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Sybil, a machine-learning model for lung cancer risk assessment

Researchers at MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health, Mass General Cancer Center, and Chang Gung Memorial Hospital have developed an artificial intelligence tool named Sybil for lung cancer risk assessment. Sybil analyzes low-dose computed tomography (LDCT) image data without using clinical or demographic data to predict a patient’s risk of developing lung cancer within six years.

Results showed that Sybil could accurately predict a patient’s future lung cancer risk from a single low-dose computed tomography (LDCT)scan. This could help to enable further personalized screening. Researchers note that future study is required to understand Sybil’s clinical applications.

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

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