Multi-omics to predict acute radiation esophagitis in patients with lung cancer treated with intensity-modulated radiation therapy

作者：Xiaoli Zheng, Wei Guo, Yunhan Wang, Jiang Zhang, Yuanpeng Zhang, Cheng Chen, Xinzhi Teng, Saikit Lam, Ta Zhou, Zongrui Ma, Ruining Liu, Hui Wu, Hong Ge, Jing Cai, Bing Li · 发表于：European journal of medical research · 年份：2023 · DOI：10.1186/s40001-023-01041-6 · 被引用次数：42 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Effects of Radiation Exposure、Lung Cancer Diagnosis and Treatment

PURPOSE: The study aimed to predict acute radiation esophagitis (ARE) with grade ≥ 2 for patients with locally advanced lung cancer (LALC) treated with intensity-modulated radiation therapy (IMRT) using multi-omics features, including radiomics and dosiomics. METHODS: 161 patients with stage IIIA-IIIB LALC who received chemoradiotherapy (CRT) or radiotherapy by IMRT with a prescribed dose from 45 to 70 Gy from 2015 to 2019 were enrolled retrospectively. All the toxicity gradings were given following the Common Terminology Criteria for Adverse Events V4.0. Multi-omics features, including radiomics, dosiomics (including dose-volume histogram dosimetric parameters), were extracted based on the planning CT image and three-dimensional dose distribution. All data were randomly divided into training cohorts (N = 107) and testing cohorts (N = 54). In the training cohorts, features with reliably high outcome relevance and low redundancy were selected under random patient subsampling. Four classification models (using clinical factors (CF) only, using radiomics features (RFs) only, dosiomics features (DFs) only, and the hybrid features (HFs) containing clinical factors, radiomics and dosiomics) were constructed employing the Ridge classifier using two-thirds of randomly selected patients as the training cohort. The remaining patient was treated as the testing cohort. A series of models were built with 30 times training-testing splits. Their performances were assessed using the area und...