Use of radiomics and dosiomics to identify predictors of radiation-induced lung injury

• Published in: Digital diagnostics 2024-11, Vol.5 (4), p.752-764
• Main Authors: Nudnov, Nikolay V., Sotnikov, Vladimir M., Ivannikov, Mikhail E., Shakhvalieva, Elina S.-A., Borisov, Aleksandr A., Ledenev, Vasiliy V., Smyslov, Aleksei Yu, Ananina, Alina V.
• Format: Article
• Language: English
• ISSN: 2712-8490; 2712-8962
• DOI: 10.17816/DD629352

BACKGROUND: Radiomics is a machine learning-based technology that extracts, analyzes, and interprets quantitative features from digital medical images. In recent years, dosiomics has become an increasingly common term in the literature to describe a new radiomics method. Dosiomics is a texture analysis method for evaluating radiotherapy dose distribution patterns. Most of the published research in dosiomics evaluates its use in predicting radiation-induced lung injury. AIM: The aim of the study was to identify predictors (biomarkers) of radiation-induced lung injury using texture analysis of computed tomography (CT) images of lungs and chest soft tissues using radiomics and dosiomics. MATERIALS AND METHODS: The study used data from 36 women with breast cancer who received postoperative conformal radiation therapy. Retrospectively, the patients were divided into two groups according to the severity of post-radiation lung lesions. 3D Slicer was used to evaluate CT results of all patients obtained during radiation treatment planning and radiation dose distribution patterns. The software was able to unload radiomic and dosiomic features from regions of interest. The regions of interest included chest soft tissue and lung areas on the irradiated side where the dose burden exceeded 3 and 10 Gy. RESULTS: The first group included 13 patients with minimal radiation-induced lung lesions, and the second group included 23 patients with post-radiation pneumofibrosis. In the lung area on the side irradiated with more than 3 Gy, statistically significant differences between the patient groups were obtained for three radiomic features and one dosiomic feature. In the lung area on the side irradiated with more than 10 Gy, statistically significant differences were obtained for 12 radiomic features and 1 dosiomic feature. In the area of chest soft tissues on the irradiated side, significant differences were obtained for 18 radiomic features and 4 dosiomic features. CONCLUSIONS: As a result, a number of radiomic and dosiomic features were identified which were statistically different in patients with minimal lesions and pulmonary pneumofibrosis following radiation therapy for breast cancer. Based on texture analysis, predictors (biomarkers) were identified to predict post-radiation lung injury and identify higher-risk patients.