靶向药物在儿童低级别神经胶质瘤 (pLGG) 治疗中的使用依赖于分子状态的确定。研究表明，可以使用基于 MRI 的放射组学特征或卷积神经网络 (CNN) 非侵入性地识别 pLGG 中的遗传改变。我们的目标是建立和评估放射组学和 CNN 相结合的非侵入性 pLGG 分子状态识别模型。这项回顾性研究使用了从 1999 年至 2018 年间接受 pLGG 治疗的 336 名患者的 T2-FLAIR MR 图像中手动分割的肿瘤区域。我们设计了 CNN 和随机森林放射组学模型，以及依赖于 CNN 和放射组学组合的模型特征，预测 pLGG 的遗传状态。此外，我们还研究了 CNN 是否可以从 MR 图像中预测放射组学特征值。 组合模型（平均 AUC：0.824）优于放射组学模型（0.802）和 CNN（0.764）。模型性能的差异具有统计显着性（p 值 < 0.05）。 CNN 能够很好地学习预测放射组学特征，例如表面体积比（平均相关性：0.864）和归一化的差异矩阵依赖性非均匀性（0.924），但无法学习其他特征，例如游程矩阵方差（ - 0.017) 和标准化非均匀性 (- 0.042)。我们的结果表明，同时依赖 CNN 和基于放射组学的特征的模型在区分 pLGG 的遗传状态方面比单独使用任何一种方法表现得更好，并且 CNN 无法表达所有手工制作的特征。© 2024。作者。

The use of targeted agents in the treatment of pediatric low-grade gliomas (pLGGs) relies on the determination of molecular status. It has been shown that genetic alterations in pLGG can be identified non-invasively using MRI-based radiomic features or convolutional neural networks (CNNs). We aimed to build and assess a combined radiomics and CNN non-invasive pLGG molecular status identification model. This retrospective study used the tumor regions, manually segmented from T2-FLAIR MR images, of 336 patients treated for pLGG between 1999 and 2018. We designed a CNN and Random Forest radiomics model, along with a model relying on a combination of CNN and radiomic features, to predict the genetic status of pLGG. Additionally, we investigated whether CNNs could predict radiomic feature values from MR images. The combined model (mean AUC: 0.824) outperformed the radiomics model (0.802) and CNN (0.764). The differences in model performance were statistically significant (p-values < 0.05). The CNN was able to learn predictive radiomic features such as surface-to-volume ratio (average correlation: 0.864), and difference matrix dependence non-uniformity normalized (0.924) well but was unable to learn others such as run-length matrix variance (- 0.017) and non-uniformity normalized (- 0.042). Our results show that a model relying on both CNN and radiomic-based features performs better than either approach separately in differentiating the genetic status of pLGGs, and that CNNs are unable to express all handcrafted features.© 2024. The Author(s).