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.

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.

Background/Objectives: MET overexpression is common in non-small cell lung cancer (NSCLC). The correlation between MET overexpression and immune checkpoint inhibitor (ICI) efficacy in NSCLC is underexplored. Methods: In this retrospective observational cohort study, we curated a dataset of 279 stage IV NSCLC patients who received ICI treatment and had MET expression assessed by CLIA-certified immunohistochemistry (IHC) assay. MET expression was graded on a scale from 0 to 3+, with overexpression defined as 2+ or 3+. Clinicopathological features associated with MET expression were assessed using logistic regression. Overall survival (OS) and progression-free survival (PFS) were evaluated via Kaplan–Meier analysis and the multivariate Cox proportional hazards model. To derive the most parsimonious and statistically robust models, stepwise refinement based on the Akaike Information Criterion (AIC) was applied. Results: MET overexpression was observed in 220 of 279 patients (78.9%). Adenocarcinoma histology (p = 0.003) and PD-L1 expression (p < 0.001) were independently associated with MET overexpression. Kaplan–Meier analysis showed patients with MET overexpression had significantly superior OS (p = 0.012) and PFS (p = 0.033). Full Cox regression analysis revealed that MET overexpression was associated with longer OS (p = 0.040), independent of PD-L1 levels. Patients with MET overexpression had numerically longer PFS (p = 0.145). Following adjustment for metastatic burden, the AIC-selected OS and PFS models both demonstrated that MET overexpression remained a significant prognostic factor (OS: p = 0.010; PFS: p = 0.026). Conclusions: This real-world study suggests that MET overexpression, as assessed by IHC, is associated with better survival in advanced NSCLC patients treated with ICIs, independent of PD-L1 level. These results suggest the potential of MET expression as a predictive marker for ICI efficacy in advanced NSCLC patients and support the combination of MET-targeted agents with anti-PD1/PD-L1 ICIs as a promising strategy for NSCLC patients with MET overexpression.