Radiomics-Based Assessment of Radiation-Induced Lung Injury after Stereotactic Body Radiotherapy

Abstract Background Over 50% of patients who receive stereotactic body radiotherapy (SBRT) develop radiographic evidence of radiation-induced lung injury. Radiomics is an emerging approach that extracts quantitative features from image data, which may provide greater value and a better understanding of pulmonary toxicity than conventional approaches. We aimed to investigate the potential of computed tomography (CT)-based radiomics in characterizing post-SBRT lung injury. Methods 48 diagnostic thoracic CT scans (acquired prior to SBRT and at 3-, 6- and 9-month post-SBRT) from 14 patients were analyzed. Nine radiomic features, i.e. , seven gray level co-occurrence matrix (GLCM) texture features and two first-order features, were investigated. The ability of radiomic features to distinguish radiation oncologist-defined moderate/severe lung injury from none/mild lung injury was assessed using logistic regression and area under the receiver operating characteristic curve (AUC). Moreover, dose-response curves (DRCs) for radiomic feature changes were determined as a function of time to investigate whether there was a significant dose-response relationship. Results The GLCM features (logistic regression p -value range 0.012-0.262, AUC range 0.643-0.750) outperformed the first-order features ( p -value range 0.100-0.990, AUC range 0.543-0.661) in distinguishing lung injury severity levels. Eight of nine radiomic features demonstrated a significant dose-response relationship at 3-, 6- and 9-month post-SBRT. Although not statistically significant, the GLCM features showed clear separations between the 3- or 6-month DRC and 9-month DRC. Conclusions Radiomic features significantly correlated with radiation oncologist-scored post-SBRT lung injury and showed a significant dose-response relationship, suggesting the potential for radiomics to provide a quantitative, objective measurement of post-SBRT lung injury.