基于皮质-皮质诱发电位和机器学习技术的神经外科术后语言功能障碍预测。

IF 1.1 Q4 MEDICINE, RESEARCH & EXPERIMENTAL Sovremennye Tehnologii v Medicine Pub Date : 2022-01-01 Epub Date: 2022-01-28 DOI:10.17691/stm2022.14.1.03
T A Ishankulov, G V Danilov, D I Pitskhelauri, O Yu Titov, A A Ogurtsova, S B Buklina, E V Gulaev, T A Konakova, A E Bykanov
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引用次数: 2

摘要

术中皮质-皮质诱发电位(CCEPs)的记录可以研究大脑皮层各功能区之间的有效联系。基于CCEP信号变化预测神经外科术后语言功能障碍的根本可能性,可作为制定脑内肿瘤切除生理允许度标准的基础,最大限度地保留患者的生活质量。本研究的目的是测试利用术中肿瘤切除阶段前记录的CCEP数据预测神经胶质性脑肿瘤患者术后语言障碍的可能性。材料和方法:报告26例患者的CCEP数据。为了预测术后语音功能的恶化,我们使用了四种方法来呈现CCEP数据和几种机器学习模型:决策树的随机森林、逻辑回归和具有不同核类型(线性、径向和多项式)的支持向量机方法。我们训练了20个模型的变体:每个模型都经过300次重采样实验。该研究共进行了6000次试验。结果:对经过300次重采样测试训练的每个模型的预测质量度量进行平均,以消除“成功”和“不成功”数据分组的影响。采用多项式核支持向量机得到的最佳结果为F1-score = 0.638。在大多数试验中,灵敏度得分较高,最佳模型灵敏度得分达到0.993;最佳模型的特异性为0.370。结论:本初步研究证明了基于神经胶质肿瘤主要阶段切除前的CCEP数据预测言语功能障碍的可能性;使用传统的机器学习方法处理数据。结果表明,具有高灵敏度的最佳模型特异性不足。进一步的研究将旨在评估CCEP在手术过程中的变化及其与术后言语障碍发展的关系。
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Prediction of Postoperative Speech Dysfunctions in Neurosurgery Based on Cortico-Cortical Evoked Potentials and Machine Learning Technology.

Intraoperative recording of cortico-cortical evoked potentials (CCEPs) enables studying effective connections between various functional areas of the cerebral cortex. The fundamental possibility of postoperative speech dysfunction prediction in neurosurgery based on CCEP signal variations could serve as a basis to develop the criteria for the physiological permissibility of intracerebral tumors removal for maximum preservation of the patients' quality of life. The aim of the study was to test the possibility of predicting postoperative speech disorders in patients with glial brain tumors by using the CCEP data recorded intraoperatively before the stage of tumor resection.

Materials and methods: CCEP data were reported for 26 patients. To predict the deterioration of speech functions in the postoperative period, we used four options for presenting CCEP data and several machine learning models: a random forest of decision trees, logistic regression, and support vector machine method with different types of kernels: linear, radial, and polynomial. Twenty variants of models were trained: each in 300 experiments with resampling. A total of 6000 tests were performed in the study.

Results: The prediction quality metrics for each model trained in 300 tests with resampling were averaged to eliminate the influence of "successful" and "unsuccessful" data grouping. The best result with F1-score = 0.638 was obtained by the support vector machine with a polynomial kernel. In most tests, a high sensitivity score was observed, and in the best model, it reached a value of 0.993; the specificity of the best model was 0.370.

Conclusion: This pilot study demonstrated the possibility of predicting speech dysfunctions based on CCEP data taken before the main stage of glial tumors resection; the data were processed using traditional machine learning methods. The best model with high sensitivity turned out to be insufficiently specific. Further studies will be aimed at assessing the changes in CCEP during the operation and their relationship with the development of postoperative speech deficit.

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来源期刊
Sovremennye Tehnologii v Medicine
Sovremennye Tehnologii v Medicine MEDICINE, RESEARCH & EXPERIMENTAL-
CiteScore
1.80
自引率
0.00%
发文量
38
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