Radiomics for Dynamic Lung Cancer Risk Prediction in USPSTF-Ineligible Patients

作者：Morteza Salehjahromi, Hui Li, Eman Showkatian, Maliazurina B. Saad, Mohamed Qayati, Sohair Ismail, Sheeba J. Sujit, Amgad Muneer, Muhammad Aminu, Lingzhi Hong, Xiaoyu Han, Simon Heeke, Tina Cascone, Xiuning Le, Natalie I. Vokes, Don L. Gibbons, Iakovos Toumazis, Edwin J. Ostrin, Mara B. Antonoff, Ara A. Vaporciyan, David A. Jaffray, Fernando Uliana Kay, Brett W. Carter, Carol C. Wu, Myrna C.B. Godoy, J. Jack Lee, David E. Gerber, John V. Heymach, Jianjun Zhang, Jia Wu · 发表于：Cancers · 年份：2025 · DOI：10.3390/cancers17213406 · 被引用次数：1 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment、Advanced X-ray and CT Imaging

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 ...