BACKGROUND: Patients with identical stages or similar tumor volumes can vary significantly in their responses to radiotherapy (RT) due to individual characteristics, making personalized RT for non-small cell lung cancer (NSCLC) challenging. This study aimed to develop a deep learning (DL) model by integrating pre- and mid-treatment computed tomography (CT) to predict the treatment response in NSCLC patients. METHODS AND MATERIAL: We retrospectively collected data from 168 NSCLC patients across three hospitals. Data from A (35 patients) and B (93 patients) were used for model training and internal validation, while data from C (40 patients) was used for external validation. DL, radiomics, and clinical features were extracted to establish a varying time-interval long short-term memory network (VTI-LSTM) for response prediction. Furthermore, we derived a model-deduced personalize dose escalation (DE) for patients predicted to have suboptimal gross tumor volume (GTV) regression. The area under the receiver operating characteristic curve (AUC) and predicted absolute error (PAE) were used to evaluate the predictive Response Evaluation Criteria in Solid Tumors (RECIST) classification and proportion of GTV residual. DE was calculated as biological equivalent dose (BED) using an α/β ratio of 10 Gy. RESULTS: The model using only pre-treatment CT achieved the highest AUC of 0.762 and 0.687 in internal and external validation respectively, while the model integrating both pre- and mid-treatment CT achieved AUC of 0.869 and 0.798, with PAE of 0.137 and 0.185. We performed personalized DE for 29 patients. Their original BED was approximately 72 Gy, within the range of 71.6 Gy to 75 Gy. DE ranged from 77.7 to 120 Gy for 29 patients, with 17 patients exceeding 100 Gy and eight patients reaching the model's preset upper limit of 120 Gy. CONCLUSIONS: Combining pre- and mid-treatment CT enhances prediction performance for RT response and offers a promising approach for personalized DE in NSCLC.