The role of Simpson grading in meningiomas after integration of the updated WHO classification and adjuvant radiotherapy.
Felix BehlingChristina FodiElgin HoffmannMirjam RenovanzMarco SkardellyGhazaleh TabatabaiCornelia BrendleJürgen HoneggerMarcos TatagibaPublished in: Neurosurgical review (2020)
Since the introduction of the Simpson grading for the extent of resection in meningiomas in 1957, its usefulness in modern neurosurgery has been challenged. Especially, the updated WHO classification regarding brain invasion and the efficacy of radiation therapy has not been taken into account when evaluating the prognostic role of the Simpson grading in this era. We analyzed the clinical and histopathological data of 1571 meningiomas that were surgically resected in the authors' institution between July 2003 and March 2017. Operative reports were reviewed regarding the extent of resection according to Simpson grading. Meningioma subtype according to the updated WHO classification of 2016 and clinical characteristics and time to tumor progression were analyzed. The mean follow-up was 38.4 months (range 1.2 to 195.6). A higher rate of tumor recurrence was observed for male gender, younger age, recurrent tumors, non-spinal tumor localization, higher WHO, and Simpson grades in the univariate analysis. In the multivariate analysis older age, recurrent tumors and higher WHO grades remained negative prognostic factors. Among the different Simpson grades, the relative risk for recurrence was highest for grade IV compared to all other grades (each p < 0.0001), while there was no difference between Simpson grades I and II. Adjuvant radiotherapy showed lower rates of tumor recurrence. Subtotal microsurgical resection remains an independent prognostic factor with a higher rate of tumor recurrence. The prognostic benefit of radical treatment of the dural attachment is questionable and needs to be considered when weighing the intraoperative risks of radicality.
Keyphrases
- prognostic factors
- radiation therapy
- early stage
- machine learning
- deep learning
- free survival
- locally advanced
- radiation induced
- squamous cell carcinoma
- emergency department
- physical activity
- patients undergoing
- white matter
- poor prognosis
- spinal cord injury
- mental health
- spinal cord
- risk assessment
- artificial intelligence
- rectal cancer
- functional connectivity
- resting state
- electronic health record
- data analysis
- climate change
- combination therapy
- middle aged
- drug induced
- cerebral ischemia