ALERT!

This site is not optimized for Internet Explorer 8 (or older).

Please upgrade to a newer version of Internet Explorer or use an alternate browser such as Chrome or Firefox.

Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography

Wednesday, April 19, 2023

Submitted by

Source

Source Name: Journal of Clinical Oncology

Author(s)

Peter G. Mikhael, Jeremy Wohlwend, Adam Yala, Ludvig Karstens, Justin Xiang, Angelo K. Takigami, Patrick P. Bourgouin, PuiYee Chan, Sofiane Mrah, Wael Amayri, Yu-Hsiang Juan, Cheng-Ta Yang, Yung-Liang Wan, Gigin Lin, Lecia V. Sequist, Florian J. Fintelmann, and Regina Barzilay

This study used lung cancer screening trial low dose CT scans to develop a model (Sybil) that predicts the likelihood of development of a lung cancer within the next year. The model was then tested using images from independent data sets totaling more than 27,000 patients. Areas under the receiver-operator curves were in the range of 0.85 to 0.95, indicating very good predictive value. The model may help individualize subsequent screening frequency or the need for diagnostic CT.

Add comment

Log in or register to post comments