External validation of the Sybil risk model as a tool to identify low-risk individuals eligible for biennial lung cancer screening

F. van der Graaf, N. Antonissen, Z. Saghir, M. Prokop and C. Jacobs

European Congress of Radiology 2024.

Purpose: Lung cancer screening protocols for follow up intervals should minimise harm, maximise cost-effectiveness, and avoid diagnostic delays. ILST suggests biennial follow-up for low-risk participants. The study aimed to retrospectively evaluate Sybil, a deep learning algorithm predicting lung cancer risk for 6 years from one LDCT, comparing it to PanCan2b for identifying biennial screening eligibility.

Methods and materials: DLCST baseline scans included 1870 non-cancer and 25 screen-detected cancer cases, diagnosed within 2 years. Sybil (scan level) and PanCan2b (per nodule) predicted risk of developing cancer within 2 years. For cases with no screen-annotated nodules, the PanCan2b risk score for participants was set as 0%. For both models, we used a nodule-risk cut-off of <1.5% to identify low-risk participants for biennial follow-up, based on ILST. For PanCan2b, the risk dominant nodule per scan was considered.

Results: The Sybil and PanCan2B models identified 1616 and 1697 individuals, respectively, meeting the criteria for biennial screening. This would result in a reduction of 87% and 94% of CT scans in the second screening round, respectively. The group referred for biennial screening included 8 and 9 cancers for Sybil and PanCan2B, respectively.

Conclusion: Both Sybil and PanCan2B selected a large group of low-risk participants for biennial screening when a <1.5% risk threshold was used at baseline CT. The difference between Sybil and the PanCan2b model is small. More research is needed to study the type of cancers with delayed diagnosis and whether such delay leads to diagnostic stage shift. In addition, more external validation of the Sybil model on other datasets is necessary to further assess its applicability in lung cancer screening, and to evaluate its performance on follow-up imaging.

Limitations: This study is a baseline, retrospective analysis on data from one screening trial.