Multi-Omics Models Predict Prognosis for Non-Small Cell Lung Cancer Patients Followed Chemotherapy and Radiotherapy: A Multi-Center Study

Background and purpose Quantifying tumor heterogeneity from various dimensions was helpful for precise treatment. This study aimed to develop and validate multi-omics models based on the computed tomography images, pathological images, dose and clinical information for predicting treatment response and overall survival of non-small cell lung cancer (NSCLC) patients followed chemotherapy and radiotherapy.Materials and methods This retrospective study included 220 NSCLC patients from three centers. After feature extraction and selection, single-omics and multi-omics models were built for treatment response prediction, with model's performance evaluated through area under the curve and box plots. For overall survival analysis, model evaluation incorporated area under the curve, concordance index and calibration curves. The shapley values calculated the contribution of different features to the models.Results Multi-omics models consistently exhibited superior discriminative ability compared to single-omics models in predicting treatment response and overall survival. For treatment response, the multi-omics model achieved area under the curve of 0.85, 0.81, and 0.87 in the training set, internal validation set, and external validation set, respectively. In the analysis of overall survival, the area under the curve and concordance index of the multi-omics model were 0.83/0.79, 0.74/0.74, and 0.73/0.72 in the training set, internal validation set, and external validation set, respectively. Conclusion Multi-omics prediction models demonstrated superior predictive ability with robustness and strong biological interpretability. By predicting treatment response and overall survival in NSCLC patients, these models had the potential to assist clinician optimizing treatment plans, supporting individualized treatment strategies, further improving tumor control probability and prolonging the patients' survival.