Purpose

Tuberous sclerosis complex (TSC) is a genetic disorder characterized by multiorgan hamartomas, including cerebral lesions, with seizures as a common presentation. Most TSC patients will also experience neurocognitive comorbidities. Our objective was to use machine learning techniques incorporating clinical and imaging data to predict the occurrence of major neurocognitive disorders and seizures in TSC patients.

Methods

A cohort of TSC patients were enrolled in this retrospective study. Clinical data included genetic, demographic, and seizure characteristics. Imaging parameters included the number, characteristics, and location of cortical tubers and the presence of subependymal nodules, SEGAs, and cerebellar tubers. A random forest machine learning scheme was used to predict seizures and neurodevelopmental delay or intellectual developmental disability. Prediction ability was assessed by the area-under-the-curve of receiver-operating-characteristics (AUC-ROC) of ten-fold cross-validation training set and an independent validation set.

Results

The study population included 77 patients, 55% male (17.1 ± 11.7 years old). The model achieved AUC-ROC of 0.72 ± 0.1 and 0.68 in the training and internal validation datasets, respectively, for predicting neurocognitive comorbidity. Performance was limited in predicting seizures (AUC-ROC of 0.54 ± 0.19 and 0.71 in the training and internal validation datasets, respectively). The integration of seizure characteristics into the model improved the prediction of neurocognitive comorbidity with AUC-ROC of 0.84 ± 0.07 and 0.75 in the training and internal validation datasets, respectively.

Conclusions

This proof of concept study shows that it is possible to achieve a reasonable prediction of major neurocognitive morbidity in TSC patients using structural brain imaging and machine learning techniques. These tools can help clinicians identify subgroups of TSC patients with an increased risk of developing neurocognitive comorbidities.

中文翻译：

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通过结构磁共振成像的机器学习预测结节性硬化症相关的神经认知障碍和癫痫发作

目的

结节性硬化症 (TSC) 是一种以多器官错构瘤为特征的遗传性疾病，包括脑损伤，以癫痫为常见表现。大多数 TSC 患者还会出现神经认知合并症。我们的目标是使用结合临床和影像数据的机器学习技术来预测 TSC 患者主要神经认知障碍和癫痫发作的发生。

方法

一组 TSC 患者参加了这项回顾性研究。临床数据包括遗传、人口统计学和癫痫发作特征。成像参数包括皮质结节的数量、特征和位置以及室管膜下结节、SEGA 和小脑结节的存在。随机森林机器学习方案用于预测癫痫发作和神经发育迟缓或智力发育障碍。通过十倍交叉验证训练集和独立验证集的接收器操作特征曲线下面积（AUC-ROC）评估预测能力。

结果

研究人群包括 77 名患者，55% 为男性（17.1 ± 11.7 岁）。该模型在训练和内部验证数据集中分别实现了 0.72 ± 0.1 和 0.68 的 AUC-ROC，用于预测神经认知合并症。预测癫痫发作的性能有限（训练和内部验证数据集中的 AUC-ROC 分别为 0.54 ± 0.19 和 0.71）。将癫痫发作特征整合到模型中提高了对神经认知合并症的预测，在训练和内部验证数据集中的 AUC-ROC 分别为 0.84 ± 0.07 和 0.75。

结论

这项概念验证研究表明，使用结构性脑成像和机器学习技术可以合理预测 TSC 患者的主要神经认知发病率。这些工具可以帮助临床医生识别 TSC 患者的亚组，这些患者发生神经认知合并症的风险增加。