Operative Management of Trigeminal Schwannomas: Based on a Modified Classification in a Study of 93 Cases
Acta Neurochirurgica(2023)
Capital Medical University
Abstract
Background Advances in microscopic and endoscopic surgical techniques have outpaced traditional classification and transcranial surgical strategies, especially with reference to the treatment of trigeminal schwannomas (TSs). A modified TS classification is proposed and appropriate surgical strategies are discussed. Methods The cases of 93 patients who underwent surgical treatment in Beijing Tiantan Hospital in the previous 6 years were analyzed retrospectively, and a literature review was conducted. Results Classification is based on surgical direction. Tumors were classified as follows: type A, backward orientation, located in the orbit or orbit and middle cranial fossa (8 cases, 8.6%); type B, upward orientation, located in the pterygopalatine fossa, infratemporal fossa or pterygopalatine fossa, infratemporal fossa, and middle cranial fossa (23 cases, 24.7%); type C, forward and backward orientations, located in the middle cranial fossa, posterior cranial fossa or both (58 cases, 62.4%); and type D, located in multiple regions (4 cases, 4.3%). 91.40% of patients underwent gross total resection (GTR) with 29 cases receiving endoscopic resection of whom 93.10% (27/29) experienced GTR. Conclusion The 93 cases were satisfactorily divided into four types, according to tumor location and surgical orientation, enabling safe and effective removal by appropriate surgery.
MoreTranslated text
Key words
Surgical strategy,Trigeminal schwannomas,Classification,Endoscopic,Skull base
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined