Lung Cancer Risk Prediction in Patients with Persistent Pulmonary Nodules Using the Brock Model and Sybil Model

Authors

Hui Li
Morteza Salehjahromi
Myrna C B Godoy
Kang Qin
Courtney M Plummer
Zheng Zhang
Lingzhi Hong
Simon Heeke
Xiuning Le
Natalie Vokes
Bingnan Zhang
Haniel A Araujo
Mehmet Altan
Carol C Wu
Mara B Antonoff
Edwin J Ostrin
Don L Gibbons
John V Heymach
J Jack Lee
David E Gerber
Jia Wu
Jianjun Zhang

Publication Date

4-29-2025

Journal

Cancers

Abstract

Background/objectives: Persistent pulmonary nodules are at higher risk of developing into lung cancers. Assessing their future cancer risk is essential for successful interception. We evaluated the performance of two risk prediction models for persistent nodules in hospital-based cohorts: the Brock model, based on clinical and radiological characteristics, and the Sybil model, a novel deep learning model for lung cancer risk prediction.

Methods: Patients with persistent pulmonary nodules-defined as nodules detected on at least two computed tomography (CT) scans, three months apart, without evidence of shrinkage-were included in the retrospective (n = 130) and prospective (n = 301) cohorts. We analyzed the correlations between demographic factors, nodule characteristics, and Brock scores and assessed the performance of both models. We also built machine learning models to refine the risk assessment for our cohort.

Results: In the retrospective cohort, Brock scores ranged from 0% to 85.82%. In the prospective cohort, 62 of 301 patients were diagnosed with lung cancer, displaying higher median Brock scores than those without lung cancer diagnosis (18.65% vs. 4.95%, p < 0.001). Family history, nodule size ≥10 mm, part-solid nodule types, and spiculation were associated with the risks of lung cancer. The Brock model had an AUC of 0.679, and Sybil's AUC was 0.678. We tested five machine learning models, and the logistic regression model achieved the highest AUC at 0.729.

Conclusions: For patients with persistent pulmonary nodules in real-world cancer hospital-based cohorts, both the Brock and Sybil models had values and limitations for lung cancer risk prediction. Optimizing predictive models in this population is crucial for improving early lung cancer detection and interception.

Keywords

brock model, lung cancer risk assessment, persistent pulmonary nodules, precancer interception, sybil model

DOI

10.3390/cancers17091499

PMID

40361426

PMCID

PMC12070823

PubMedCentral® Posted Date

4-29-2025

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes