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

Wednesday, April 19, 2023

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Source Name: Journal of Clinical Oncology


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.

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