81 (95% CI, 0.79-0.83). The model was well calibrated (E/O over 3 years, 0.87 [95% CI, 0.69-1.14]). Conclusions and Relevance: Compared with questionnaire-based approaches, the protein-based INTEGRAL-Risk model improved short-term prediction of lung cancer in people with a smoking history. This model has potential to improve selection of high-risk individuals who are most likely to benefit from lung cancer screening.
Source: JAMA
Authors: Hana Zahed, Xiaoshuang Feng, Karine Alcala, Karl Smith-Byrne, Elham Moez, Florence Guida, Demetrius Albanes, Stephanie J. Weinstein, Alan A. Arslan, Qiuyin Cai, Xiao-Ou Shu, Wei Zheng, Chu Chen, Matthew Triplette, Lesley F. Tinker, Arnulf Langhammer, Therese Haugdahl Nøst, Kristian Hveem, Roger L. Milne, Julie K. Bassett, Mahdi Sheikh, Reza Malekzadeh, Ying Wang, Alpa V. Patel, Kala Visvanathan, Jian-Min Yuan, Renwei Wang, Woon-Puay Koh, Howard D. Sesso, Xuehong Zhang, Mikael B. Johansson, Christopher Amos, Rayjean J. Hung, David Muller, Hilary A. Robbins, Mattias Johansson
Institutions: Centre international de recherche sur le cancer, University of Oxford, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Division of Cancer Epidemiology and Genetics, New York University, Vanderbilt University, Vanderbilt University Medical Center, Fred Hutch Cancer Center, Women's Health Initiative, University of Washington, Norwegian University of Science and Technology, UiT The Arctic University of Norway, The University of Melbourne, Cancer Council Victoria, Monash Health, Monash University, University of Tehran, Shariati Hospital, Tehran University of Medical Sciences, American Cancer Society, Johns Hopkins University, University of Pittsburgh, UPMC Hillman Cancer Center, University of Pittsburgh Medical Center, National University of Singapore, Brigham and Women's Hospital, Yale University, Umeå University, University of New Mexico, University of Toronto, Imperial College London
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funder: National Institutes of Health
OpenAlex ID: W7161543969