Figure 3:

Examples of Sybil, an open-source deep learning model, correctly predicting lung cancers (visible lung cancer at baseline CT). (A) Axial low-dose CT image of the lungs in a 54-year-old heavy-smoking man shows a 1.5-cm subsolid nodule in the right lower lobe, with the (B) attention map focusing on this area (arrow). The Sybil lung risk scores were 13.1% for 1-year risk, 20.2% for 2-year risk, 20.7% for 3-year risk, 23.6% for 4-year risk, 24.8% for 5-year risk, and 31.2% for 6-year risk. (C) Axial CT image after 2 years shows that the subsolid nodule had grown to 2.0 cm and was subsequently diagnosed as adenocarcinoma (arrow). The Sybil lung risk score is a deep learning–based risk score for predicting future lung cancer risk within 6 years from a low-dose chest CT scan.