Apparent Diffusion Coefficient Histogram Analysis for the Preoperative Evaluation of Ki-67 Expression in Pituitary Macroadenoma

Xue, Caiqiang; Liu, Suwei; Deng, Juan; Liu, Xianwang; Li, Shenglin; Zhang, Peng; Zhou, Junlin

doi:10.1007/s00062-021-01134-x

Cited by 17 publications

(4 citation statements)

References 29 publications

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“…Variance refers to the change relative to the mean gray value and is used to reflect the heterogeneity of the tumor (30); skewness reflects the symmetry of the distribution of variables from the curve shape characteristics (16,30), and the greater the absolute value of skewness, the greater the morphological deviation of the distribution; kurtosis describes the flatness of the overall numerical distribution (16), and these histogram parameters are associated with the heterogeneity of the tumor and play an important role in the differential diagnosis and prognostic evaluation of the tumor. A previous study (31) showed that ADC variance can help to distinguish meningoepithelial from fibroblastic meningiomas.…”

Section: Discussionmentioning

confidence: 99%

ADC histogram parameters differentiating atypical from transitional meningiomas: correlation with Ki-67 proliferation index

Han,

Liu,

Jing

et al. 2023

Acta Radiol

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Background Preoperative differentiation of atypical meningioma (AtM) from transitional meningioma (TrM) is critical to clinical treatment. Purpose To investigate the role of apparent diffusion coefficient (ADC) histogram analysis in differentiating AtM from TrM and its correlation with the Ki-67 proliferation index (PI). Methods Clinical, imaging, and pathological data of 78 AtM and 80 TrM were retrospectively collected. Regions of interest (ROIs) were delineated on axial ADC images using MaZda software and histogram parameters (mean, variance, skewness, kurtosis, 1st percentile [ADCp1], 10th percentile [ADCp10], 50th percentile [ADCp50], 90th percentile [ADCp90], and 99th percentile [ADCp99]) were generated. The Mann–Whitney U test was used to compare the differences in histogram parameters between the two groups; receiver operating characteristic (ROC) curves were used to assess diagnostic efficacy in differentiating AtM from TrM preoperatively. The correlation between histogram parameters and Ki-67 PI was analyzed. Results All histogram parameters of AtM were lower than those of TrM, and the variance, skewness, kurtosis, ADCp90, and ADCp99 were significantly different ( P < 0.05). Combined ADC histogram parameters (variance, skewness, kurtosis, ADCp90, and ADCp99) achieved the best diagnostic performance for distinguishing AtM from TrM. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.800%, 76.25%, 67.95%, 70.15%, 70.93%, and 73.61%, respectively. All histogram parameters were negatively correlated with Ki-67 PI (r = −0.012 to −0.293). Conclusion ADC histogram analysis is a potential tool for non-invasive differentiation of AtM from TrM preoperatively, and ADC histogram parameters were negatively correlated with the Ki-67 PI.

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Section: Discussionmentioning

confidence: 99%

ADC histogram parameters differentiating atypical from transitional meningiomas: correlation with Ki-67 proliferation index

Han,

Liu,

Jing

et al. 2023

Acta Radiol

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“…Variance refers to the change relative to the mean gray value and is used to re ect the heterogeneity of the tumor [20]; skewness re ects the symmetry of the distribution of variables from the curve shape characteristics [20,21], and the greater the absolute value of skewness, the greater the morphological deviation of the distribution; kurtosis describes the atness of the overall numerical distribution [21], and these histogram parameters are associated with the heterogeneity of the tumor and play an important role in the differential diagnosis and prognostic evaluation of the tumor. Previous studies [22] have shown that ADC variability helps to distinguish meningoepithelial from brous meningiomas.…”

Section: Pearson Correlation Between Adc Histogram Parameters and Ki-...mentioning

confidence: 99%

ADC histogram parameters differentiating atypical from transitional meningiomas: correlation with Ki-67 proliferation index

Han

Liu

Jing

et al. 2022

Preprint

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Purpose: To investigate the role of apparent diffusion coefficient (ADC) histogram analysis in differentiating atypical meningioma (AtM) from transitional meningioma (TrM) and its correlation with the Ki-67 proliferation index. Methods: Clinical, imaging, and pathological data of 78 patients with AtM and 80 patients with TrM were retrospectively collected. Regions of interest (ROIs) were delineated on axial ADC images using MaZda software and histogram parameters (mean, variance, skewness, kurtosis, 1st percentile (ADCp1), 10th percentile (ADCp10), 50th percentile (ADCp50), 90th percentile (ADCp90), and 99th percentile (ADCp99)) were generated. The Mann–Whitney U test was used to compare the differences in histogram parameters between the two groups; receiver operating characteristic (ROC) curves were used to assess diagnostic efficacy in differentiating AtM from TrM preoperatively. The correlation between histogram parameters and Ki-67 proliferation index was analyzed. Results: All histogram parameters of AtM were lower than those of TrM, and the variance, skewness, kurtosis, ADCp90, and ADCp99 were significantly different (P < 0.05). The optimal cutoff value of 147.00 × 10 – 3 mm2/s, ADCp99 achieved the best diagnostic performance for distinguishing AtM from TrM: area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.764, 58.75%, 82.05%, 70.25%, 77.05%, and 65.98%, respectively. All histogram parameters were negatively correlated with Ki-67 expression (r = -0.012 to -0.293). Conclusion: ADC histogram analysis is a potential tool for noninvasive differentiation of AtM from TrM preoperatively, and ADC histogram parameters were negatively correlated with the Ki-67 proliferation index.

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“…Histogram analysis is a post-processing technique for measuring several parameters. It compiles each voxel within a region of interest (ROI) into a histogram that can provide information regarding tumor homogeneity/heterogeneity ( Xue et al, 2022 ). Histogram analysis can better elucidate the tissue microstructure compared with traditional methods using only ROI-based analysis ( Xu et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

MRI histogram analysis of tumor-infiltrating CD8+ T cell levels in patients with glioblastoma

Xue

Qin

Zhang

et al. 2023

NeuroImage: Clinical

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Apparent Diffusion Coefficient Histogram Analysis for the Preoperative Evaluation of Ki-67 Expression in Pituitary Macroadenoma

Cited by 17 publications

References 29 publications

ADC histogram parameters differentiating atypical from transitional meningiomas: correlation with Ki-67 proliferation index

ADC histogram parameters differentiating atypical from transitional meningiomas: correlation with Ki-67 proliferation index

ADC histogram parameters differentiating atypical from transitional meningiomas: correlation with Ki-67 proliferation index

MRI histogram analysis of tumor-infiltrating CD8+ T cell levels in patients with glioblastoma

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