CT-based different regions of interest radiomics analysis for acute radiation pneumonitis in patients with locally advanced NSCLC after chemoradiotherapy

• Published in: Clinical and translational radiation oncology 2024-09, Vol.48, p.100828, Article 100828
• Main Authors: Hou, Liqiao, Chen, Kuifei, Zhou, Chao, Tang, Xingni, Yu, Changhui, Jia, Haijian, Xu, Qianyi, Zhou, Suna, Yang, Haihua
• Format: Article
• Language: English
• Subjects: Computed tomography simulated; Non-small cell cancer; Original; Radiation pneumonitis; Radiomics
• ISSN: 2405-6308; 2405-6308
• DOI: 10.1016/j.ctro.2024.100828

•Our research demonstrated that radiomics models, divided into different ROIs based on dosimetry parameters, proved to be a more effective tool for discriminating acute RP compared to dosimetry models.•Differences in the algorithms may introduce some bias into the model. To ensure model accuracy and stability, our study employs six machine learning algorithms to build the model. This approach reduces the model’s instability caused by the singularity of algorithms and helps identify the optimal algorithm to build a reliable and stable model.•The radiomics model was able to predict the acute RP more effectively in comparison with the traditional dosimetry model. Especially the radiomics model based on the V30 Lung-PTV region was able to achieve a higher accuracy when compared to the other regions. To establish a radiomics model using radiomics features from different region of interests (ROI) based on dosimetry-related regions in enhanced computed tomography (CT) simulated images to predict radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC). Our retrospective study was conducted based on a cohort of 236 NSCLC patients (59 of them with RP≥2) who were treated in 2 institutions and divided into the primary cohort (n = 182,46 of them with RP≥2) and external validation cohort (n = 54,13 of them with RP≥2). Radiomic features extracted from three ROIs were defined as the whole lung (WL), the dose volume histogram (DVH) of the lung V20 (V20_Lung) and the DVH of the V30 of lung minus the planning target volume (PTV) (V30 Lung-PTV). A total of 107 radiomics features were extracted from each ROIs. The U test, correlation coefficient and least absolute shrinkage and selection operator (LASSO) were performed for features selection. Six models based on different classification algorithms were developed to select the best radiomics model (R model).In addition, we built a dosimetry model then combined it with the best R model to create a mixed model (R+D model) The receiver operating characteristic (ROC) curve was delineated to assess the predictive efficacy of the models. Decision curve analysis could benefit from the model proposals through the assessment of clinical utility. Among the three ROIs, the best R model constructed from the LightGBM algorithm demonstrated the strongest discriminative ability in the ROI of V30 Lung-PTV. The corresponding area under the curve (AUC) value was 0.930 (95 % confidence interval (CI): 0.829–0.941). The D mod