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      The Simpson grading: defining the optimal threshold for gross total resection in meningioma surgery

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          Abstract

          Classification of the extent of resection into gross and subtotal resection (GTR and STR) after meningioma surgery is derived from the Simpson grading. Although utilized to indicate adjuvant treatment or study inclusion, conflicting definitions of STR in terms of designation of Simpson grade III resections exist. Correlations of Simpson grading and dichotomized scales (Simpson grades I–II vs ≥ III and grade I–III vs ≥ IV) with postoperative recurrence/progression were compared using Cox regression models. Predictive values were further compared by time-dependent receiver operating curve (tdROC) analyses. In 939 patients (28% males, 72% females) harboring WHO grade I (88%) and II/III (12%) meningiomas, Simpson grade I, II, III, IV, and V resections were achieved in 29%, 48%, 11%, 11%, and < .5%, respectively. Recurrence/progression was observed in 112 individuals (12%) and correlated with Simpson grading ( p = .003). The risk of recurrence/progression was increased after STR in both dichotomized scales but higher when subsuming Simpson grade ≥ IV than grade ≥ III resections (HR: 2.49, 95%CI 1.50–4.12; p < .001 vs HR: 1.67, 95%CI 1.12–2.50; p = .012). tdROC analyses showed moderate predictive values for the Simpson grading and significantly ( p < .05) lower values for both dichotomized scales. AUC values differed less between the Simpson grading and the dichotomization into grade I–III vs ≥ IV than grade I–II vs ≥ III resections. Dichotomization of the extent of resection is associated with a loss of the prognostic value. The value for the prediction of progression/recurrence is higher when dichotomizing into Simpson grade I–III vs ≥ IV than into grade I–II vs ≥ III resections.

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          Most cited references23

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          The recurrence of intracranial meningiomas after surgical treatment.

          D. Simpson (1957)
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            Time-dependent ROC curve analysis in medical research: current methods and applications

            Background ROC (receiver operating characteristic) curve analysis is well established for assessing how well a marker is capable of discriminating between individuals who experience disease onset and individuals who do not. The classical (standard) approach of ROC curve analysis considers event (disease) status and marker value for an individual as fixed over time, however in practice, both the disease status and marker value change over time. Individuals who are disease-free earlier may develop the disease later due to longer study follow-up, and also their marker value may change from baseline during follow-up. Thus, an ROC curve as a function of time is more appropriate. However, many researchers still use the standard ROC curve approach to determine the marker capability ignoring the time dependency of the disease status or the marker. Methods We comprehensively review currently proposed methodologies of time-dependent ROC curves which use single or longitudinal marker measurements, aiming to provide clarity in each methodology, identify software tools to carry out such analysis in practice and illustrate several applications of the methodology. We have also extended some methods to incorporate a longitudinal marker and illustrated the methodologies using a sequential dataset from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver. Results From our methodological review, we have identified 18 estimation methods of time-dependent ROC curve analyses for censored event times and three other methods can only deal with non-censored event times. Despite the considerable numbers of estimation methods, applications of the methodology in clinical studies are still lacking. Conclusions The value of time-dependent ROC curve methods has been re-established. We have illustrated the methods in practice using currently available software and made some recommendations for future research. Electronic supplementary material The online version of this article (doi:10.1186/s12874-017-0332-6) contains supplementary material, which is available to authorized users.
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              DNA methylation-based classification and grading system for meningioma: a multicentre, retrospective analysis.

              The WHO classification of brain tumours describes 15 subtypes of meningioma. Nine of these subtypes are allotted to WHO grade I, and three each to grade II and grade III. Grading is based solely on histology, with an absence of molecular markers. Although the existing classification and grading approach is of prognostic value, it harbours shortcomings such as ill-defined parameters for subtypes and grading criteria prone to arbitrary judgment. In this study, we aimed for a comprehensive characterisation of the entire molecular genetic landscape of meningioma to identify biologically and clinically relevant subgroups.
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                Author and article information

                Contributors
                benjamin.brokinkel@ukmuenster.de
                Journal
                Neurosurg Rev
                Neurosurg Rev
                Neurosurgical Review
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0344-5607
                1437-2320
                18 August 2020
                18 August 2020
                2021
                : 44
                : 3
                : 1713-1720
                Affiliations
                [1 ]GRID grid.16149.3b, ISNI 0000 0004 0551 4246, Department of Neurosurgery, , University Hospital Münster, ; Albert-Schweitzer-Campus 1, Building A1, 48149 Munster, Germany
                [2 ]GRID grid.16149.3b, ISNI 0000 0004 0551 4246, Institute for Clinical Radiology, , University Hospital Münster, ; Münster, Germany
                [3 ]GRID grid.16149.3b, ISNI 0000 0004 0551 4246, Institute of Neuropathology, , University Hospital Münster, ; Münster, Germany
                [4 ]GRID grid.5949.1, ISNI 0000 0001 2172 9288, Institute of Biostatistics and Clinical Research, , University Münster, ; Münster, Germany
                Author information
                http://orcid.org/0000-0003-3462-3479
                Article
                1369
                10.1007/s10143-020-01369-1
                8397672
                32809081
                c856bbf2-91ba-4544-9739-64c4afb781eb
                © The Author(s) 2020, corrected publication 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 May 2020
                : 26 July 2020
                : 12 August 2020
                Categories
                Original Article
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2021

                Surgery
                meningiomas,microsurgery,progression,recurrence,simpson grading
                Surgery
                meningiomas, microsurgery, progression, recurrence, simpson grading

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