Reevaluation of missed lung cancer with artificial intelligence

Serge Sicular, Mehmet Alpaslan, Francis A. Ortega, Nora Keathley, Srivas Venkatesh, Rebecca M. Jones, Robert V. Lindsey

Research output: Contribution to journalArticlepeer-review

Abstract

Lung cancer is often missed on chest radiographs, despite chest radiography typically being the first imaging modality in the diagnosis pathway. We present a 46 year-old male with chest pain referred for chest X-ray, and initial interpretation reported no abnormality within the patient's lungs. The patient was discharged but returned 4 months later with persistent and worsening symptoms. At this time, chest X-ray was again performed and revealed an enlarging left perihilar mass with post-obstructive atelectasis in the left lower lobe. Follow-up chest computerized tomography scan confirmed lung cancer with post-obstructive atelectasis, and subsequent bronchoscopy-assisted biopsy confirmed squamous cell carcinoma. Retrospective analysis of the initial chest radiograph, which had reported normal findings, was performed with Chest-CAD, a Food and Drug Administration (FDA) cleared computer-assisted detection (CAD) software device that analyzes chest radiograph studies using artificial intelligence. The device highlighted the perihilar region of the left lung as suspicious. Additional information provided by artificial intelligence software holds promise to prevent missed detection of lung cancer on chest radiographs.

Original languageEnglish
Article number101733
JournalRespiratory Medicine Case Reports
Volume39
DOIs
StatePublished - Jan 2022

Keywords

  • Artificial intelligence
  • Chest radiograph
  • Lung cancer
  • Machine learning
  • Misdiagnosis

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