ArticleJournal of neuro-oncology2023
Advancing personalized prognosis in atypical and anaplastic meningiomas through interpretable machine learning models.
Article in Journal of neuro-oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning for predicting post-operative outcomes in meningiomas: a systematic review and meta-analysis.Acta neurochirurgica · 2024Pooled it
- Established machine learning matches tabular foundation models in clinical predictions.BMC medical informatics and decision making · 2026Article
- A scoping review of artificial intelligence applications in meningioma from image analysis to prognostic prediction.Discover oncology · 2026Review
- Robust and interpretable deep learning system for prognostic stratification of extranodal natural killer/T-cell lymphoma.European journal of nuclear medicine and molecular imaging · 2025Article
- Machine learning-based diagnostic prediction of minimal change disease: model development study.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe 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.
methodsIn 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.
resultsFrom 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.
conclusionWith 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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Registered trials
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.