Background: Non-smokers and individuals with minimal smoking history represent a significant proportion of lung cancer cases but are often overlooked in current risk assessment models. Pulmonary nodules are commonly detected incidentally—appearing in approximately 24–31% of all chest CT scans regardless of smoking status. However, most established risk models, such as the Brock model, were developed using cohorts heavily enriched with individuals who have substantial smoking histories. This limits their generalizability to non-smoking and light-smoking populations, highlighting the need for more inclusive and tailored risk prediction strategies.Purpose: We aimed to develop a longitudinal radiomics-based approach for lung cancer risk prediction, integrating time-varying radiomic modeling to enhance early detection in USPSTF-ineligible patients.Methods: Unlike conventional models that rely on a single scan, we conducted a longitudinal analysis of 122 patients who were later diagnosed with lung cancer, with a total of 622 CT scans analyzed. Of these patients, 69% were former smokers, while 30% had never smoked. Quantitative radiomic features were extracted from serial chest CT scans to capture temporal changes in nodule evolution. A time-varying survival model was implemented to dynamically assess lung cancer risk. Additionally, we evaluated the integration of handcrafted radiomic features and the deep learning-based Sybil model to determine the added value of combining local nodule characteristics with global lung assessments.Results: Our radiomic analysis identified specific CT patterns associated with malignant transformation, including increased nodule size, voxel intensity, textural entropy, as indicators of tumor heterogeneity and progression. Integrating radiomics, delta-radiomics, and longitudinal imaging features resulted in the optimal predictive performance during cross-validation (concordance index [C-index]: 0.69), surpassing that of models using demographics alone (C-index: 0.50) and Sybil alone (C-index: 0.54). Compared to the Brock model (67% accuracy, 100% sensitivity, 33% specificity), our composite risk model achieved 78% accuracy, 89% sensitivity, and 67% specificity, demonstrating improved early cancer risk stratification. Kaplan–Meier curves and individualized cancer development probability functions further validated the model’s ability to track dynamic risk progression for individual patients. Visual analysis of longitudinal CT scans confirmed alignment between predicted risk and evolving nodule characteristics.Conclusions: Our study demonstrates that integrating radiomics, sybil, and clinical factors enhances future lung cancer risk prediction in USPSTF-ineligible patients, outperforming existing models and supporting personalized screening and early intervention strategies.
背景：非吸烟者及吸烟史极少的个体在肺癌病例中占相当比例，但在当前风险评估模型中常被忽视。肺结节通常为偶然发现——无论吸烟状态如何，约24%-31%的胸部CT扫描中可见结节。然而，目前大多数成熟的风险模型（如Brock模型）是基于大量吸烟史人群队列开发的，这限制了其在非吸烟及轻度吸烟人群中的普适性，凸显了需要更具包容性和针对性风险预测策略的必要性。 目的：本研究旨在开发一种基于纵向影像组学的肺癌风险预测方法，通过整合时变影像组学建模，以增强对美国预防服务工作组（USPSTF）筛查标准不适用患者的早期检测能力。 方法：与依赖单次扫描的传统模型不同，我们对122例后续确诊肺癌的患者进行了纵向分析，共纳入622次CT扫描。其中69%为既往吸烟者，30%为从不吸烟者。通过从系列胸部CT扫描中提取定量影像组学特征，捕捉结节演化的时间变化。采用时变生存模型动态评估肺癌风险。此外，我们评估了手工提取影像组学特征与基于深度学习的Sybil模型的整合效果，以探究结合局部结节特征与全肺评估的附加价值。 结果：我们的影像组学分析识别出与恶性转化相关的特定CT模式，包括结节增大、体素强度增加、纹理熵值升高等反映肿瘤异质性和进展的指标。整合影像组学、Δ影像组学及纵向影像特征在交叉验证中获得了最佳预测性能（一致性指数[C-index]：0.69），优于仅使用人口统计学特征的模型（C-index：0.50）和单独使用Sybil的模型（C-index：0.54）。与Brock模型（准确率67%，灵敏度100%，特异度33%）相比，我们的复合风险模型达到78%准确率、89%灵敏度和67%特异度，显示出更优的早期癌症风险分层能力。Kaplan-Meier曲线和个体化癌症发生概率函数进一步验证了该模型追踪个体患者动态风险进展的能力。纵向CT扫描的可视化分析证实了预测风险与结节演变特征的一致性。 结论：本研究证明，整合影像组学、Sybil模型及临床因素可显著提升对USPSTF筛查标准不适用患者的未来肺癌风险预测效能，其表现优于现有模型，为个体化筛查和早期干预策略提供了支持。
Radiomics for Dynamic Lung Cancer Risk Prediction in USPSTF-Ineligible Patients
肿瘤(癌症)患者之家