Improving radiomics-based isocitrate dehydrogenase 1 prediction in glioma patients using semi-supervised machine learning models

Semi-supervised approaches may improve the performance of radiomics-based ML models in predicting glioma IDH1 status. Using pseudolabels, these models can increase the size of training data, potentially leading to enhancement of model predictive performance. Additionally, these models may improve prediction efficiency by requiring fewer image sequences.

Authors

Shahriar Faghani (ORCID: https://orcid.org/0000-0003-3275-2971)
Mahsa Vatanparast (ORCID: https://orcid.org/0000-0002-9852-0244)
Danial Elyassirad
Benyamin Gheiji
Amir Mahmoud Ahmadzadeh (ORCID: https://orcid.org/0000-0002-2133-4283)
Amirhossein Jafarnezhad

Institutions

Mayo Clinic (US)
Mashhad University of Medical Sciences (IR)
Shiraz University of Medical Sciences (IR)
University of Pennsylvania (US)

Publication Details

Journal: BMC Medical Imaging
Published: 2025-12-09
DOI: https://doi.org/10.1186/s12880-025-02040-1
Primary Topic: Glioma Diagnosis and Treatment
Type: article
Field-Weighted Citation Impact: 0.00

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article

Improving radiomics-based isocitrate dehydrogenase 1 prediction in glioma patients using semi-supervised machine learning models

Shahriar Faghani, Mahsa Vatanparast, Danial Elyassirad, Benyamin Gheiji et al.

Dec 9, 2025

BMC Medical Imaging

Glioma Diagnosis and Treatment

article

Improving radiomics-based isocitrate dehydrogenase 1 prediction in glioma patients using semi-supervised machine learning models

Shahriar Faghani, Mahsa Vatanparast, Danial Elyassirad, Benyamin Gheiji, Amir Mahmoud Ahmadzadeh, Amirhossein Jafarnezhad

December 9, 2025article en

Abstract

Semi-supervised approaches may improve the performance of radiomics-based ML models in predicting glioma IDH1 status. Using pseudolabels, these models can increase the size of training data, potentially leading to enhancement of model predictive performance. Additionally, these models may improve prediction efficiency by requiring fewer image sequences.

BMC Medical ImagingVol. 26(1)

Mayo Clinic (US), Mashhad University of Medical Sciences (IR), Shiraz University of Medical Sciences (IR), University of Pennsylvania (US)

Openalex Percentile: Top 40%

Glioma Diagnosis and Treatment

DOI Open Access PDF OpenAlex

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Improving radiomics-based isocitrate dehydrogenase 1 prediction in glioma patients using semi-supervised machine learning models — Shahriar Faghani, Mahsa Vatanparast, et al. · BMC Medical Imaging (2025) | TGRS Research Map | TGRS