PT - JOURNAL ARTICLE AU - Wang, Haifeng AU - Hu, Zhanqi AU - Jiang, Dian AU - Lin, Rongbo AU - Zhao, Cailei AU - Zhao, Xia AU - Zhou, Yihang AU - Zhu, Yanjie AU - Zeng, Hongwu AU - Liang, Dong AU - Liao, Jianxiang AU - Li, Zhicheng TI - Predicting Antiseizure Medication Treatment in Children with Rare Tuberous Sclerosis Complex–Related Epilepsy Using Deep Learning AID - 10.3174/ajnr.A8053 DP - 2023 Dec 01 TA - American Journal of Neuroradiology PG - 1373--1383 VI - 44 IP - 12 4099 - http://www.ajnr.org/content/44/12/1373.short 4100 - http://www.ajnr.org/content/44/12/1373.full SO - Am. J. Neuroradiol.2023 Dec 01; 44 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