Function-Wise Dual-Omics analysis for radiation pneumonitis prediction in lung cancer patients - Li, Bing; Ren, Ge; Guo, Wei; Zhang, Jiang; Lam, Sai-Kit; Zheng, Xiaoli; Teng, Xinzhi; Wang, Yunhan; Yang, Yang; Dan, Qinfu; Meng, Lingguang; Ma, Zongrui; et al. (Frontiers in Pharmacology, 2022)
Purpose: This study investigates the impact of lung function on radiation pneumonitis prediction using a dual-omics analysis method. Methods: We retrospectively collected data of 126 stage III lung cancer patients treated with chemo-radiotherapy using intensity-modulated radiotherapy, including pre-treatment planning CT images, radiotherapy dose distribution, and contours of organs and structures. Lung perfusion functional images were generated using a previously developed deep learning method. The whole lung (WL) volume was divided into function-wise lung (FWL) regions based on the lung perfusion functional images. A total of 5474 radiomics features and 213 dose features (including dosiomics features and dose-volume histogram factors) were extracted from the FWL and WL regions, respectively. The radiomics features (R), dose features (D), and combined dual-omics features (RD) were used for the analysis in each lung region of WL and FWL, labeled as WL-R, WL-D, WL-RD, FWL-R, FWL-D, and FWL-RD. The feature selection was carried out using ANOVA, followed by a statistical F-test and Pearson correlation test. Thirty times train-test splits were used to evaluate the predictability of each group. The overall average area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and f1-score were calculated to assess the performance of each group. Results: The FWL-RD achieved a significantly higher average AUC than the WL-RD group in the training (FWL-RD: 0.927±0.031, WL-RD: 0.849±0.064) and testing cohorts (FWL-RD: 0.885±0.028, WL-RD: 0.762±0.053, p < 0.001). When using radiomics features only, the FWL-R group yielded a better classification result than the model trained with WL-R features in the training (FWL-R: 0.919±0.036, WL-R: 0.820±0.052) and testing cohorts (FWL-R: 0.862±0.028, WL-R: 0.750±0.057, p < 0.001). The FWL-D group obtained an average AUC of 0.782±0.032, obtaining a better classification performance than the WL-D feature-based model of 0.740±0.028 in the training cohort, while no significant difference was observed in the testing cohort (FWL-D: 0.725±0.064, WL-D: 0.710 ± 0.068, p = 0.54). Conclusions: The dual-omics features from different lung functional regions can improve the prediction of radiation pneumonitis for lung cancer patients under IMRT treatment. This function-wise dual-omics analysis method holds great promise to improve the prediction of radiation pneumonitis for lung cancer patients.