T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma

Cao, Tiexin; Jiang, Rifeng; Zheng, Lingmin; Zhang, Rufei; Chen, Xiaodan; Wang, Zongmeng; Jiang, Peirong; Chen, Yilin; Zhong, Tianjin; Chen, Hu; Wu, Pu-Yeh; Xue, Yi; Lin, Lin

doi:10.1007/s00330-022-09026-5

Cited by 18 publications

(15 citation statements)

References 69 publications

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“…In this study, only the histogram percentile parameters (10th and 90th percentiles) were proven to be feasible to predict meningioma consistency. While the conventional parameters (mean and median) failed, which is consistent with previous studies [ 25 , 36 ]. Our results suggest that histogram parameters can act as more promising biomarkers than conventional metrics in revealing tumour microstructure, especially intratumuoral heterogeneity.…”

Section: Discussionsupporting

confidence: 92%

Histogram analysis of mono-exponential, bi-exponential and stretched-exponential diffusion-weighted MR imaging in predicting consistency of meningiomas

Zheng,

Jiang,

Lin

et al. 2023

Cancer Imaging

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Background The consistency of meningiomas is critical to determine surgical planning and has a significant impact on surgical outcomes. Our aim was to compare mono-exponential, bi-exponential and stretched exponential MR diffusion-weighted imaging in predicting the consistency of meningiomas before surgery. Methods Forty-seven consecutive patients with pathologically confirmed meningiomas were prospectively enrolled in this study. Two senior neurosurgeons independently evaluated tumour consistency and classified them into soft and hard groups. A volume of interest was placed on the preoperative MR diffusion images to outline the whole tumour area. Histogram parameters (mean, median, 10th percentile, 90th percentile, kurtosis, skewness) were extracted from 6 different diffusion maps including ADC (DWI), D*, D, f (IVIM), alpha and DDC (SEM). Comparisons between two groups were made using Student’s t-Test or Mann-Whitney U test. Parameters with significant differences between the two groups were included for Receiver operating characteristic analysis. The DeLong test was used to compare AUCs. Results DDC, D* and ADC 10th percentile were significantly lower in hard tumours than in soft tumours (P ≤ 0.05). The alpha 90th percentile was significantly higher in hard tumours than in soft tumours (P < 0.02). For all histogram parameters, the alpha 90th percentile yielded the highest AUC of 0.88, with an accuracy of 85.10%. The D* 10th percentile had a relatively higher AUC value, followed by the DDC and ADC 10th percentile. The alpha 90th percentile had a significantly greater AUC value than the ADC 10th percentile (P ≤ 0.05). The D* 10th percentile had a significantly greater AUC value than the ADC 10th percentile and DDC 10th percentile (P ≤ 0.03). Conclusion Histogram parameters of Alpha and D* may serve as better imaging biomarkers to aid in predicting the consistency of meningioma.

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Section: Discussionsupporting

confidence: 92%

Histogram analysis of mono-exponential, bi-exponential and stretched-exponential diffusion-weighted MR imaging in predicting consistency of meningiomas

Zheng,

Jiang,

Lin

et al. 2023

Cancer Imaging

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“…Surov et al (36) performed ADC whole-lesion histogram analysis of meningiomas and found that the histogram parameters were negatively correlated with Ki-67 PI except ADC min . Cao et al (31) also found similar results—that Ki-67 PI is correlated with the following histogram parameters: C90, mean of T1, kurtosis, C10, and variance of ADC (all P < 0.05). Georg et al (37) found that skewness was significantly associated with Ki-67 PI in meningiomas and is a valuable molecular marker for differentiating low- and high-grade meningiomas and assessing the risk of tumor recurrence.…”

Section: Discussionmentioning

confidence: 57%

“…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. Another study (32) on differentiating between meningioma and schwannoma showed that schwannomas had significantly higher ADC values than meningiomas and the kurtosis of ADC had the best AUC among all histogram measures (AUC = 1.0).…”

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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“…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. In this study, variance, skewness, and kurtosis were signi cantly different between the two groups.…”

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

confidence: 96%

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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T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma

Cited by 18 publications

References 69 publications

Histogram analysis of mono-exponential, bi-exponential and stretched-exponential diffusion-weighted MR imaging in predicting consistency of meningiomas

Histogram analysis of mono-exponential, bi-exponential and stretched-exponential diffusion-weighted MR imaging in predicting consistency of meningiomas

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

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