- 著者
- Shunsuke TSUZUKI Yoshihiro MURAGAKI Masayuki NITTA Taiichi SAITO Takashi MARUYAMA Shunichi KORIYAMA Manabu TAMURA Takakazu KAWAMATA
- 出版者
- The Japan Neurosurgical Society
- 雑誌
- Neurologia medico-chirurgica (ISSN:04708105)
- 巻号頁・発行日
- pp.2022-0340, (Released:2024-01-10)
- 参考文献数
- 36
Neurosurgery is complex surgery that requires a strategy that maximizes the removal of tumors and minimizes complications; thus, a safe environment during surgery should be guaranteed. In this study, we aimed to verify the safety of brain surgery using intraoperative magnetic resonance imaging (iMRI), based on surgical experience since 2000. Thus, we retrospectively examined 2,018 surgical procedures that utilized iMRI performed in the operating room at Tokyo Women's Medical University Hospital between March 2000 and October 2019. As per our data, glioma constituted the majority of the cases (1,711 cases, 84.8%), followed by cavernous hemangioma (61 cases, 3.0%), metastatic brain tumor (37 cases, 1.8%), and meningioma (31 cases, 1.5%). In total, 1,704 patients who underwent glioma removal were analyzed for mortality within 30 days of surgery and for reoperation rates and the underlying causes within 24 hours and 30 days of surgery. As per our analysis, only one death out of all the glioma cases (0.06%) was reported within the 30-day period. Meanwhile, reoperation within 30 days was performed in 37 patients (2.2%) due to postoperative bleeding in 17 patients (1.0%), infection in 12 patients (0.7%), hydrocephalus in 6 patients (0.4%), cerebrospinal fluid (CSF) leakage in 1 patient, and brain edema in 1 patient (0.06%). Of these, 14 cases (0.8%) of reoperation were performed within 24 hours, that is, 13 cases (0.8%) due to postoperative bleeding and 1 case (0.06%) due to acute hydrocephalus. Mortality rate within 30 days was less than 0.1%. Thus, information-guided surgery with iMRI can improve the safety of surgical resections, including those of gliomas.
- 著者
- Takafumi SHIMAMOTO Yuko SANO Kitaro YOSHIMITSU Ken MASAMUNE Yoshihiro MURAGAKI
- 出版者
- The Japan Neurosurgical Society
- 雑誌
- Neurologia medico-chirurgica (ISSN:04708105)
- 巻号頁・発行日
- pp.2022-0350, (Released:2023-05-11)
- 参考文献数
- 25
Brain tissue deformation during surgery significantly reduces the accuracy of image-guided neurosurgeries. We generated updated magnetic resonance images (uMR) in this study to compensate for brain shifts after dural opening using a convolutional neural network (CNN). This study included 248 consecutive patients who underwent craniotomy for initial intra-axial brain tumor removal and correspondingly underwent preoperative MR (pMR) and intraoperative MR (iMR) imaging. Deep learning using CNN to compensate for brain shift was performed using the pMR as input data, and iMR obtained after dural opening as the ground truth. For the tumor center (TC) and the maximum shift position (MSP), statistical analysis using the Wilcoxon signed-rank test was performed between the target registration error (TRE) for the pMR and iMR (i.e., the actual amount of brain shift) and the TRE for the uMR and iMR (i.e., residual error after compensation). The TRE at the TC decreased from 4.14 ± 2.31 mm to 2.31 ± 1.15 mm, and the TRE at the MSP decreased from 9.61 ± 3.16 mm to 3.71 ± 1.98 mm. The Wilcoxon signed-rank test of the pMR TRE and uMR TRE yielded a p-value less than 0.0001 for both the TC and MSP. Using a CNN model, we designed and implemented a new system that compensated for brain shifts after dural opening. Learning pMR and iMR with a CNN demonstrated the possibility of correcting the brain shift after dural opening.