Evidence map›Paper›PMID 38023177›Full record

ArticleFrontiers in oncology2023

A prognostic model for tumor recurrence and progression after meningioma surgery: preselection for further molecular work-up.

Luis Padevit, Flavio Vasella, Jason Friedman, Valentino Mutschler, Freya Jenkins, Ulrike Held, Elisabeth Jane Rushing, Hans-Georg Wirsching, Michael Weller, Luca Regli and 1 more

Abstract read
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Luis PadevitDepartment of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.
Flavio VasellaDepartment of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.
Jason FriedmanDepartment of Informatics, Eidgenössische Technische Hochschule (ETH) Zürich, Zurich, Switzerland.
Valentino MutschlerDepartment of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.
Freya JenkinsDepartment of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.
Ulrike HeldEpidemiology, Biostatistics and Prevention Institute (EBPI), University of Zurich, Zurich, Switzerland.
Elisabeth Jane RushingDepartment of Neuropathology, University Hospital and University of Zurich, Zurich, Switzerland.
Hans-Georg WirschingDepartment of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.
Michael WellerDepartment of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.
Luca RegliDepartment of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.
Marian Christoph NeidertDepartment of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The 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. Methods: All 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. Results: Out 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). Conclusion: The 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.

Indexed as

classificationimmunohistochemistrymeningiomaprediction modelpreselectionprogressionrecurrence

Identifiers

PMID38023177
PMCPMC10646388

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.