Heterogeneous biomechanical/mathematical modeling of spatial prediction of glioblastoma progression using magnetic resonance imaging-based finite element method

Ghahramani, Mohammad Reza; Bavi, Omid

doi:10.1016/j.cmpb.2024.108441

Computer Methods and Programs in Biomedicine

2024

DOI: 10.1016/j.cmpb.2024.108441

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Heterogeneous biomechanical/mathematical modeling of spatial prediction of glioblastoma progression using magnetic resonance imaging-based finite element method

Mohammad Reza Ghahramani,

Omid Bavi

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2024

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Cited by 2 publications

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A fractional model of tumor growth with a free boundary

Esmaili,

Heydari,

Razzaghi

2024

J. Appl. Math. Comput.

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A fractional model of tumor growth with a free boundary

Esmaili,

Heydari,

Razzaghi

2024

J. Appl. Math. Comput.

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Assessment of MGMT promoter methylation status in glioblastoma using deep learning features from multi-sequence MRI of intratumoral and peritumoral regions

Yu,

Zhou,

et al. 2024

Cancer Imaging

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Objective This study aims to evaluate the effectiveness of deep learning features derived from multi-sequence magnetic resonance imaging (MRI) in determining the O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status among glioblastoma patients. Methods Clinical, pathological, and MRI data of 356 glioblastoma patients (251 methylated, 105 unmethylated) were retrospectively examined from the public dataset The Cancer Imaging Archive. Each patient underwent preoperative multi-sequence brain MRI scans, which included T1-weighted imaging (T1WI) and contrast-enhanced T1-weighted imaging (CE-T1WI). Regions of interest (ROIs) were delineated to identify the necrotic tumor core (NCR), enhancing tumor (ET), and peritumoral edema (PED). The ET and NCR regions were categorized as intratumoral ROIs, whereas the PED region was categorized as peritumoral ROIs. Predictive models were developed using the Transformer algorithm based on intratumoral, peritumoral, and combined MRI features. The area under the receiver operating characteristic curve (AUC) was employed to assess predictive performance. Results The ROI-based models of intratumoral and peritumoral regions, utilizing deep learning algorithms on multi-sequence MRI, were capable of predicting MGMT promoter methylation status in glioblastoma patients. The combined model of intratumoral and peritumoral regions exhibited superior diagnostic performance relative to individual models, achieving an AUC of 0.923 (95% confidence interval [CI]: 0.890 – 0.948) in stratified cross-validation, with sensitivity and specificity of 86.45% and 87.62%, respectively. Conclusion The deep learning model based on MRI data can effectively distinguish between glioblastoma patients with and without MGMT promoter methylation.

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Heterogeneous biomechanical/mathematical modeling of spatial prediction of glioblastoma progression using magnetic resonance imaging-based finite element method

Cited by 2 publications

References 54 publications

A fractional model of tumor growth with a free boundary

A fractional model of tumor growth with a free boundary

Assessment of MGMT promoter methylation status in glioblastoma using deep learning features from multi-sequence MRI of intratumoral and peritumoral regions

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