Editorial: Nanomaterial and nanostructures for cancer and pathogenic infection diagnosis and therapy

Frontiers in Nanotechnology, Journal Year: 2025, Volume and Issue: 7

Published: Jan. 27, 2025

Language: Английский

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

Cancer Imaging, Journal Year: 2024, Volume and Issue: 24(1)

Published: Dec. 23, 2024

Abstract Objective This study aims to evaluate the effectiveness of deep learning features derived from multi-sequence magnetic resonance imaging (MRI) in determining O 6 -methylguanine-DNA methyltransferase (MGMT) promoter methylation status among glioblastoma patients. Methods Clinical, pathological, and MRI data 356 patients (251 methylated, 105 unmethylated) were retrospectively examined public dataset The Cancer Imaging Archive. Each patient underwent preoperative brain scans, which included T1-weighted (T1WI) contrast-enhanced (CE-T1WI). Regions interest (ROIs) delineated identify necrotic tumor core (NCR), enhancing (ET), peritumoral edema (PED). ET NCR regions categorized as intratumoral ROIs, whereas PED region was ROIs. Predictive models developed using Transformer algorithm based on intratumoral, peritumoral, combined features. area under receiver operating characteristic curve (AUC) employed assess predictive performance. Results ROI-based regions, utilizing algorithms MRI, capable predicting MGMT model exhibited superior diagnostic performance relative individual models, achieving an AUC 0.923 (95% confidence interval [CI]: 0.890 – 0.948) stratified cross-validation, with sensitivity specificity 86.45% 87.62%, respectively. Conclusion can effectively distinguish between without methylation.

Language: Английский

A fractional model of tumor growth with a free boundary

Journal of Applied Mathematics and Computing, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 26, 2024

Language: Английский