Cone‐beam CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients. Issue 4 (15th February 2020)

• Record Type: Journal Article
• Authors: Qin, Qingjin; Shi, Anhui; Zhang, Ran; Wen, Qiang; Niu, Tianye; Chen, Jinhu; Qiu, Qingtao; Wan, Yidong; Sun, Xiaorong; Xing, Ligang
• Is Part Of: Thoracic cancer. Volume 11:Issue 4(2020)
• Page Start: 964
• Page End: 972
• Publication Date: 2020-02-15
• Subjects: Cone‐beam CT -- lung toxicity -- non‐small cell lung cancer -- radiomics -- stereotactic body radiotherapy; Chest -- Cancer -- Periodicals; Chest -- Cancer -- Treatment -- Periodicals; Chest -- Surgery -- Periodicals; 616.99494005
• DOI: 10.1111/1759-7714.13349
• Languages: English
• ISSNs: 1759-7706

Abstract

Abstract: Background: Stereotactic body radiotherapy (SBRT) is the standard care for inoperable early stage non‐small cell lung cancer (NSCLC). The purpose of our study was to investigate whether a prediction model based on cone‐beam CT (CBCT) plus pretreatment CT radiomics features could improve the prediction of tumor control and lung toxicity after SBRT in comparison to a model based on pretreatment CT radiomics features alone. Methods: A total of 34 cases of stage I NSCLC patients who received SBRT were included in the study. The pretreatment planning CT and serial CBCT radiomics features were analyzed using the imaging biomarker explorer (IBEX) software platform. Multivariate logistic regression was conducted for the association between progression‐free survival (PFS), lung toxicity and features. The predictive capabilities of the models based on CBCT and CT features were compared using receiver operating characteristic (ROC) curves. Results: Five CBCT features and two planning CT features were correlated with disease progression. Six CBCT features and two planning CT features were related to lung injury. The ROC curves indicated that the model based on the CBCT plus planning CT features might be better than the model based on the planning CT features in predicting lung injury. The other ROC curves indicated that the model based on the planning CT features was similar to the model based on the CBCT plus planning CT features in predicting disease progression. Conclusions: Both pretreatment CT and CBCT radiomics features could predict disease progression and lung injury. A model with CBCT plus pretreatment CT radiomics features might improve the prediction of lung toxicity in comparison with a model with pretreatment CT features alone. Key points: Significant findings of the study : A model with cone‐beam CT radiomics features plus pre‐treatment CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients. What this study adds : In the prediction of PFS and lung toxicity in early‐stage NSCLC patients treated with SBRT, CBCT radiomics could be another effective method.