Prediction of tuberous sclerosis-associated neurocognitive disorders and seizures via machine learning of structural magnetic resonance imaging

Autor: Anat Shrot, Omer Shlomovitz, Philip Lawson, Shai Shrot, Bruria Ben-Zeev, Chen Hoffmann, Michal Tzadok
Rok vydání: 2021
Předmět:
Zdroj: Neuroradiology. 64:611-620
ISSN: 1432-1920
0028-3940
Popis: 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. 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. 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. 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.
Databáze: OpenAIRE
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