Exploring the predictive value of additional peritumoral regions based on deep learning and radiomics: A multicenter study

作者：Xiangjun Wu, Di Dong, Lu Zhang, Mengjie Fang, Yongbei Zhu, Bingxi He, Zhaoxiang Ye, Minming Zhang, Shuixing Zhang, Jie Tian · 发表于：Medical Physics · 年份：2021 · DOI：10.1002/mp.14767 · 被引用次数：45 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Salivary Gland Tumors Diagnosis and Treatment、Head and Neck Cancer Studies

PURPOSE: The present study assessed the predictive value of peritumoral regions on three tumor tasks, and further explored the influence of peritumors with different sizes. METHODS: We retrospectively collected 333 samples of gastrointestinal stromal tumors from the Second Affiliated Hospital of Zhejiang University School of Medicine, and 183 samples of gastrointestinal stromal tumors from Tianjin Medical University Cancer Hospital. We also collected 211 samples of laryngeal carcinoma and 233 samples of nasopharyngeal carcinoma from the First Affiliated Hospital of Jinan University. The tasks of three tumor datasets were risk assessment (gastrointestinal stromal tumor), T3/T4 staging prediction (laryngeal carcinoma), and distant metastasis prediction (nasopharyngeal carcinoma), respectively. First, deep learning and radiomics were respectively used to construct peritumoral models, to study whether the peritumor had predictive value on three tumor datasets. Furthermore, we defined different sizes peritumors including fixed size (not considering tumor size) and adaptive size (according to average tumor radius) to explore the influence of peritumor of different sizes and types of tumors. Finally, we visualized the deep learning and radiomic models to observe the influence of the peritumor in three datasets. RESULTS: The performance of intra-peritumors are better than intratumors alone in three datasets. Specifically, the comparisons of area under receiver operating characteristi...