Department of Neurosurgery, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China.
Department of Emergency Medicine, the Sixth Medical Centre, Chinese PLA General Hospital, Beijing, 100048, China.
Eur Radiol. 2024 Apr;34(4):2759-2771. doi: 10.1007/s00330-023-10212-2. Epub 2023 Sep 22.
A decline in language function is a common complication after glioma surgery, affecting patients' quality of life and survival. This study predicts the postoperative decline in language function and whether it can be recovered based on the preoperative white matter structural network.
Eighty-one right-handed patients with glioma involving the left hemisphere were retrospectively included. Their language function was assessed using the Western Aphasia Battery before and 1 week and 3 months after surgery. Structural connectome combining DTI features was selected to predict postoperative language decline and recovery. Nested cross-validation was used to optimize the models, evaluate the prediction performance of the models, and identify the most predictive features.
Five, seven, and seven features were finally selected as the predictive features in each model and used to establish predictive models for postoperative language decline (1 week after surgery), long-term language decline (3 months after surgery), and language recovery, respectively. The overall accuracy of the three models in nested cross-validation and overall area under the receiver operating characteristic curve were 0.840, 0.790, and 0.867, and 0.841, 0.778, and 0.901, respectively.
We used machine learning algorithms to establish models to predict whether the language function of glioma patients will decline after surgery and whether postoperative language deficit can recover, which may help improve the development of treatment strategies. The difference in features in the non-language decline or the language recovery group may reflect the structural basis for the protection and compensation of language function in gliomas.
Models can predict the postoperative language decline and whether it can recover in glioma patients, possibly improving the development of treatment strategies. The difference in selected features may reflect the structural basis for the protection and compensation of language function.
• Structural connectome combining diffusion tensor imaging features predicted glioma patients' language decline after surgery. • Structural connectome combining diffusion tensor imaging features predicted language recovery of glioma patients with postoperative language disorder. • Diffusion tensor imaging and connectome features related to language function changes imply plastic brain regions and connections.
语言功能下降是胶质瘤手术后常见的并发症，影响患者的生活质量和生存。本研究基于术前白质结构网络预测术后语言功能下降及其是否可恢复。
回顾性纳入 81 例左半球胶质瘤且为右利手的患者。在术前及术后 1 周和 3 个月使用西方失语症成套测验评估语言功能。结合弥散张量成像特征的结构连接组学用于预测术后语言下降及恢复。嵌套交叉验证用于优化模型，评估模型的预测性能，并识别最具预测性的特征。
最终在每个模型中分别选择了 5、7 和 7 个特征作为预测特征，以建立用于预测术后语言下降（术后 1 周）、长期语言下降（术后 3 个月）和语言恢复的预测模型。在嵌套交叉验证和整体接收者操作特征曲线下面积中，三个模型的整体准确率分别为 0.840、0.790 和 0.867，0.841、0.778 和 0.901。
我们使用机器学习算法建立了模型来预测胶质瘤患者手术后语言功能是否会下降以及术后语言缺陷是否可以恢复，这可能有助于改善治疗策略的制定。在非语言下降或语言恢复组中特征的差异可能反映了胶质瘤中语言功能保护和代偿的结构基础。
模型可以预测胶质瘤患者术后语言下降及其是否可恢复，可能改善治疗策略的制定。所选特征的差异可能反映了语言功能保护和代偿的结构基础。
• 结合弥散张量成像特征的结构连接组学预测胶质瘤患者术后语言下降。• 结合弥散张量成像特征的结构连接组学预测术后语言障碍的胶质瘤患者的语言恢复。• 与语言功能变化相关的弥散张量成像和连接组学特征提示了可塑性脑区和连接。
