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Research
Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography
From the abstract: Low-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalised future cancer risk assessment could focus approaches toward those most likely to benefit. We hypothesised that a deep learning model assessing the entire volumetric LDCT data could be built to predict individual risk without requiring additional demographic or clinical data.
Details
author(s)
Adam Yala
Regina Barzilay
publication date
12 January 2023
source
Journal of Clinical Oncology
related programme
MIT Jameel Clinic
Link to publication
External link ->
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