A prognostic model for tumor recurrence and progression after meningioma surgery: preselection for further molecular work-up
PurposeThe selection of patients for further therapy after meningioma surgery remains a challenge. Progress has been made in this setting in selecting patients that are more likely to have an aggressive disease course by using molecular tests such as gene panel sequencing and DNA methylation profili...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-11-01
|
| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1279933/full |
| _version_ | 1797643343170109440 |
|---|---|
| author | Luis Padevit Flavio Vasella Jason Friedman Valentino Mutschler Freya Jenkins Ulrike Held Elisabeth Jane Rushing Hans-Georg Wirsching Michael Weller Luca Regli Marian Christoph Neidert Marian Christoph Neidert |
| author_facet | Luis Padevit Flavio Vasella Jason Friedman Valentino Mutschler Freya Jenkins Ulrike Held Elisabeth Jane Rushing Hans-Georg Wirsching Michael Weller Luca Regli Marian Christoph Neidert Marian Christoph Neidert |
| author_sort | Luis Padevit |
| collection | DOAJ |
| description | PurposeThe selection of patients for further therapy after meningioma surgery remains a challenge. Progress has been made in this setting in selecting patients that are more likely to have an aggressive disease course by using molecular tests such as gene panel sequencing and DNA methylation profiling. The aim of this study was to create a preselection tool warranting further molecular work-up.MethodsAll patients undergoing surgery for resection or biopsy of a cranial meningioma from January 2013 until December 2018 at the University Hospital Zurich with available tumor histology were included. Various prospectively collected clinical, radiological, histological and immunohistochemical variables were analyzed and used to train a logistic regression model to predict tumor recurrence or progression. Regression coefficients were used to generate a scoring system grading every patient into low, intermediate, and high-risk group for tumor progression or recurrence.ResultsOut of a total of 13 variables preselected for this study, previous meningioma surgery, Simpson grade, progesterone receptor staining as well as presence of necrosis and patternless growth on histopathological analysis of 378 patients were included into the final model. Discrimination showed an AUC of 0.81 (95% CI 0.73 – 0.88), the model was well-calibrated. Recurrence-free survival was significantly decreased in patients in intermediate and high-risk score groups (p-value < 0.001).ConclusionThe proposed prediction model showed good discrimination and calibration. This prediction model is based on easily obtainable information and can be used as an adjunct for patient selection for further molecular work-up in a tertiary hospital setting. |
| first_indexed | 2024-03-11T14:13:26Z |
| format | Article |
| id | doaj.art-a6d4b31f6bc54f80824e785a0aa8410c |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-03-11T14:13:26Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-a6d4b31f6bc54f80824e785a0aa8410c2023-11-01T16:28:23ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-11-011310.3389/fonc.2023.12799331279933A prognostic model for tumor recurrence and progression after meningioma surgery: preselection for further molecular work-upLuis Padevit0Flavio Vasella1Jason Friedman2Valentino Mutschler3Freya Jenkins4Ulrike Held5Elisabeth Jane Rushing6Hans-Georg Wirsching7Michael Weller8Luca Regli9Marian Christoph Neidert10Marian Christoph Neidert11Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, SwitzerlandDepartment of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, SwitzerlandDepartment of Informatics, Eidgenössische Technische Hochschule (ETH) Zürich, Zurich, SwitzerlandDepartment of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, SwitzerlandDepartment of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, SwitzerlandEpidemiology, Biostatistics and Prevention Institute (EBPI), University of Zurich, Zurich, SwitzerlandDepartment of Neuropathology, University Hospital and University of Zurich, Zurich, SwitzerlandDepartment of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, SwitzerlandDepartment of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, SwitzerlandDepartment of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, SwitzerlandDepartment of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, SwitzerlandDepartment of Neurosurgery, Kantonsspital St. Gallen, St. Gallen, SwitzerlandPurposeThe selection of patients for further therapy after meningioma surgery remains a challenge. Progress has been made in this setting in selecting patients that are more likely to have an aggressive disease course by using molecular tests such as gene panel sequencing and DNA methylation profiling. The aim of this study was to create a preselection tool warranting further molecular work-up.MethodsAll patients undergoing surgery for resection or biopsy of a cranial meningioma from January 2013 until December 2018 at the University Hospital Zurich with available tumor histology were included. Various prospectively collected clinical, radiological, histological and immunohistochemical variables were analyzed and used to train a logistic regression model to predict tumor recurrence or progression. Regression coefficients were used to generate a scoring system grading every patient into low, intermediate, and high-risk group for tumor progression or recurrence.ResultsOut of a total of 13 variables preselected for this study, previous meningioma surgery, Simpson grade, progesterone receptor staining as well as presence of necrosis and patternless growth on histopathological analysis of 378 patients were included into the final model. Discrimination showed an AUC of 0.81 (95% CI 0.73 – 0.88), the model was well-calibrated. Recurrence-free survival was significantly decreased in patients in intermediate and high-risk score groups (p-value < 0.001).ConclusionThe proposed prediction model showed good discrimination and calibration. This prediction model is based on easily obtainable information and can be used as an adjunct for patient selection for further molecular work-up in a tertiary hospital setting.https://www.frontiersin.org/articles/10.3389/fonc.2023.1279933/fullmeningiomaprediction modelimmunohistochemistryrecurrenceprogressionclassification |
| spellingShingle | Luis Padevit Flavio Vasella Jason Friedman Valentino Mutschler Freya Jenkins Ulrike Held Elisabeth Jane Rushing Hans-Georg Wirsching Michael Weller Luca Regli Marian Christoph Neidert Marian Christoph Neidert A prognostic model for tumor recurrence and progression after meningioma surgery: preselection for further molecular work-up Frontiers in Oncology meningioma prediction model immunohistochemistry recurrence progression classification |
| title | A prognostic model for tumor recurrence and progression after meningioma surgery: preselection for further molecular work-up |
| title_full | A prognostic model for tumor recurrence and progression after meningioma surgery: preselection for further molecular work-up |
| title_fullStr | A prognostic model for tumor recurrence and progression after meningioma surgery: preselection for further molecular work-up |
| title_full_unstemmed | A prognostic model for tumor recurrence and progression after meningioma surgery: preselection for further molecular work-up |
| title_short | A prognostic model for tumor recurrence and progression after meningioma surgery: preselection for further molecular work-up |
| title_sort | prognostic model for tumor recurrence and progression after meningioma surgery preselection for further molecular work up |
| topic | meningioma prediction model immunohistochemistry recurrence progression classification |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1279933/full |
| work_keys_str_mv | AT luispadevit aprognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT flaviovasella aprognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT jasonfriedman aprognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT valentinomutschler aprognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT freyajenkins aprognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT ulrikeheld aprognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT elisabethjanerushing aprognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT hansgeorgwirsching aprognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT michaelweller aprognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT lucaregli aprognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT marianchristophneidert aprognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT marianchristophneidert aprognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT luispadevit prognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT flaviovasella prognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT jasonfriedman prognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT valentinomutschler prognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT freyajenkins prognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT ulrikeheld prognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT elisabethjanerushing prognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT hansgeorgwirsching prognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT michaelweller prognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT lucaregli prognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT marianchristophneidert prognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup AT marianchristophneidert prognosticmodelfortumorrecurrenceandprogressionaftermeningiomasurgerypreselectionforfurthermolecularworkup |