Evidence map›Paper›PMID 39528858›Full record

SynthesisNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2025

Machine learning-based models for prediction of survival in medulloblastoma: a systematic review and meta-analysis.

Bardia Hajikarimloo, Mohammad Amin Habibi, Mohammadamin Sabbagh Alvani, Sima Osouli Meinagh, Alireza Kooshki, Omid Afkhami-Ardakani, Fatemeh Rasouli, Salem M Tos, Roozbeh Tavanaei, Mohammadhosein Akhlaghpasand and 2 more

Abstract readSystematic ReviewMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 5 pooled it
–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

9 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Machine learning models for predicting response to epidermal growth factor receptor tyrosine kinase inhibitors in non-small cell lung cancer brain metastases: a systematic review and meta-analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
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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

12 authors.

Bardia HajikarimlooDepartment of Neurological Surgery, University of Virginia, Charlottesville, VA, USA. kjh7vp@uvahealth.org.ORCID http://orcid.org/0000-0001-8801-1158
Mohammad Amin HabibiDepartment of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.ORCID http://orcid.org/0000-0001-7600-6925
Mohammadamin Sabbagh AlvaniStudent Research Committee, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Sima Osouli MeinaghDepartment of Neurology, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Alireza KooshkiStudent Research Committee, Birjand University of Medical Sciences, Birjand, Iran.
Omid Afkhami-ArdakaniUniversity of Pécs, Medical School, Pécs, Hungary.
Fatemeh RasouliNorth Khorasan University of Medical Sciences, Bojnurd, Iran.
Salem M TosDepartment of Neurological Surgery, University of Virginia, Charlottesville, VA, USA.ORCID http://orcid.org/0000-0001-7154-6869
Roozbeh TavanaeiFunctional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Mohammadhosein AkhlaghpasandFunctional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Rana HashemiFunctional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Arman HasanzadeFunctional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMedulloblastoma (MB) is the pediatric population's most frequent malignant intracranial lesions. Prognostication plays a crucial role in optimizing treatment strategy in the MB setting. Several studies have developed ML-based models to predict survival outcomes in individuals with MB. In this systematic review and meta-analysis study, we aimed to evaluate the role of ML-based models in predicting survival in MB patients.

methodLiterature records were retrieved on May 14th, 2024, using the relevant key terms without filters in PubMed, Embase, Scopus, and Web of Science. Records were screened according to the eligibility criteria, and the data from the included studies were extracted. The quality assessment was performed using the QUADAS-2 tool. The meta-analysis and sensitivity analysis were conducted using R software.

resultsSix studies were included, with 2771 patients ranging from 46 to 1759 individuals. A total of 23 ML and DL models were developed, 20 of which were ML and three DL. Random forest (RF) was the most frequent classifier, as it was utilized in nine models, followed by support vector machine (SVM). Eight models were included in the meta-analysis. Our meta-analysis revealed a pooled AUC of 0.77 (95% CI: 0.75-0.80). In addition, the radionics-based and genomics-based models had a pooled AUC of 0.77 (95% CI: 0.76-079) and 0.76 (0.63-0.88), respectively (P = 0.77).

conclusionOur results suggested that ML-based models, especially ML algorithms, could play a vital and efficient role in the prediction of survival of patients based on radiomics and genomics.

Indexed as

Cerebellar NeoplasmsMachine LearningMedulloblastomaHumansPrognosisDeep LearningMachine LearningMedulloblastomaRandom ForestSurvival

Identifiers

PMID39528858

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.