Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan province, China.
Yanjing Medical College of Capital Medical University, Beijing, China.
BMC Cancer. 2023 Sep 11;23(1):848. doi: 10.1186/s12885-023-11338-8.
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.
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.
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.
The current study demonstrates that MRI radiomics is able to predict molecular subgroups of PLGGs and KIAA1549-BRAF fusion with satisfying sensitivity.
This study was retrospectively registered at clinicaltrials.gov (NCT04217018).
本研究旨在基于多参数 MRI 提取的放射组学特征，建立预测儿童低级别胶质瘤（PLGGs）分子亚组（低危组和中/高危组）和分子标志物（KIAA1549-BRAF 融合）的机器学习模型。
本回顾性研究纳入 61 例 PLGGs 患者，根据分子亚组或分子标志物，将患者分为训练集和内部验证集，比例为 2:1。将患者分为低危组和中/高危组、BRAF 融合阳性和阴性组。我们从多参数 MRI 中提取了 5929 个放射组学特征。然后，我们去除冗余特征，在训练集上训练随机森林模型以预测分子亚组或分子标志物，并在内部验证集上验证其性能。通过 3 倍交叉验证验证预测模型的性能。
我们构建了一个分类模型，使用 4 个相关特征区分低危 PLGGs 和中/高危 PLGGs，在内部验证集中的 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，在内部验证集中的 AUC 为 0.818，准确率为 81.0%。
本研究表明 MRI 放射组学能够预测 PLGGs 的分子亚组和 KIAA1549-BRAF 融合，具有较高的灵敏度。
本研究在 clinicaltrials.gov 上进行了回顾性注册（NCT04217018）。
