The deep learning model, known as Sybil, predicts future lung cancer risk based on a single low-dose chest CT and can identify individuals who would benefit from regular monitoring. LungCancer OncTwitter
Lung cancer screening for current or former smokers and others at high risk of developing lung cancer reduces deaths from lung cancer, but at present these screening programs capture only a fraction of the individuals who could benefit.Sybil is a validated deep learning model that predicts future lung cancer risk based on a single low-dose CT scan, they explain.
"On the basis of our initial results, one potential clinical application is to use Sybil to decrease follow-up scans or biopsies among patients with nodules that are low risk," they concluded. First, however, it will be necessary to"gain confidence" that the model is generalizable, because 92% of patients in the NLST development dataset were White, they said., two experts explore the potential for Sybil to accelerate widespread lung cancer screening implementation. The editorialists are Gerard A. Silvestri, MD, of the Medical University of South Carolina, Charleston, and James R. Jett, MD, Emeritus, National Jewish Health, Denver, and Biodesix, Inc, Boulder.
"Even more worrisome, a recent study found that in patients with abnormal/suspicious findings on their screening LDCT, follow-up testing occurred in only 42% of the patients, suggesting the possibility of delays in diagnosis which might result in patients who would have had early stage resectable disease being diagnosed with advanced cancer and the resultant poorer outcomes," they added.
One advantage with Sybil is that"[i]n theory, the model code can be run in real time in the background of any radiology reading station and act as a second reader."
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