ViTR-SP: A CT-based vision transformer model for prediction of pneumonitis in patients with non-small cell lung cancer who received thoracic radiotherapy and immunotherapy.

Abstract

e20034 Background: The combination of thoracic radiotherapy (TRT) and immunotherapy accompanied by the accumulation of pulmonary toxicity. This study aimed to construct a deep radiomics model based on sequential computed tomography (CT) images for radioimmune-associated pneumonitis prediction in non-small cell lung cancer (NSCLC) patients treated with TRT and immune checkpoint inhibitors (ICIs). Methods: Three models were constructed based on a vision transformer (ViT) network using sequential chest CT images, tumor-mask and lung-mask as inputs, respectively. NSCLC patients treated with TRT and ICIs at Shandong Cancer Hospital and Qilu Hospital were included for model training and external validation, respectively. The image-level model was trained based on ViT to identify predictive images, and the image-level probability distribution were performed. Then, a ViT-Recursive Neural Network (ViT-RNN) model framework was constructed at the patient-level to integrate the image-level information to derive the final prediction, which was evaluated in an independent validation cohort. Results: A total of 230 NSCLC patients from Shandong Cancer Hospital were randomly assigned in a 7:3 ratio for model training and validation, respectively. Another 42 patients from Qilu Hospital were used as an independent external validation cohort. The ViT-RNN model for symptomatic pneumonitis (ViTR-SP) based on sequential CT images achieved better area under curve (AUC), sensitivity, and specificity of 0.725, 0.750, and 0.886 than lung-mask based model (0.575, 0.625, and 0.705) and tumor-mask based model (0.658, 0.667, and 0.773) in the internal validation set. In the external validation set, the proposed model achieved the AUC, sensitivity, and specificity of 0.654, 0.818, 0.727, while the lung-mask and tumor-mask based models had the AUC, sensitivity, and specificity of 0.540, 0.545, 0.677, and 0.630, 0.545, 0.742, respectively. The performance of the model was improved after integrating clinical features, with internal and external validation AUC of 0.782 and 0.672, respectively. Conclusions: In conclusion, the clinical combined ViTR-SP neural network risk prediction model based on sequential chest CT images can be used for predicting the risk of developing radioimmune-associated pneumonitis in NSCLC patients.