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.
背景/目的：持续性肺结节具有较高的肺癌转化风险。评估其未来癌变风险对于成功实施早期干预至关重要。本研究在基于医院的队列中评估了两种持续性结节风险预测模型的性能：基于临床与影像学特征的Brock模型，以及新型深度学习肺癌风险预测模型Sybil。方法：回顾性队列（n=130）与前瞻性队列（n=301）纳入持续性肺结节患者（定义为至少两次间隔三个月的CT扫描均检出且无缩小证据的结节）。我们分析了人口学因素、结节特征与Brock评分的相关性，评估了两种模型的预测效能，并构建机器学习模型以优化本队列的风险评估。结果：在回顾性队列中，Brock评分范围为0%至85.82%。前瞻性队列中，301例患者中有62例确诊肺癌，其Brock评分中位数显著高于未确诊肺癌者（18.65% vs. 4.95%，p<0.001）。肺癌风险与肺癌家族史、结节直径≥10 mm、部分实性结节类型及毛刺征显著相关。Brock模型的AUC为0.679，Sybil模型为0.678。在测试的五种机器学习模型中，逻辑回归模型表现最佳（AUC=0.729）。结论：在真实世界肿瘤医院队列的持续性肺结节患者中，Brock与Sybil模型均具有肺癌风险预测价值，但也存在局限性。优化该人群的预测模型对提升肺癌早期检出与干预水平具有重要意义。
肿瘤(癌症)患者之家