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MA19.03 Sex Differences in the Prediction of Future Lung Cancer Risk Based on a Single Low-Dose Chest Computed Tomography Scan

Year: 2023

Type: conference-abstract

Abstract: A validated open-access deep learning algorithm called Sybil can accurately predict long-term lung cancer risk from a single low-dose chest computed tomography (LDCT) scan and its accuracy exceeds clinical risk assessment. Sybil was trained on predominantly (60%) men. The use of artificial intelligence algorithms trained on imbalanced cohorts may lead to inequitable outcomes in real-world settings. We aimed to study whether Sybil works equally well in both sexes.

Source: Journal of Thoracic Oncology

DOI: 10.1016/j.jtho.2023.09.253

Authors: J. Simon, P. Mikhael, I. Tahir, A. Graur, A. Fata, J.-A. Shepard, F. Jacobson, R. Barzilay, L. Sequist, L. Pace, F.J. Fintelmann

Institutions: Massachusetts General Hospital, Massachusetts Institute of Technology, Brigham and Women's Hospital

Language: English

Topic: Lung Cancer Diagnosis and Treatment

Subfield: Pulmonary and Respiratory Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $4,000

OpenAlex ID: W4388076758

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