Cryobiopsy vs Forceps for Bronchoscopic Lung Biopsy: The FROSTBITE-2 Randomized Clinical Trial

# Cryobiopsy vs Forceps for Bronchoscopic Lung Biopsy: The FROSTBITE-2 Randomized Clinical Trial *Published: 2026 May 18* reported grants from Erbe USA during the conduct of the study and consulting fees from Verathon. Dr Kapp reported grants from Erbe USA during the conduct of the study and consulting fees from Verathon. Dr Illei reported grants from Erbe USA during the conduct of the study and consulting fees from AbbVie, AstraZeneca, Sanofi, Bristol Myers Squibb, and Boehringer Ingelheim. Dr Shofer reported grants from Erbe USA during the conduct of the study. Dr Gilbert reported grants from Erbe USA during the conduct of the study and consulting fees from Olympus. Dr DiBardino reported grants from Erbe USA during the conduct of the study; consulting fees from Intuitive Surgical and Galvanize Therapeutics; grants from Galvanize Therapeutics, Philips, and Johnson & Johnson; and consulting fees from Johnson & Johnson outside the submitted work. Dr DeMaio reported grants from Erbe USA during the conduct of the study and consulting fees from Medtronic. Dr Sethi reported grants from Erbe USA during the conduct of the study and consulting fees from Olympus, Noah Medical, Steris, and BodyVision. Dr Wahidi reported consulting fees from Olympus, Cook Medical, Fujifilm, and Intuitive Surgical. Dr Gillespie reported consulting fees from Cook, Olympus, Intuitive, Noah Medical, and BodyVision. Dr Sachdeva reported grants from Erbe USA during the conduct of the study and consulting fees from Medtronic, Ambu, Cook, Merit, and AstraZeneca. Dr Duke reported grants from Cook Medical and Swim Across America and consulting fees from Intuitive. Dr Lentz reported consulting fees from Intuitive. Dr Vachani reported grants from Optellum Ltd and Median Technologies and consulting fees from Intuitive Surgical. Dr Molena reported consulting fees from AstraZeneca, Intuitive Surgical, Johnson & Johnson, Medela, Merck, and Proteomics. Dr Silvestri reported grants from Olympus and Johnson & Johnson and consulting fees from Olympus. Dr Maldonado reported grants from Erbe USA, Medtronic, and AstraZeneca during the conduct of the study and consulting fees from Medtronic and Galvanize Therapeutics. Dr Yarmus reported grants from Erbe USA during the conduct of the study and consulting fees from Olympus. No other disclosures were reported. 29. JAMA. 2026 May 18:e268044. doi: 10.1001/jama.2026.8044. Online ahead of print. Biomarker-Based Eligibility for Lung Cancer Screening: Validation of the Protein-Based INTEGRAL-Risk Model. Zahed H(1), Feng X(1), Alcala K(1), Smith-Byrne K(2), Moez E(3), Guida F(4), Albanes D(5), Weinstein SJ(5), Arslan AA(6)(7), Cai Q(8), Shu XO(8), Zheng W(8), Chen C(9)(10), Triplette M(11)(12), Tinker LF(10), Langhammer A(13), Nøst TH(14)(15), Hveem K(16), Milne RL(17)(18)(19), Bassett JK(17)(18), Sheikh M(1), Malekzadeh R(20), Wang Y(21), Patel AV(21), Visvanathan K(22)(23), Yuan JM(24)(25), Wang R(26), Koh WP(27), Sesso HD(28), Zhang X(29), Johansson MB(30), Amos C(31), Hung RJ(3)(32), Muller D(33), Robbins HA(1), Johansson M(1). Author information: (1)Early Detection, Prevention, and Infections Branch (EPR), International Agency for Research on Cancer, Lyon, France. (2)Oxford Population Health, Cancer Epidemiology Unit, University of Oxford, Oxford, United Kingdom. (3)Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada. (4)Environment and Lifestyle Epidemiology Branch (ENV), International Agency for Research on Cancer, Lyon, France. (5)Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland. (6)Department of Obstetrics and Gynecology, New York University Grossman School of Medicine, New York, New York. (7)Department of Population Health, New York University Grossman School of Medicine, New York, New York. (8)Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee. (9)Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, Washington. (10)Women's Health Initiative Clinical Coordinating Center, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, Washington. (11)Cancer Prevention Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington. (12)Division of Pulmonary, Critical Care & Sleep Medicine, University of Washington, Seattle. (13)HUNT Research Centre, NTNU - Norwegian University of Science and Technology, Høgskoleringen, Trondheim, Norway. (14)Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromso, Norway. (15)K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU - Norwegian University of Science and Technology, Høgskoleringen, Trondheim, Norway. (16)Department of Public Health and Nursing, NTNU - Norwegian University of Science and Technology, Høgskoleringen, Trondheim, Norway. (17)Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia. (18)Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia. (19)Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia. (20)Digestive Disease Research Center, Shariati Hospital, Tehran University of Medical Science, Tehran, Iran. (21)American Cancer Society, Atlanta, Georgia. (22)Division of Cancer Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. (23)Johns Hopkins Women's Malignancies Program, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. (24)Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania. (25)Cancer Epidemiology and Prevention Program, University of Pittsburgh Medical Center (Division of Cancer Control and Population Sciences, UPMC) Hillman Cancer Centre, University of Pittsburgh, Pittsburgh, Pennsylvania. (26)University of Pittsburgh Medical Center (Division of Cancer Control and Population Sciences, UPMC) Hillman Cancer Centre, University of Pittsburgh, Pittsburgh, Pennsylvania. (27)Yong Loo Lin School of Medicine, National University of Singapore, Singapore. (28)Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts. (29)Yale School of Nursing, Yale University, New Haven, Connecticut. (30)Department of Diagnostics and Intervention, Oncology, Umea University, Umea, Sweden. (31)Cancer Center, Medical School, University of New Mexico, Albuquerque. (32)Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada. (33)Faculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom. Comment in doi: 10.1001/jama.2021.1117. doi: 10.1001/jama.2025.4017. doi: 10.1001/jama.2025.19798. ## 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. DOI: 10.1001/jama.2026.8044 PMCID: PMC13184794 DOI: 10.1001/jama.2026.7908