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Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the United States and Japan
Atilla P Kiraly1, Corbin A Cunningham1, Ryan Najafi1
1From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.).
None:
Purpose To evaluate the impact of an artificial intelligence (AI) assistant for lung cancer screening on multinational clinical workflows. Materials and Methods An AI assistant for lung cancer screening was evaluated on two retrospective randomized multireader multicase studies where 627 (141 cancer-positive cases) low-dose chest CT cases were each read twice (with and without AI assistance) by experienced thoracic radiologists (six U.S.-based or six Japan-based radiologists), resulting in a total of 7524 interpretations. Positive cases were defined as those within 2 years before a pathology-confirmed lung cancer diagnosis. Negative cases were defined as those without any subsequent cancer diagnosis for at least 2 years and were enriched for a spectrum of diverse nodules. The studies measured the readers' level of suspicion (on a 0-100 scale), country-specific screening system scoring categories, and management recommendations. Evaluation metrics included the area under the receiver operating characteristic curve (AUC) for level of suspicion and sensitivity and specificity of recall recommendations. Results With AI assistance, the radiologists' AUC increased by 0.023 (0.70 to 0.72; P = .02) for the U.S. study and by 0.023 (0.93 to 0.96; P = .18) for the Japan study. Scoring system specificity for actionable findings increased 5.5% (57% to 63%; P < .001) for the U.S. study and 6.7% (23% to 30%; P < .001) for the Japan study. There was no evidence of a difference in corresponding sensitivity between unassisted and AI-assisted reads for the U.S. (67.3% to 67.5%; P = .88) and Japan (98% to 100%; P > .99) studies. Corresponding stand-alone AI AUC system performance was 0.75 (95% CI: 0.70, 0.81) and 0.88 (95% CI: 0.78, 0.97) for the U.S.- and Japan-based datasets, respectively. Conclusion The concurrent AI interface improved lung cancer screening specificity in both U.S.- and Japan-based reader studies, meriting further study in additional international screening environments. Keywords: Assistive Artificial Intelligence, Lung Cancer Screening, CT Supplemental material is available for this article. Published under a CC BY 4.0 license.
