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

作者：Hui Li, Morteza Salehjahromi, Myrna C.B. Godoy, Kang Qin, Courtney M. Plummer, Zheng Zhang, Lingzhi Hong, Simon Heeke, Xiuning Le, Natalie I. Vokes, Bingnan Zhang, Haniel A. Araújo, 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 · 发表于：Cancers · 年份：2025 · DOI：10.3390/cancers17091499 · 被引用次数：5 · 研究领域：Lung Cancer Diagnosis and Treatment、Lung Cancer Treatments and Mutations、Lung Cancer Research Studies

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. Conclus...