背景:我们旨在基于从多参数MRI提取的影像组学特征,开发机器学习模型,用于预测儿童低级别神经胶质瘤(PLGG)的分子亚群(低危组和中/高危组)和分子标记(KIAA1549-BRAF融合)。
方法:本回顾性研究包括61例PLGG患者,根据分子亚组或分子标记以2:1的比例分为训练集和内部验证集。将患者分为低风险和中/高风险组,BRAF融合阳性和阴性组,分别。我们从多参数MRI中提取了5929个放射学特征。此后,我们删除了多余的功能,在训练集上训练随机森林模型,用于预测分子亚群或分子标记,并在内部验证集上验证了它们的性能。通过3折交叉验证验证了预测模型的性能。
结果:我们使用4个相关特征构建了区分低风险PLGs和中/高风险PLGs的分类模型,内部验证集的AUC为0.833,准确率为76.2%。在使用4个相关特征预测KIAA1549-BRAF融合的预测模型中,内部验证集的AUC为0.818,准确率为81.0%.
结论:目前的研究表明,MRI影像组学能够以令人满意的灵敏度预测PLGG和KIAA1549-BRAF融合的分子亚群。
背景:本研究在clinicaltrials.gov(NCT04217018)进行了回顾性注册。
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 subgroups of PLGGs and KIAA1549-BRAF fusion with satisfying sensitivity.
BACKGROUND: This study was retrospectively registered at clinicaltrials.gov (NCT04217018).