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Advancing personalized prognosis in atypical and anaplastic meningiomas through interpretable machine learning models

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Abstract

Purpose

The primary purpose of this study was to utilize machine learning (ML) models to create a web application that can predict survival outcomes for patients diagnosed with atypical and anaplastic meningiomas.

Methods

In this retrospective cohort study, patients diagnosed with WHO grade II and III meningiomas were selected from the National Cancer Database (NCDB) to analyze survival outcomes at 12, 36, and 60 months. Five machine learning algorithms - TabPFN, TabNet, XGBoost, LightGBM, and Random Forest were employed and optimized using the Optuna library for hyperparameter tuning. The top-performing models were then deployed into our web-based application.

Results

From the NCDB, 12,197 adult patients diagnosed with histologically confirmed WHO grade II and III meningiomas were retrieved. The mean age was 61 (± 20), and 6,847 (56.1%) of these were females. Performance evaluation indicated that the top-performing models for each outcome were the models built with the TabPFN algorithm. The TabPFN models yielded area under the receiver operating characteristic (AUROC) values of 0.805, 0.781, and 0.815 in predicting 12-, 36-, and 60-month mortality, respectively.

Conclusion

With the continuous growth of neuro-oncology data, ML algorithms act as key tools in predicting survival outcomes for WHO grade II and III meningioma patients. By incorporating these interpretable models into a web application, we can practically utilize them to improve risk evaluation and prognosis for meningioma patients.

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Data Availability

Restrictions apply to the availability of these data. Data were obtained from the NCDB, a prospectively maintained repository collaboratively developed by the Commission on Cancer (CoC) of the American College of Surgeons and the American Cancer Society.

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Acknowledgements

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Funding

This research received no external funding.

Author information

Authors and Affiliations

  1. Department of Neurosurgery, Mount Sinai Health System, New York, NY, USA

    Mert Karabacak, Alejandro Carrasquilla, Raj K. Shrivastava & Konstantinos Margetis

  2. School of Medicine, SUNY Downstate Health Sciences University, New York, NY, USA

    Pemla Jagtiani

Authors
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  4. Raj K. Shrivastava
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  5. Konstantinos Margetis
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Contributions

Conceptualization, MK, PJ, AC, RS, and KM; Methodology, MK, and KM; Software, MK; Formal Analysis, MK; Data Curation, MK; Writing – Original Draft Preparation, MK, and PJ; Writing – Review & Editing, AC, RS and KM; Visualization, MK; Supervision, RS, and KM; Project Administration, MK, and KM.

Corresponding author

Correspondence to Konstantinos Margetis.

Ethics declarations

Disclaimer for the use of web application

The web application should not be used to guide any clinical decisions, including diagnosis and treatment. The authors make no warranties or representations, express or implied, regarding the accuracy, timeliness, relevance, or utility of the information contained in this tool.

Statement for NCDB use

The Commission on Cancer (CoC) of the American College of Surgeons and the American Cancer Society is the source of the data used herein; none of these institutions have verified and are not responsible for the statistical validity of the data analysis or the conclusions derived by the authors.

Institutional review board statement

This study was deemed exempt from approval by the Icahn School of Medicine at Mount Sinai institutional review board because it involved analysis of deidentified patient data.

Source code

The source code for preprocessing and analyzing the data is available on GitHub (https://github.com/mertkarabacak/NCDB-Meningioma).

Conflict of interest

The authors declare no conflict of interest.

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Karabacak, M., Jagtiani, P., Carrasquilla, A. et al. Advancing personalized prognosis in atypical and anaplastic meningiomas through interpretable machine learning models. J Neurooncol 164, 671–681 (2023). https://doi.org/10.1007/s11060-023-04463-8

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  • DOI: https://doi.org/10.1007/s11060-023-04463-8

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