Stability of MRI radiomic features according to various imaging parameters in fast scanned T2-FLAIR for acute ischemic stroke patients

Joo, Leehi; Jung, Sung Eun; Lee, Hyunna; Park, Seo Young; Kim, M; Park, Ji Eun; Choi, Keum Mi

doi:10.1038/s41598-021-96621-z

Cited by 14 publications

(12 citation statements)

References 40 publications

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“…(2) Positive DWI and negative T2 fluid-attenuated inversion recovery (T2 FLAIR) indicated the onset of the patients within 4.5 h. It had also been recommended as an effective indicator for judging the time window of stroke patients with unknown onset, so as to achieve individualized treatment [22]. (3) PWI could display cerebral hemodynamic status.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, DWI could determine the size, location, and onset time of lesions at the early stage of AIS, and had high sensitivity in the examination of small infarcts at the early stage of AIS, shortening the DNT time of patients [ 21 ]. (2) Positive DWI and negative T2 fluid-attenuated inversion recovery (T2 FLAIR) indicated the onset of the patients within 4.5 h. It had also been recommended as an effective indicator for judging the time window of stroke patients with unknown onset, so as to achieve individualized treatment [ 22 ]. (3) PWI could display cerebral hemodynamic status.…”

Section: Discussionmentioning

confidence: 99%

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Application of Multimodal Magnetic Resonance Imaging in Green Channel of Acute and Hyperacute Ischemic Stroke

Zhou¹,

Meng³

et al. 2022

Contrast Media & Molecular Imaging

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The purpose of this study was to observe the effects of multimodal magnetic resonance imaging (MRI) in the green channel of acute and hyperacute ischemic strokes, in order to better guide clinical treatment. The clinical data of 126 patients with acute and hyperacute ischemic stroke who received interventional treatment in the emergency green channel was collected retrospectively. The patients who received multimodal computed tomography (CT) were included in the CT group. Patients who underwent multimodal MRI examinations were included in the MRI group, and the door-to-needle time (DNT), neurological function, and prognosis of the two groups were compared. The result turned out that among the 126 patients included, 40 patients underwent CT examination (CT group) and 86 patients underwent MRI examination (MRI group). A comparison of general data between the CT group and the MRI group showed P > 0.05 . The MRI group’s DNT time (61.23 ± 9.32) min was shorter than that of the CT group (87.22 ± 14.26) min, P < 0.05 . Through comparison, the P values of mRS scores and NIHSS scores in both groups were greater than 0.05. After treatment, the mRS score and NIHSS score of the MRI group was lower, with P < 0.05 . According to the results of this study, it can be concluded that emergency multimodal MRI could shorten the DNT time of patients with acute and hyperacute ischemic stroke, reduce the degree of neurological impairment, and improve the prognosis.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Application of Multimodal Magnetic Resonance Imaging in Green Channel of Acute and Hyperacute Ischemic Stroke

Zhou¹,

Meng³

et al. 2022

Contrast Media & Molecular Imaging

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“…A CCC of >0.85 was chosen as the threshold to indicate a strong correlation (reproducibility). [20][21][22][28][29][30][31][32] In general,a CV of <10% or a CCC of >0.85 is used to indicate a radiomic feature that is reproducible across a study cohort by previous studies. [20][21][22][24][25][26][27][28][29][30][31][32] In this study, we applied these metrics (CV and CCC) in an opposite approach.…”

Section: Discussionmentioning

confidence: 99%

Uniqueness of radiomic features in non‐small cell lung cancer

Zhang

2022

J Applied Clin Med Phys

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Purpose The uniqueness of radiomic features, combined with their reproducibility, determines the reliability of radiomic studies. This study is to test the hypothesis that radiomic features extracted from a defined region of interest (ROI) are unique to the underlying structure (e.g., tumor). Approach Two cohorts of non‐small cell lung cancer (NSCLC) patients were retrospectively retrieved from a GE and a Siemens CT scanner. The lung nodules (ROI) were delineated manually and radiomic features were extracted using IBEX. The same ROI was then translocated randomly to four other tissue regions of the same set of images: adipose, heart, lung beyond nodule, and muscle for radiomic feature extraction. Coefficient of variation (CV) within different ROIs and concordance correlation coefficient (CCC) between lung nodule and a given tissue region were calculated to test to determine feature uniqueness. The radiomic features were considered nonunique when (1) the CV < 10% and CCC > 0.85 for over 50% of patients; and (2) the CCC > 0.85 appeared in ≥2 tissue regions beyond the defined region. Results In total, 14 patients from GE and 18 patients from Siemens are analyzed. The results show that 12 features fall below the 10% CV threshold for over 50% of patients in the GE cohort and 29 features in the Siemens cohort. According to CCC, 18 radiomic features in GE and 16 features in Siemens are identified as nonunique, with 11 overlapping features. Combining CV and CCC, 9 of 123 calculated features (7.3%) are identified as nonunique to a defined ROI. Conclusions Radiomic feature uniqueness should be considered to improve the reliability of radiomics study.

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“…When it comes to noninvasively diagnosing brain tumors, computer-aided detection (CAD) is favoured. When employing MRI to capture the pictures of the brain, noise and artefacts such as labelling and intensity changes are inevitable during the acquisition process [9]. ere are also numerous other structures in the brain imaging, including cerebrospinal fluid, grey and white matter, and skull tissues, apart from the tumor.…”

Section: Related Workmentioning

confidence: 99%

Design and Implementation of Brain Tumor Segmentation and Detection Using a Novel Woelfel Filter and Morphological Segmentation

et al. 2022

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Neuroimaging is critical in the diagnosis and treatment of brain cancers; however, the first detection of tumors is a challenge. Detection techniques like image segmentation are heavily reliant on the segmented image’s resolution. Magnetic resonance imaging (MRI) tumor segmentation has emerged as a new study area in the medical imaging field. This spongy and delicate mass of tissue is the brain. Stable conditions allow for patterns to enter and interact with each other. To put it simply, a tumor is a mass of tissue that has grown unchecked by the natural mechanisms that keep it under control. When cells divide uncontrollably, they create a cancerous tumor. Brain tumors can be detected and segmented using a variety of methods. A new method for detecting brain tumors using MRI images is presented in this research. An innovative Woelfel filter is used for enhancement, and morphological segmentation approaches combined with anisotropic diffusion are used for segmentation. Segmentation of brain tumors can be accomplished using thresholding and morphological techniques, which are both effective. The tumor will be located and identified using morphological image processing. Image denoising refers to the process of removing artefacts such as noise and aliasing from digital images. Here MATLAB programming language is utilised as it incorporates all the toolboxes required for the application involved in the work.

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Stability of MRI radiomic features according to various imaging parameters in fast scanned T2-FLAIR for acute ischemic stroke patients

Cited by 14 publications

References 40 publications

Application of Multimodal Magnetic Resonance Imaging in Green Channel of Acute and Hyperacute Ischemic Stroke

Application of Multimodal Magnetic Resonance Imaging in Green Channel of Acute and Hyperacute Ischemic Stroke

Uniqueness of radiomic features in non‐small cell lung cancer

Design and Implementation of Brain Tumor Segmentation and Detection Using a Novel Woelfel Filter and Morphological Segmentation

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