Predicting Epileptogenic Tubers in Patients With Tuberous Sclerosis Complex Using a Fusion Model Integrating Lesion Network Mapping and Machine Learning
Overall, this fusion model demonstrates high accuracy and robust clinical utility as a noninvasive tool for the localization of ETs. It effectively addresses the current challenges in identifying ETs, providing valuable support for surgical planning in patients with TSC-related epilepsy.
Authors
- Dezhi Cao (ORCID: https://orcid.org/0000-0002-7424-8063)
- Tinghong Liu (ORCID: https://orcid.org/0000-0003-0156-7490)
- Haobin Wei
- Qi Wang (ORCID: https://orcid.org/0009-0002-9931-6816)
- Zhirong Wei
- Ping Ding (ORCID: https://orcid.org/0000-0003-4476-8700)
- Xinyu Huang
- Suhui Kuang (ORCID: https://orcid.org/0009-0005-1945-3450)
- Shuli Liang (ORCID: https://orcid.org/0000-0002-7292-745X)
- Shaohui Zhang (ORCID: https://orcid.org/0000-0002-0057-1167)
- Jinshan Xu
- Bing Liu (ORCID: https://orcid.org/0000-0003-2029-5187)
Institutions
- Ministry of Education of the People's Republic of China (CN)
- Capital Medical University (CN)
- Beijing Normal University (CN)
- Chinese PLA General Hospital (CN)
- Beijing Academy of Artificial Intelligence (CN)
- Institute of Automation (CN)
- Chinese Institute for Brain Research (CN)
- Beijing Children’s Hospital (CN)
- Shenzhen Children's Hospital (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- Annals of Clinical and Translational Neurology
- Published
- 2025-12-13
- DOI
- https://doi.org/10.1002/acn3.70277
- Primary Topic
- Tuberous Sclerosis Complex Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Beijing Municipal Science and Technology Commission