Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas

作者：Zhen Liu, Xuanke Hong, Linglong Wang, Zeyu Ma, Fangzhan Guan, Weiwei Wang, Yuning Qiu, Xueping Zhang, Wenchao Duan, Minkai Wang, Chen Sun, Yuanshen Zhao, Jingxian Duan, Qiuchang Sun, Lin Liu, Lei Ding, Yuchen Ji, Dongming Yan, Xianzhi Liu, Jingliang Cheng, Zhenyu Zhang, Zhicheng Li, Jing Yan · 发表于：BMC Cancer · 年份：2023 · DOI：10.1186/s12885-023-11338-8 · 被引用次数：15 · 研究领域：Glioma Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、Chromatin Remodeling and Cancer

BACKGROUND: We aimed to develop machine learning models for prediction of molecular subgroups (low-risk group and intermediate/high-risk group) and molecular marker (KIAA1549-BRAF fusion) of pediatric low-grade gliomas (PLGGs) based on radiomic features extracted from multiparametric MRI. METHODS: 61 patients with PLGGs were included in this retrospective study, which were divided into a training set and an internal validation set at a ratio of 2:1 based on the molecular subgroups or the molecular marker. The patients were classified into low-risk and intermediate/high-risk groups, BRAF fusion positive and negative groups, respectively. We extracted 5929 radiomic features from multiparametric MRI. Thereafter, we removed redundant features, trained random forest models on the training set for predicting the molecular subgroups or the molecular marker, and validated their performance on the internal validation set. The performance of the prediction model was verified by 3-fold cross-validation. RESULTS: We constructed the classification model differentiating low-risk PLGGs from intermediate/high-risk PLGGs using 4 relevant features, with an AUC of 0.833 and an accuracy of 76.2% in the internal validation set. In the prediction model for predicting KIAA1549-BRAF fusion using 4 relevant features, an AUC of 0.818 and an accuracy of 81.0% were achieved in the internal validation set. CONCLUSIONS: The current study demonstrates that MRI radiomics is able to predict molecular subgrou...