Radiology Artificial Intelligence, Journal Year: 2024, Volume and Issue: 6(4)
Published: June 12, 2024
Language: Английский
Radiology Artificial Intelligence, Journal Year: 2024, Volume and Issue: 6(4)
Published: June 12, 2024
Language: Английский
Neuroradiology, Journal Year: 2024, Volume and Issue: 66(5), P. 761 - 773
Published: March 12, 2024
Abstract Purpose This study aimed to perform multimodal analysis by vision transformer (vViT) in predicting O6-methylguanine-DNA methyl transferase (MGMT) promoter status among adult patients with diffuse glioma using demographics (sex and age), radiomic features, MRI. Methods The training test datasets contained 122 1,570 images 30 484 images, respectively. features were extracted from enhancing tumors (ET), necrotic tumor cores (NCR), the peritumoral edematous/infiltrated tissues (ED) contrast-enhanced T1-weighted (CE-T1WI) T2-weighted (T2WI). vViT had 9 sectors; 1 demographic sector, 6 sectors (CE-T1WI ET, CE-T1WI NCR, ED, T2WI ED), 2 image (CE-T1WI, T2WI). Accuracy area under curve of receiver-operating characteristics (AUC-ROC) calculated for dataset. performance was compared AlexNet, GoogleNet, VGG16, ResNet McNemar Delong test. Permutation importance (PI) Mann–Whitney U performed. Results accuracy 0.833 (95% confidence interval [95%CI]: 0.714–0.877) 0.840 (0.650–0.995) patient-based analysis. higher than VGG16 ResNet, AUC-ROC GoogleNet ( p <0.05). ED demonstrated highest (PI=0.239, 95%CI: 0.237–0.240) all other <0.0001). Conclusion is a competent deep learning model MGMT status. most dominant contribution.
Language: Английский
Citations
7Cancer 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
Citations
4Iran Journal of Computer Science, Journal Year: 2024, Volume and Issue: unknown
Published: Dec. 31, 2024
Language: Английский
Citations
3Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown
Published: April 4, 2025
Language: Английский
Citations
0Magnetic Resonance Imaging, Journal Year: 2024, Volume and Issue: 111, P. 266 - 276
Published: May 29, 2024
To evaluate the performance of multimodal model, termed variable Vision Transformer (vViT), in task predicting isocitrate dehydrogenase (IDH) status among adult patients with diffuse glioma.
Language: Английский
Citations
2Deleted Journal, Journal Year: 2024, Volume and Issue: unknown
Published: June 28, 2024
To assess the effectiveness of vViT model for predicting postoperative renal function decline by leveraging clinical data, medical images, and image-derived features; to identify most dominant factor influencing this prediction.
Language: Английский
Citations
2Radiology Artificial Intelligence, Journal Year: 2024, Volume and Issue: 6(4)
Published: June 12, 2024
Language: Английский
Citations
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