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# Sybil deep learning model and biennial lung cancer screening in low-risk Lung-RADS scans
- URL: https://www.coios.me/sybil-deep-learning-model-and-biennial-lung-cancer/
- Published: 2026-11-08T13:02:31.000Z
- Updated: 2026-11-08T14:12:32.000Z
- Description: In 3,120 scans from one hospital, Sybil moved 33.8% of negative screens to biennial screening with no delayed cancers.
- Author: Daniel Ryan
- Tags: Data, models and systems, AI in medicine, United States, #preprint, #refines, #item, #source-2026-11, #score-62

This study tested whether AI risk scores could pick out Lung-RADS 1 and 2 scans safe for biennial screening. Sybil, an imaging deep learning model, and the Brock model were tested on 3,120 scans from one safety-net hospital. Cut-offs were calibrated to move 30% of negative screens to biennial screening in a National Lung Screening Trial subset. Sybil's overall AUC was 0.819 to 0.912, and 0.746 at 2 years for Lung-RADS 1 and 2\. Its cut-off moved 33.8% of hospital negative screens with no delayed cancers, while Brock's AUC was 0.513\. The analysis is retrospective and from one hospital, with 72 cancers over a median 2.3 years of follow-up.

*Why it is interesting: Lung-RADS 1 and 2 made up 90.5% of scans, so interval decisions drive much of the screening workload. Sybil held up on external data where the established Brock model performed near chance.*

Source

[medRxiv, 4 November 2026](https://doi.org/10.64898/2026.11.03.26367146?ref=coios.me)

Paper

Investigating AI’s potential in risk-stratifying lung cancer screenings for extending screening intervals and reducing workload: A retrospective analysis of a tertiary hospital cohort

Authors

Nowroozi A, Bondarenko M, Brar SS, Haas B, Brown J, Rustagi A et al.

DOI

10.64898/2026.11.03.26367146

Type

Preprint

Design

Retrospective single-centre cohort of lung cancer screening CTs (n=3120, to Sept 2024, median follow-up 2.3 years) with external validation of Sybil and Brock models and bootstrapped simulation of biennial screening intervals

Verdict

Refines prior evidence

Driver

[AI in medicine](https://www.coios.me/d-ai-medicine/)