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Prediction of Hemifacial Spasm Re-Appearing Phenomenon after Microvascular Decompression Surgery in Patients with Hemifacial Spasm Using Dynamic Susceptibility Contrast Perfusion Magnetic Resonance Imaging

  • Seung Hoon Lim (Department of Neurosurgery, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine) ;
  • Xiao-Yi Guo (Department of Medicine, Graduate School, Kyung Hee University) ;
  • Hyug-Gi Kim (Department of Radiology, Kyung Hee University Hospital) ;
  • Hak Cheol Ko (Department of Neurosurgery, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine) ;
  • Soonchan Park (Department of Radiology, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine) ;
  • Chang-Woo Ryu (Department of Radiology, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine) ;
  • Geon-Ho Jahng (Department of Radiology, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine)
  • Received : 2024.03.06
  • Accepted : 2024.06.25
  • Published : 2025.01.01

Abstract

Objective : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS reappearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

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Acknowledgement

The authors would like to express their gratitude to Professor Seung Hwan Lee, who is a neurosegen and is currently hospitalized due to a serious illness. Professor Lee initiated this project with Dr. Geon-Ho Jahng, but he was unable to continue his contribution. Therefore, this paper is dedicated to Professor Lee. In addition, the authors thank Mr. Justin Jang (Dynapex LLC, Seoul, Korea) for a technical support to batch-processing DSC images. Finally, the authors appreciate Miss Seon Hwa Lee (Clinical Research Institute, Kyung Hee University Hospital at Gangdong, Seoul, Korea) for providing advice on the statistical analyses. The research was supported by the National Research Foundation of Korea (NRF) grants funded by the Ministry of Science and ICT (RS-2024-00335770, G.H.J.), Republic of Korea.