Computed tomography and radiation dose images-based deep-learning model for predicting radiation pneumonitis in lung cancer patients after radiation therapy. (May 2023)

• Record Type: Journal Article
• Authors: Zhang, Zhen; Wang, Zhixiang; Luo, Tianchen; Yan, Meng; Dekker, Andre; De Ruysscher, Dirk; Traverso, Alberto; Wee, Leonard; Zhao, Lujun
• Is Part Of: Radiotherapy and oncology. Volume 182(2023)
• Publication Date: 2023-05
• Subjects: Radiotherapy -- Radiation pneumonitis -- Deep learning -- Artificial intelligence -- Actuarial outcome models; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2023.109581
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: CT and radiation dose can play synergistically with each other to model. Deep learning methods have higher predictive power than traditional methods. The predictive logic of deep learning can be inferred by activation maps. Simple tuning method can be used in deep learning model to adapt to different data. Abstract: Purpose: To develop a deep learning model that combines CT and radiation dose (RD) images to predict the occurrence of radiation pneumonitis (RP) in lung cancer patients who received radical (chemo)radiotherapy. Methods: CT, RD images and clinical parameters were obtained from 314 retrospectively-collected patients (training set) and 35 prospectively-collected patients (test-set-1) who were diagnosed with lung cancer and received radical radiotherapy in the dose range of 50 Gy and 70 Gy. Another 194 (60 Gy group, test-set-2) and 158 (74 Gy group, test-set-3) patients from the clinical trial RTOG 0617 were used for external validation. A ResNet architecture was used to develop a prediction model that combines CT and RD features. Thereafter, the CT and RD weights were adjusted by using 40 patients from test-set-2 or 3 to accommodate cohorts with different clinical settings or dose delivery patterns. Visual interpretation was implemented using a gradient-weighted class activation map (grad-CAM) to observe the area of model attention during the prediction process. To improve the usability, ready-to-use online software was developed. Results: The discriminative ability of a baseline trained model had an AUC of 0.83 for test-set-1, 0.55 for test-set-2, and 0.63 for test-set-3. After adjusting CT and RD weights of the model using a subset of the RTOG-0617 subjects, the discriminatory power of test-set-2 and 3 improved to AUC 0.65 and AUC 0.70, respectively. Grad-CAM showed the regions of interest to the model that contribute to the prediction of RP. Conclusion: A novel deep learning approach combining CT and RD images can effectively and accurately predict the occurrence of RP, and this model can be adjusted easily to fit new cohorts.