Establishing a Deep Learning Model That Integrates Pretreatment and Midtreatment Computed Tomography to Predict Treatment Response in Non-Small Cell Lung Cancer.
Authors
Affiliations (10)
Affiliations (10)
- Department of Radiation Oncology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.
- Department of Radiation Oncology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University, Shanxi, China.
- Department of Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
- Department of Radiation Oncology, Shanxi YK Healthcare General Hospital, Shanxi, China.
- Department of Radiation Oncology, Linfen Central Hospital, Shanxi, China.
- Department of Oncology, The First People's Hospital of Hefei, The Third Affiliated Hospital of Anhui Medical University, Anhui, China.
- Department of Radiation Oncology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University, Shanxi, China. Electronic address: [email protected].
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China. Electronic address: [email protected].
- Department of Radiation Oncology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China. Electronic address: [email protected].
Abstract
Patients with identical stages or similar tumor volumes can vary significantly in their responses to radiation therapy (RT) due to individual characteristics, making personalized RT for non-small cell lung cancer (NSCLC) challenging. This study aimed to develop a deep learning model by integrating pretreatment and midtreatment computed tomography (CT) to predict the treatment response in NSCLC patients. We retrospectively collected data from 168 NSCLC patients across 3 hospitals. Data from Shanghai General Hospital (SGH, 35 patients) and Shanxi Cancer Hospital (SCH, 93 patients) were used for model training and internal validation, while data from Linfen Central Hospital (LCH, 40 patients) were used for external validation. Deep learning, radiomics, and clinical features were extracted to establish a varying time interval long short-term memory network for response prediction. Furthermore, we derived a model-deduced personalize dose escalation (DE) for patients predicted to have suboptimal gross tumor volume regression. The area under the receiver operating characteristic curve (AUC) and predicted absolute error were used to evaluate the predictive Response Evaluation Criteria in Solid Tumors classification and the proportion of gross tumor volume residual. DE was calculated as the biological equivalent dose using an /α/β ratio of 10 Gy. The model using only pretreatment CT achieved the highest AUC of 0.762 and 0.687 in internal and external validation respectively, whereas the model integrating both pretreatment and midtreatment CT achieved AUC of 0.869 and 0.798, with predicted absolute error of 0.137 and 0.185, respectively. We performed personalized DE for 29 patients. Their original biological equivalent dose 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 8 patients reaching the model's preset upper limit of 120 Gy. Combining pretreatment and midtreatment CT enhances prediction performance for RT response and offers a promising approach for personalized DE in NSCLC.