Lung cancer is a leading cause of cancer-related deaths worldwide, with its high mortality rate primarily attributed to delayed diagnosis. Radiomics, by extracting abundant quantitative features from medical images, offers novel possibilities for early diagnosis and precise treatment of lung cancer. This article reviewed the latest advancements in radiomics for lung cancer management, particularly its integration with artificial intelligence (AI) to optimize diagnostic processes and personalize treatment strategies. Despite existing challenges, such as non-standardized image acquisition parameters and limitations in model reproducibility, the incorporation of AI significantly enhanced the precision and efficiency of image analysis, thereby improving the prediction of disease progression and the formulation of treatment plans. We emphasized the critical importance of standardizing image acquisition parameters and discussed the role of AI in advancing the clinical application of radiomics, alongside future research directions.
ObjectiveTo evaluate the clinical value of a combined diagnostic model integrating circulating cell-free DNA (cfDNA) methylation markers and CT imaging features for differentiating benign and malignant lung nodules and for early lung cancer detection. This study pioneers a two-step multi-omics modeling approach to construct a robust diagnostic model. MethodsA retrospective cohort of 140 patients (70 malignant and 70 benign, confirmed by postoperative pathology) with lung nodules who underwent surgical treatment at West China Hospital, Sichuan University, from January 2014 to December 2024 was included. Methylation profiles of 54 cfDNA regions were detected via targeted methylation sequencing. CT imaging features (e.g., nodule size, type, and signs) were extracted. A two-step modeling strategy was applied: ① imaging features were modeled directly using binary logistic regression, while methylation features were selected via LASSO regression before modeling; ② a combined model was constructed using the scores from both models. Model performance was evaluated using receiver operating characteristic (ROC) curves, with internal validation via Bootstrap (1000 iterations). ResultsAll patients were split into a training set (n=84) and a test set (n=56). In the test set, the combined model achieved an area under the ROC curve (AUC) of 0.86 [95% confidence interval (CI): 0.74-0.95], with both sensitivity and specificity reaching 82%. This outperformed the individual imaging model (AUC=0.74) and methylation model (AUC=0.82). ConclusionThe multi-omics combined diagnostic model significantly improved the ability to distinguish benign from malignant lung nodules, particularly for early-stage lesions like ground-glass opacities. Its non-invasive and high-sensitivity features provide a promising translational tool for lung cancer screening, with promising clinical application prospects.
Objective For potential patients with better prognosis of non-small-cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) mutations, a simpler and more effective model with easy-to-obtain histopathological parameters was established. MethodsThe computed tomography (CT) images of 158 patients with EGFR-mutant NSCLC who were first diagnosed in West China Hospital of Sichuan University were retrospectively analyzed, and the target areas of the lesions were described. Patients were randomly assigned to either a model training group or a test group.The radiomics features were extracted from the CT images, and the least absolute shrinkage and selection operator (LASSO) regression method was used to screen out the valuable radiomics features. The logistic regression method was used to establish a radiomic model, and the nomogram was used to evaluate the discrimination ability. Finally, the calibration curve, receiver characteristic curve (ROC), Kaplan-Meier curve and decision curve analysis (DCA) were employed to assess model efficacy. ResultsA nomogram combining three important clinical factors : gender, lesion location, treatment, and imaging risk score was established to predict the 3-year, 5-year, and 8-year survival rates of NSCLC patients with EGFR mutation. The calibration curve demonstrated highly consistent between model-predicted survival probabilities and observed overall survival (OS). The area under the curve (AUC) -ROC of the predicted 3-year, 5-year and 8-year OS was 0.70, 0.79 and 0.68, respectively. The Kaplan-Meier curve revealed significant OS disparities when comparing high- and low-risk patient subgroups. The DCA curve showed that the predicted 3-year and 5-year OS increased more clinical benefits than the treatment of all patients or no treatment.ConclusionThe nomogram for predicting the survival prognosis of NSCLC patients with EGFR mutation was constructed and verified, which can effectively predict the survival time range of NSCLC patients, and provide a reference for more individualized treatment decisions for such patients in clinical practice.
