Use of Apparent Diffusion Coefficient Histogram in Differentiating Between Medulloblastoma and Pilocytic Astrocytoma in Children

Wang, Weijian; Cheng, Jingliang; Zhang, Yong; Wang, Chaoyan

doi:10.12659/msm.909136

Cited by 16 publications

(22 citation statements)

References 23 publications

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“…The larger the absolute value of skewness, the greater the skewing of distribution form. The kurtosis of histograms was the statistic describing the distribution steep degree of all values in the whole, which reflected the relative sharpness or flatness of a distribution compared with normal distribution [12]. A positive peak indicated a sharper distribution than normal distribution and a negative peak indicated a flatter distribution than normal distribution.…”

Section: Discussionmentioning

confidence: 99%

Whole-tumor histogram analysis of apparent diffusion coefficient maps in grading diagnosis of ependymoma

et al. 2020

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Objective To study the value of whole-tumor histogram analysis which is based on apparent diffusion coefficient maps in grading diagnosis of ependymoma. Methods 71 patients with ependymal tumors were retrospectively analyzed, including 13 cases of WHO grade I, 28 cases of WHO grade II, and 30 cases of WHO grade III. Mazda software was used to draw the region of interest (ROI) in the apparent diffusion coefficient maps of three groups on every layer of tumor level. The whole-tumor gray histogram analysis was carried to obtained nine characteristic parameters, including mean, variance, kurtosis, skewness, Perc.01%, Perc.10%, Perc.50%, Perc.90%, and Perc.99%. When the parameters satisfy the test of normal distribution and homogeneity of variance, single factor analysis of variance (ANOVA) was carried to compare the three groups and LSD t test was performed to compare the two groups. Besides, the ROC curve was used to analyze the diagnostic efficacy of the parameters. Results Variance, Perc.01%, and Perc.10% had significant differences among the three groups (all P < 0.05). The remaining six parameters had no significant difference among the three groups (all P > 0.05). And, between WHO I and WHO II, the sensitivity and specificity of the Perc.10% were 85.7% and 100.0%, the AUC was 0.872, and the cutoff was 126.5. Between WHO I and WHO III, the sensitivity and specificity of the Perc.10% were 85.7% and 87.7%, the AUC was 0.835, and the optimum critical value was 131.33. Besides, the sensitivity, specificity, and AUC of variance between WHO II and WHO III are 68.4%, 76.9%, 0.794, and 2645.7, respectively. They had higher identification efficiency. Conclusion Whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps could provide ancillary diagnostic value in grading diagnosis of ependymoma. Perc.10% had a high diagnostic efficiency.

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Section: Discussionmentioning

confidence: 99%

Whole-tumor histogram analysis of apparent diffusion coefficient maps in grading diagnosis of ependymoma

et al. 2020

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“…Its four quantitative descriptive characteristics are morphological, statistical, regional, and model-based [23]. Several recent studies have used KUR and SKW as diagnostic indicators for various purposes, including to analyse liver fibrosis due to hepatitis C virus infection [24], central nervous system injuries with tumour differentiation [25], substantia nigra lesions due to carbon monoxide exposure [26], pancreatic tumour type differentiation [27, 28], and the association between metabolic patterns and several types of lesions in patients with cervical carcinoma [29]. Son et al [30] collected tomographic measurements of lung tissue that included the KUR and SKW values associated with ground-glass nodules with or without solid components.…”

Section: Discussionmentioning

confidence: 99%

Solid Indeterminate Nodules with a Radiological Stability Suggesting Benignity: A Texture Analysis of Computed Tomography Images Based on the Kurtosis and Skewness of the Nodule Volume Density Histogram

et al. 2019

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Background The number of incidental findings of pulmonary nodules using imaging methods to diagnose other thoracic or extrathoracic conditions has increased, suggesting the need for in-depth radiological image analyses to identify nodule type and avoid unnecessary invasive procedures. Objectives The present study evaluated solid indeterminate nodules with a radiological stability suggesting benignity (SINRSBs) through a texture analysis of computed tomography (CT) images. Methods A total of 100 chest CT scans were evaluated, including 50 cases of SINRSBs and 50 cases of malignant nodules. SINRSB CT scans were performed using the same noncontrast enhanced CT protocol and equipment; the malignant nodule data were acquired from several databases. The kurtosis (KUR) and skewness (SKW) values of these tests were determined for the whole volume of each nodule, and the histograms were classified into two basic patterns: peaks or plateaus. Results The mean (MEN) KUR values of the SINRSBs and malignant nodules were 3.37 ± 3.88 and 5.88 ± 5.11, respectively. The receiver operating characteristic (ROC) curve showed that the sensitivity and specificity for distinguishing SINRSBs from malignant nodules were 65% and 66% for KUR values >6, respectively, with an area under the curve (AUC) of 0.709 (p < 0.0001). The MEN SKW values of the SINRSBs and malignant nodules were 1.73 ± 0.94 and 2.07 ± 1.01, respectively. The ROC curve showed that the sensitivity and specificity for distinguishing malignant nodules from SINRSBs were 65% and 66% for SKW values >3.1, respectively, with an AUC of 0.709 (p < 0.0001). An analysis of the peak and plateau histograms revealed sensitivity, specificity, and accuracy values of 84%, 74%, and 79%, respectively. Conclusions KUR, SKW, and histogram shape can help to noninvasively diagnose SINRSBs but should not be used alone or without considering clinical data.

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“…On the other hand, ADC histogram metrics (entropy and 90th percentile) were proven to correlate with the histological proliferation marker Ki67, suggesting a potential role of ADC as an independent prognostic factor in medulloblastomas [ 96 ]. In addition, various ROI-derived and histogram-derived ADC-metrics were proven useful for the differential diagnosis between medulloblastoma and other infra-tentorial neoplasms such as pilocytic astrocytomas, metastases, hemangioblastomas, and ependymomas [ 97 , 98 , 99 ]. Moreover, DTI histogram metrics showed different values in medulloblastomas and pilocytic astrocytomas [ 100 ], with mean diffusivity (MD) outperforming other DTI-metrics, consistently with the notion of MD providing insights analogous to ADC regarding tumor microstructure.…”

Section: Medulloblastomasmentioning

confidence: 99%

Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors

Sanvito

Castellano

Falini

2021

Cancers

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In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.

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Use of Apparent Diffusion Coefficient Histogram in Differentiating Between Medulloblastoma and Pilocytic Astrocytoma in Children

Cited by 16 publications

References 23 publications

Whole-tumor histogram analysis of apparent diffusion coefficient maps in grading diagnosis of ependymoma

Whole-tumor histogram analysis of apparent diffusion coefficient maps in grading diagnosis of ependymoma

Solid Indeterminate Nodules with a Radiological Stability Suggesting Benignity: A Texture Analysis of Computed Tomography Images Based on the Kurtosis and Skewness of the Nodule Volume Density Histogram

Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors

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