Stochastic differential equation modeling approach for grading astrocytomas on brain MRI images
Abstract Astrocytomas are among the most prevalent primary brain tumors and are classified into four grades by the World Health Organization. Accurate grading is essential for guiding treatment, as therapeutic strategies depend heavily on tumor grade. This paper presents a new preoperative classific...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-06144-0 |
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| author | Mahsa Raisi-Nafchi Mahnoosh Tajmirriahi Hossein Rabbani Zahra Amini |
| author_facet | Mahsa Raisi-Nafchi Mahnoosh Tajmirriahi Hossein Rabbani Zahra Amini |
| author_sort | Mahsa Raisi-Nafchi |
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| description | Abstract Astrocytomas are among the most prevalent primary brain tumors and are classified into four grades by the World Health Organization. Accurate grading is essential for guiding treatment, as therapeutic strategies depend heavily on tumor grade. This paper presents a new preoperative classification method for astrocytomas, addressing the issue of data scarcity in medical imaging. This work leverages an advanced statistical modeling approach based on stochastic differential equations to analyze post-contrast T1-weighted brain MRI images that require minimal data and offer rapid processing times. In this method, the alpha-stable nature of MRI images is represented by applying a fractional Laplacian filter, and the parameters of the resulting alpha-stable distribution are fed to classifiers to detect the grade of astrocytomas. The method is implemented in both 1D and 2D processing modes, with customized preprocessing for each. Three classification algorithms were evaluated: support vector machine, K-nearest neighbor, and random forest. In the three-class classification task (Grades II–IV), the support vector machine exhibited superior performance, achieving accuracy, sensitivity, and specificity of 98.49%, 98.42%, and 99.23% in 2D mode, and 93.52%, 93.23%, and 96.72% in 1D mode. The results indicate that the proposed framework has the potential to significantly enhance preoperative grading of astrocytomas. |
| format | Article |
| id | doaj-art-b463cf6fe45345c4a1ac643b88b1b1da |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-b463cf6fe45345c4a1ac643b88b1b1da2025-08-20T03:03:42ZengNature PortfolioScientific Reports2045-23222025-07-0115111410.1038/s41598-025-06144-0Stochastic differential equation modeling approach for grading astrocytomas on brain MRI imagesMahsa Raisi-Nafchi0Mahnoosh Tajmirriahi1Hossein Rabbani2Zahra Amini3Bioimaging and Biomedical Engineering Department, School of Advanced Technologies in Medicine, Isfahan University of Medical SciencesBioelectrics and Biomedical Engineering Department, Medical Image and Signal Processing Research Centre, School of Advanced Technologies in Medicine, Isfahan University of Medical SciencesBioelectrics and Biomedical Engineering Department, Medical Image and Signal Processing Research Centre, School of Advanced Technologies in Medicine, Isfahan University of Medical SciencesBioimaging and Biomedical Engineering Department, School of Advanced Technologies in Medicine, Isfahan University of Medical SciencesAbstract Astrocytomas are among the most prevalent primary brain tumors and are classified into four grades by the World Health Organization. Accurate grading is essential for guiding treatment, as therapeutic strategies depend heavily on tumor grade. This paper presents a new preoperative classification method for astrocytomas, addressing the issue of data scarcity in medical imaging. This work leverages an advanced statistical modeling approach based on stochastic differential equations to analyze post-contrast T1-weighted brain MRI images that require minimal data and offer rapid processing times. In this method, the alpha-stable nature of MRI images is represented by applying a fractional Laplacian filter, and the parameters of the resulting alpha-stable distribution are fed to classifiers to detect the grade of astrocytomas. The method is implemented in both 1D and 2D processing modes, with customized preprocessing for each. Three classification algorithms were evaluated: support vector machine, K-nearest neighbor, and random forest. In the three-class classification task (Grades II–IV), the support vector machine exhibited superior performance, achieving accuracy, sensitivity, and specificity of 98.49%, 98.42%, and 99.23% in 2D mode, and 93.52%, 93.23%, and 96.72% in 1D mode. The results indicate that the proposed framework has the potential to significantly enhance preoperative grading of astrocytomas.https://doi.org/10.1038/s41598-025-06144-0AstrocytomaBrain tumor gradingInnovation modelStochastic differential equationSDE |
| spellingShingle | Mahsa Raisi-Nafchi Mahnoosh Tajmirriahi Hossein Rabbani Zahra Amini Stochastic differential equation modeling approach for grading astrocytomas on brain MRI images Scientific Reports Astrocytoma Brain tumor grading Innovation model Stochastic differential equation SDE |
| title | Stochastic differential equation modeling approach for grading astrocytomas on brain MRI images |
| title_full | Stochastic differential equation modeling approach for grading astrocytomas on brain MRI images |
| title_fullStr | Stochastic differential equation modeling approach for grading astrocytomas on brain MRI images |
| title_full_unstemmed | Stochastic differential equation modeling approach for grading astrocytomas on brain MRI images |
| title_short | Stochastic differential equation modeling approach for grading astrocytomas on brain MRI images |
| title_sort | stochastic differential equation modeling approach for grading astrocytomas on brain mri images |
| topic | Astrocytoma Brain tumor grading Innovation model Stochastic differential equation SDE |
| url | https://doi.org/10.1038/s41598-025-06144-0 |
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