RT Journal Article
SR Electronic
T1 Predicting Antiseizure Medication Treatment in Children with Rare Tuberous Sclerosis Complex–Related Epilepsy Using Deep Learning
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 1373
OP 1383
DO 10.3174/ajnr.A8053
VO 44
IS 12
A1 Wang, Haifeng
A1 Hu, Zhanqi
A1 Jiang, Dian
A1 Lin, Rongbo
A1 Zhao, Cailei
A1 Zhao, Xia
A1 Zhou, Yihang
A1 Zhu, Yanjie
A1 Zeng, Hongwu
A1 Liang, Dong
A1 Liao, Jianxiang
A1 Li, Zhicheng
YR 2023
UL http://www.ajnr.org/content/44/12/1373.abstract
AB BACKGROUND AND PURPOSE: Tuberous sclerosis complex disease is a rare, multisystem genetic disease, but appropriate drug treatment allows many pediatric patients to have positive outcomes. The purpose of this study was to predict the effectiveness of antiseizure medication treatment in children with tuberous sclerosis complex–related epilepsy.MATERIALS AND METHODS: We conducted a retrospective study involving 300 children with tuberous sclerosis complex–related epilepsy. The study included the analysis of clinical data and T2WI and FLAIR images. The clinical data consisted of sex, age of onset, age at imaging, infantile spasms, and antiseizure medication numbers. To forecast antiseizure medication treatment, we developed a multitechnique deep learning method called WAE-Net. This method used multicontrast MR imaging and clinical data. The T2WI and FLAIR images were combined as FLAIR3 to enhance the contrast between tuberous sclerosis complex lesions and normal brain tissues. We trained a clinical data-based model using a fully connected network with the above-mentioned variables. After that, a weighted-average ensemble network built from the ResNet3D architecture was created as the final model.RESULTS: The experiments had shown that age of onset, age at imaging, infantile spasms, and antiseizure medication numbers were significantly different between the 2 drug-treatment outcomes (P < .05). The hybrid technique of FLAIR3 could accurately localize tuberous sclerosis complex lesions, and the proposed method achieved the best performance (area under the curve = 0.908 and accuracy of 0.847) in the testing cohort among the compared methods.CONCLUSIONS: The proposed method could predict antiseizure medication treatment of children with rare tuberous sclerosis complex–related epilepsy and could be a strong baseline for future studies.ACCaccuracyASMantiseizure medicationAUCarea under the curveCNNconvolutional neural networkDCAdecision curve analysisFCNNfully connected neural networkFNfalse-negativeFPfalse-positiveReLUrectified linear unitROCreceiver operating characteristicSENsensitivitySPEspecificityTNtrue-negativeTPtrue-positiveTSCtuberous sclerosis complexWAEweighted-average ensemble