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• W4385830108 abstract "Abstract The infiltrative nature of malignant gliomas results in active tumor spreading into the peritumoral edema, which is not visible in conventional magnetic resonance imaging (cMRI) even after contrast injection. MR relaxometry (qMRI) measures relaxation rates dependent on tissue properties and can offer additional contrast mechanisms to highlight the non-enhancing infiltrative tumor. To investigate if qMRI data provides additional information compared to cMRI sequences when considering deep learning-based brain tumor detection and segmentation, preoperative conventional (T1w per- and post-contrast, T2w and FLAIR) and quantitative (pre- and post-contrast R 1 , R 2 and proton density) MR data was obtained from 23 patients with typical radiological findings suggestive of a high-grade glioma. 2D deep learning models were trained on transversal slices ( n = 528) for tumor detection and segmentation using either cMRI or qMRI. Moreover, trends in quantitative R 1 and R 2 rates of regions identified as relevant for tumor detection by model explainability methods were qualitatively analyzed. Tumor detection and segmentation performance for models trained with a combination of qMRI pre- and post-contrast was the highest (detection Matthews correlation coefficient (MCC) = 0.72, segmentation dice similarity coefficient (DSC) = 0.90), however, the difference compared to cMRI was not statistically significant. Overall analysis of the relevant regions identified using model explainability showed no differences between models trained on cMRI or qMRI. When looking at the individual cases, relaxation rates of brain regions outside the annotation and identified as relevant for tumor detection exhibited changes after contrast injection similar to region inside the annotation in the majority of cases. In conclusion, models trained on qMRI data obtained similar detection and segmentation performance to those trained on cMRI data, with the advantage of quantitatively measuring brain tissue properties within a similar scan time. When considering individual patients, the analysis of relaxation rates of regions identified by model explainability suggests the presence of infiltrative tumor outside the cMRI-based tumor annotation." @default.
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• W4385830108 date "2023-09-01" @default.
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• W4385830108 title "Deep learning-based detection and identification of brain tumor biomarkers in quantitative MR-images" @default.
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• W4385830108 doi "https://doi.org/10.1088/2632-2153/acf095" @default.
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