Automatic detection of cognitive impairment in patients with white matter hyperintensity and causal analysis of related factors using artificial intelligence of MRI

Feng, Junbang; Hui, Dongming; Zheng, Qingqing; Guo, Yi; Xia, Yuwei; Shi, Feng; Zhou, Qing; Yu, Fei; He, Xiaojing; Wang, Shike; Li, Chuanming

doi:10.1016/j.compbiomed.2024.108684

Cited by 1 publication

(2 citation statements)

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“…While these well-established metrics capture the quality of the segmentation, they do not assess quantitative differences between segmentations in terms of voxel-based gray-level statistics and patterns contained therein. This information, which is captured by radiomic features, is currently evaluated in a wide array of diseases of the brain, with recent MRI applications including detection of cognitive impairment [8], distinguishing between active and chronic multiple myeloma lesions [9], EGFR and HER2 status prediction in adenocarcinoma brain metastases [5], corticospinal tract involvement in glioma [4], and prediction of local tumor control in patients with brain metastases following postoperative radiotherapy [6].…”

Section: Svanera Et Al Originally Used Three Metrics Which Were Previ...mentioning

confidence: 99%

“…The prior evaluation of Cerebrum-7T used three measures of segmentation similarity for evaluation of segmentation accuracy, with an emphasis on the Dice coefficient [2], but did not explore the impact of differences in segmentation masks on radiomic features at 7T that capture intra-volume properties such as signal heterogeneity. This topic is of interest because radiomic features -which are now widely used for the analysis of both focal brain lesions and diffuse abnormalities within the brain [3][4][5][6][7][8][9]-are sensitive to the type of segmentation method [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Reproducibility of radiomic features of the brain on ultrahigh-resolution MRI at 7 Tesla: a comparison of different segmentation techniques

Klinger,

Leithner,

Woo

et al. 2024

Preprint

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ObjectivesTo determine the impact of segmentation techniques on radiomic features extracted from ultrahigh-field (UHF) MRI of the brain.Materials and MethodsTwenty-one 7T MRI scans of the brain, including a 3D magnetization-prepared two rapid acquisition gradient echo (MP2RAGE) T1-weighted sequence with an isotropic 0.63 mm³ voxel size, were analyzed. Radiomic features (histogram, texture, and shape; total n=101) from six brain regions -cerebral gray and white matter, basal ganglia, ventricles, cerebellum, and brainstem-were extracted from segmentation masks constructed with four different techniques: the iGT (reference standard), based on a custom pipeline that combined automatic segmentation tools and expert reader correction; the deep-learning algorithm Cerebrum-7T; the Freesurfer-v7 software suite; and the Nighres algorithm. Principal components (PCs) were calculated for histogram and texture features. To test the reproducibility of radiomic features, intraclass correlation coefficients (ICC) were used to compare Cerebrum-7, Freesurfer-v7, and Nighres to the iGT, respectively.ResultsFor histogram PCs, median ICCs for Cerebrum-7T, Freesurfer-v7, and Nighres were 0.99, 0.42, and 0.11 for the gray matter; 0.84, 0.25, and 0.43 for the basal ganglia; 0.89, 0.063, and 0.036 for the white matter; 0.84, 0.21, and 0.33 for the ventricles; 0.94, 0.64, and 0.93 for the cerebellum; and 0.78, 0.21, and 0.53 for the brainstem. For texture PCs, median ICCs for Cerebrum-7T, Freesurfer-v7, and Nighres were 0.95, 0.21, and 0.15 for the gray matter; 0.70, 0.36, and 0.023 for the basal ganglia; 0.91, 0.25, and 0.023 for the white matter; 0.80, 0.75, and 0.59 for the ventricles; 0.95, 0.43, and 0.86 for the cerebellum; and 0.72, 0.39, and 0.46 for the brainstem. For shape features, median ICCs for Cerebrum-7T, FreeSurfer-v7, and Nighres were 0.99, 0.91, and 0.36 for the gray matter; 0.89, 0.90, and 0.13 for the basal ganglia; 0.98, 0.91, and 0.027 for the white matter; 0.91, 0.91, and 0.36 for the ventricles; 0.80, 0.68, and 0.47 for the cerebellum; and 0.79, 0.17, and 0.15 for the brainstem.ConclusionsRadiomic features in UHF MRI of the brain show substantial variability depending on the segmentation algorithm. The deep learning algorithm Cerebrum-7T enabled the highest reproducibility. Dedicated software tools for UHF MRI may be needed to achieve more stable results.

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Section: Svanera Et Al Originally Used Three Metrics Which Were Previ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reproducibility of radiomic features of the brain on ultrahigh-resolution MRI at 7 Tesla: a comparison of different segmentation techniques

Klinger,

Leithner,

Woo

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Automatic detection of cognitive impairment in patients with white matter hyperintensity and causal analysis of related factors using artificial intelligence of MRI

Cited by 1 publication

References 46 publications

Reproducibility of radiomic features of the brain on ultrahigh-resolution MRI at 7 Tesla: a comparison of different segmentation techniques

Reproducibility of radiomic features of the brain on ultrahigh-resolution MRI at 7 Tesla: a comparison of different segmentation techniques

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