Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the United States and Japan

Atilla P. Kiraly*, Corbin A. Cunningham, Ryan Najafi, Zaid Nabulsi, Jie Yang, Charles Lau, Joseph R. Ledsam, Wenxing Ye, Diego Ardila, Scott M. McKinney, Rory Pilgrim, Yun Liu, Hiroaki Saito, Yasuteru Shimamura, Mozziyar Etemadi, David Melnick, Sunny Jansen, Greg S. Corrado, Lily Peng, Daniel TseShravya Shetty, Shruthi Prabhakara, David P. Nadich, Neeral Beladia, Krish Eswaran

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article numbere230079
JournalRadiology: Artificial Intelligence
Volume6
Issue number3
DOIs
StatePublished - May 2024

Keywords

  • Assistive Artificial Intelligence
  • CT
  • Lung Cancer Screening

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Artificial Intelligence

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