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Journal of Neuro-Oncology

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28-09-2023 | Meningioma | Research

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

Authors: Mert Karabacak, Pemla Jagtiani, Alejandro Carrasquilla, Raj K. Shrivastava, Konstantinos Margetis

Published in: Journal of Neuro-Oncology | Issue 3/2023

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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Title: Advancing personalized prognosis in atypical and anaplastic meningiomas through interpretable machine learning models
Authors: Mert Karabacak
Pemla Jagtiani
Alejandro Carrasquilla
Raj K. Shrivastava
Konstantinos Margetis
Publication date: 28-09-2023
Publisher: Springer US
Keywords: Meningioma
Meningioma
Published in: Journal of Neuro-Oncology / Issue 3/2023
Print ISSN: 0167-594X
Electronic ISSN: 1573-7373
DOI: https://doi.org/10.1007/s11060-023-04463-8

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Purpose

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

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