Deep Learning for Glioblastoma Multiforme Detection from MRI: A Statistical Analysis for Demographic Bias
Glioblastoma, IDH-wildtype (GBM), is the most aggressive and complex brain tumour classified by the World Health Organization (WHO), characterised by high mortality rates and diagnostic limitations inherent to invasive conventional procedures. Early detection is essential for improving patient outco...
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2025-06-01
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| author | Kebin Contreras Julio Gutierrez-Rengifo Oscar Casanova-Carvajal Angel Luis Alvarez Patricia E. Vélez-Varela Ana Lorena Urbano-Bojorge |
| author_facet | Kebin Contreras Julio Gutierrez-Rengifo Oscar Casanova-Carvajal Angel Luis Alvarez Patricia E. Vélez-Varela Ana Lorena Urbano-Bojorge |
| author_sort | Kebin Contreras |
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| description | Glioblastoma, IDH-wildtype (GBM), is the most aggressive and complex brain tumour classified by the World Health Organization (WHO), characterised by high mortality rates and diagnostic limitations inherent to invasive conventional procedures. Early detection is essential for improving patient outcomes, underscoring the need for non-invasive diagnostic tools. This study presents a convolutional neural network (CNN) specifically optimised for GBM detection from T1-weighted magnetic resonance imaging (MRI), with systematic evaluations of layer depth, activation functions, and hyperparameters. The model was trained on the RSNA-MICCAI data set and externally validated on the Erasmus Glioma Database (EGD), which includes gliomas of various grades and preserves cranial structures, unlike the skull-stripped RSNA-MICCAI images. This morphological discrepancy demonstrates the generalisation capacity of the model across anatomical and acquisition differences, achieving an F1-score of 0.88. Furthermore, statistical tests, such as Shapiro–Wilk, Mann–Whitney U, and Chi-square, confirmed the absence of demographic bias in model predictions, based on <i>p</i>-values, confidence intervals, and statistical power analyses supporting its demographic fairness. The proposed model achieved an area under the curve–receiver operating characteristic (AUC-ROC) of 0.63 on the RSNA-MICCAI test set, surpassing all prior results submitted to the BraTS 2021 challenge, and establishing a reliable and generalisable approach for non-invasive GBM detection. |
| format | Article |
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| institution | OA Journals |
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| language | English |
| publishDate | 2025-06-01 |
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| series | Applied Sciences |
| spelling | doaj-art-ce006a32fb7d49aeb28a75c23acde2f52025-08-20T02:32:56ZengMDPI AGApplied Sciences2076-34172025-06-011511627410.3390/app15116274Deep Learning for Glioblastoma Multiforme Detection from MRI: A Statistical Analysis for Demographic BiasKebin Contreras0Julio Gutierrez-Rengifo1Oscar Casanova-Carvajal2Angel Luis Alvarez3Patricia E. Vélez-Varela4Ana Lorena Urbano-Bojorge5Departamento de Biología, Facultad de Ciencias Naturales, Exactas y de la Educación FACNED, Universidad del Cauca, Popayan 190002, ColombiaDepartamento de Ciencias de la Computación, Universidad Industrial de Santander, Bucaramanga 680006, ColombiaCentro de Tecnología Biomédica, Campus de Montegancedo, Universidad Politécnica de Madrid, 28040 Madrid, SpainEscuela de Ingeniería de Fuenlabrada, Universidad Rey Juan Carlos, 28922 Madrid, SpainDepartamento de Biología, Facultad de Ciencias Naturales, Exactas y de la Educación FACNED, Universidad del Cauca, Popayan 190002, ColombiaDepartamento de Biología, Facultad de Ciencias Naturales, Exactas y de la Educación FACNED, Universidad del Cauca, Popayan 190002, ColombiaGlioblastoma, IDH-wildtype (GBM), is the most aggressive and complex brain tumour classified by the World Health Organization (WHO), characterised by high mortality rates and diagnostic limitations inherent to invasive conventional procedures. Early detection is essential for improving patient outcomes, underscoring the need for non-invasive diagnostic tools. This study presents a convolutional neural network (CNN) specifically optimised for GBM detection from T1-weighted magnetic resonance imaging (MRI), with systematic evaluations of layer depth, activation functions, and hyperparameters. The model was trained on the RSNA-MICCAI data set and externally validated on the Erasmus Glioma Database (EGD), which includes gliomas of various grades and preserves cranial structures, unlike the skull-stripped RSNA-MICCAI images. This morphological discrepancy demonstrates the generalisation capacity of the model across anatomical and acquisition differences, achieving an F1-score of 0.88. Furthermore, statistical tests, such as Shapiro–Wilk, Mann–Whitney U, and Chi-square, confirmed the absence of demographic bias in model predictions, based on <i>p</i>-values, confidence intervals, and statistical power analyses supporting its demographic fairness. The proposed model achieved an area under the curve–receiver operating characteristic (AUC-ROC) of 0.63 on the RSNA-MICCAI test set, surpassing all prior results submitted to the BraTS 2021 challenge, and establishing a reliable and generalisable approach for non-invasive GBM detection.https://www.mdpi.com/2076-3417/15/11/6274glioblastoma multiformebiasstatisticconvolutional neural networksmagnetic resonance imagingdeep learning |
| spellingShingle | Kebin Contreras Julio Gutierrez-Rengifo Oscar Casanova-Carvajal Angel Luis Alvarez Patricia E. Vélez-Varela Ana Lorena Urbano-Bojorge Deep Learning for Glioblastoma Multiforme Detection from MRI: A Statistical Analysis for Demographic Bias Applied Sciences glioblastoma multiforme bias statistic convolutional neural networks magnetic resonance imaging deep learning |
| title | Deep Learning for Glioblastoma Multiforme Detection from MRI: A Statistical Analysis for Demographic Bias |
| title_full | Deep Learning for Glioblastoma Multiforme Detection from MRI: A Statistical Analysis for Demographic Bias |
| title_fullStr | Deep Learning for Glioblastoma Multiforme Detection from MRI: A Statistical Analysis for Demographic Bias |
| title_full_unstemmed | Deep Learning for Glioblastoma Multiforme Detection from MRI: A Statistical Analysis for Demographic Bias |
| title_short | Deep Learning for Glioblastoma Multiforme Detection from MRI: A Statistical Analysis for Demographic Bias |
| title_sort | deep learning for glioblastoma multiforme detection from mri a statistical analysis for demographic bias |
| topic | glioblastoma multiforme bias statistic convolutional neural networks magnetic resonance imaging deep learning |
| url | https://www.mdpi.com/2076-3417/15/11/6274 |
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