Histogram Analysis Parameters Apparent Diffusion Coefficient for Distinguishing High and Low-Grade Meningiomas: A Multicenter Study

Comparison of MUSE-DWI and conventional DWI in the application of invasive breast cancer and malignancy grade prediction: A comparative study

Objective

To compare MUSE-DWI with conventional DWI in assessing lesions of invasive breast cancer and evaluating the ADC values for preoperative histological grading.

Methods

A retrospective analysis was conducted on 63 lesions confirmed as invasive breast cancer by surgical or biopsy pathology. Preoperatively, all patients underwent MUSE-DWI, conventional DWI, and dynamic contrast-enhanced (DCE) scans. Two radiologists with over 5 years of experience (intermediate and senior levels, respectively) subjectively evaluated the images for clarity, image artifacts, and distortion. Objective evaluation included signal-to-noise ratio (SNR) of lesions and fibrous tissue, as well as the ADC values of both imaging techniques. Due to the limited number of cases classified as grade I and the insignificant difference in disease-specific survival and recurrence scores between grades I and II tumors, grades I and II were grouped as low-grade, while grade III was classified as high-grade. Receiver operating characteristic (ROC) curves were used to evaluate the efficacy of ADC values in preoperatively predicting the grading of invasive breast cancer.

Results

The SNR and subjective quality scores of MUSE-DWI images were significantly higher than those of conventional DWI (p < 0.05). For the same case, the ADC values of MUSE-DWI were lower than those of conventional DWI. The AUC values for predicting the grading of invasive breast cancer were 0.849 for MUSE-DWI and 0.801 for conventional DWI.

Conclusion

Compared to conventional DWI, MUSE-DWI significantly reduces artifacts and distortions, greatly improving image quality. Moreover, MUSE-DWI demonstrates higher diagnostic efficacy for preoperative histological grading of invasive breast cancer.

Noninvasive prediction of IDH mutation status in gliomas using preoperative multiparametric MRI radiomics nomogram: A mutlicenter study

PURPOSE

To establish and validate a radiomics nomogram for preoperative prediction of isocitrate dehydrogenase (IDH) mutation status of gliomas in a multicenter setting.

METHODS

414 gliomas patients were collected (306 from local institution and 108 from TCGA). 851 radiomics features were extracted from contrast-enhanced T1-weighted (CE-T1W) and fluid attenuated inversion recovery (FLAIR) sequence, respectively. The features were refined using least absolute shrinkage and selection operator (LASSO) regression combing 10-fold cross-validation. The optimal radiomics features with age and sex were processed by multivariate logistic regression analysis to construct a prediction model, which was developed in the training dataset and assessed in the test and validation dataset. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis were applied in the test and external validation datasets to evaluate the performance of the prediction model.

RESULTS

Ten robust radiomics features were selected from the 1702 features (four CE-T1W features and six FLAIR features). A nomogram was plotted to represent the prediction model. The accuracy and AUC of the radiomics nomogram achieved 86.96% and 0.891(0.809-0.947) in the test dataset and 84.26% and 0.881(0.805-0.936) in the external validation dataset (all p < 0.05). The positive predictive value (PPV) and negative predictive value (NPV) were 83.72% and 87.75% in the test dataset and 87.81% and 82.09% in the external validation dataset.

CONCLUSION

IDH genotypes of gliomas can be identified by preoperative multiparametric MRI radiomics nomogram and might be clinically meaningful for treatment strategy and prognosis stratification of gliomas.

Apparent diffusion coefficient histogram analysis for differentiating fibroblastic meningiomas from non-fibroblastic WHO grade 1 meningiomas

PURPOSE

To investigate the role of apparent diffusion coefficient (ADC) histogram analysis in differentiating fibroblastic meningiomas (FM) from non-fibroblastic WHO grade 1 meningiomas (nFM).

METHODS

This retrospective study analyzed the histopathological and diagnostic imaging data of 220 patients with histopathologically confirmed FM and nFM. The whole tumors were delineated on axial ADC images, and histogram parameters (mean, variance, skewness, kurtosis, as well as the 1st, 10th, 50th, 90th, and 99th percentile ADC [ADCp1, ADCp10, ADCp50, ADCp90, and ADCp99, respectively]) were obtained. Multivariate logistic regression analysis was used to identify the most valuable variables for discriminating FM from nFM WHO grade 1 meningiomas, and their diagnostic efficacy in differentiating FM from nFM before surgery was assessed using receiver operating characteristic (ROC) curves.

RESULTS

The mean, variance, ADCp50, ADCp90, and ADCp99 of the FM group were all lower than those of the nFM group (P < 0.05), there was significant difference in location and sex (P < 0.05). Multivariate logistic regression showed ADCp99 (P < 0.001) and location (P = 0.007) were the most valuable parameters in the discrimination of FM and nFM WHO grade 1 meningiomas. The diagnostic efficacy was achieved an AUC of 0.817(95% CI, 0.759-0.866), the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 66.4%, 83.6%, 75.0%, 80.2%, and 71.3%, respectively.

CONCLUSION

ADC histogram analysis is helpful in noninvasive differentiation of FM and nFM WHO grade 1 meningiomas, and combined ADCp99 and location have the best diagnostic efficacy.

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

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.

Intratumoral heterogeneity after targeted therapy in murine cancer models with differing degrees of malignancy

INTRODUCTION

Conventional morphologic and volumetric assessment of treatment response is not suitable for adequately assessing responses to targeted cancer therapy. The aim of this study was to evaluate changes in tumor composition after targeted therapy in murine models of breast cancer with differing degrees of malignancy via non-invasive magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Mice bearing highly malignant 4T1 tumors or low malignant 67NR tumors were treated with either a combination of two immune checkpoint inhibitors (ICI, anti-PD1 and anti-CTLA-4) or the multi-tyrosine kinase inhibitor sorafenib, following experiments with macrophage-depleting clodronate-loaded liposomes and vessel-stabilizing angiopoietin-1. Mice were imaged on a 9.4 T small animal MRI system with a multiparametric (mp) protocol, comprising T1 and T2 mapping and diffusion-weighted imaging. Tumors were analyzed ex vivo with histology.

RESULTS AND DISCUSSIONS

All treatments led to an increase in non-viable areas, but therapy-induced intratumoral changes differed between the two tumor models and the different targeted treatments. While ICI treatment led to intratumoral hemorrhage, sorafenib treatment mainly induced intratumoral necrosis. Treated 4T1 tumors showed increasing and extensive areas of necrosis, in comparison to 67NR tumors with only small, but also increasing, necrotic areas. After either of the applied treatments, intratumoral heterogeneity, was increased in both tumor models, and confirmed ex vivo by histology. Apparent diffusion coefficient with subsequent histogram analysis proved to be the most sensitive MRI sequence. In conclusion, mp MRI enables to assess dedicated therapy-related intratumoral changes and may serve as a biomarker for treatment response assessment.

Differential diagnosis of atypical and anaplastic meningiomas based on conventional MRI features and ADC histogram parameters using a logistic regression model nomogram

The purpose of the study was to determine the value of a logistic regression model nomogram based on conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) histogram parameters in differentiating atypical meningioma (AtM) from anaplastic meningioma (AnM). Clinical and imaging data of 34 AtM and 21 AnM diagnosed by histopathology were retrospectively analyzed. The whole tumor delineation along the tumor edge on ADC images and ADC histogram parameters were automatically generated and comparisons between the two groups using the independent samples t test or Mann-Whitney U test. Univariate and multivariate logistic regression analyses were used to construct the nomogram of the AtM and AnM prediction model, and the model's predictive efficacy was evaluated using calibration and decision curves. Significant differences in the mean, enhancement, perc.01%, and edema were noted between the AtM and AnM groups (P < 0.05). Age, sex, location, necrosis, shape, max-D, variance, skewness, kurtosis, perc.10%, perc.50%, perc.90%, and perc.99% exhibited no significant differences (P > 0.05). The mean and enhancement were independent risk factors for distinguishing AtM from AnM. The area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the nomogram were 0.871 (0.753-0.946), 80.0%, 81.0%, 79.4%, 70.8%, and 87.1%, respectively. The calibration curve demonstrated that the model's probability to predict AtM and AnM was in favorable agreement with the actual probability, and the decision curve revealed that the prediction model possessed satisfactory clinical availability. A logistic regression model nomogram based on conventional MRI features and ADC histogram parameters is potentially useful as an auxiliary tool for the preoperative differential diagnosis of AtM and AnM.

Volumetric apparent diffusion coefficient (ADC) histogram analysis of the brain in paediatric patients with hypoxic ischaemic encephalopathy

Purpose

To evaluate the whole brain, hippocampus, thalamus, and lentiform nucleus by volumetric apparent diffusion coefficient (ADC) histogram analysis in paediatric patients with hypoxic-ischaemic encephalopathy (HIE).

Material and methods

This retrospective study included 25 patients with HIE and 50 patients as the control group. Diffusion-weighted imaging was obtained at b-values of 1000 mm2/s. The histogram parameters of ADC values, including the mean, minimum, maximum, 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles, as well as skewness, kurtosis, and variance were determined. The interclass correlation coefficient (ICC) was used to assess the inter-observer agreement.

Results

ADCmin, ADCmean, and ADCmax, as well as the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of ADC values for the HIE group were all lower than those of the control group (p < 0.001) in the volumetric histogram analysis of the hippocampus, thalamus, and lentiform nucleus. In the whole-brain histogram analysis, ADC min, and the 50th and 75th percentiles of ADC values did not differ significantly, while other parameters were lower in the HIE group. The ROC curve revealed that the ADC histogram parameters of the hippocampus provided the most accurate results for the diagnosis of HIE. The area under the curve (AUC) of the 95th percentile of ADC values was the highest (AUC = 0.915; cut-off 1.262 × 10-3 mm2/s; sensitivity 88% and specificity 84%).

Conclusions

Volumetric ADC histogram analysis of the whole brain, hippocampus, thalamus, and lentiform nucleus with b-values of 1000 mm2/s can serve as an imaging marker for determining HIE.

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

OBJECTIVE

To investigate the value of histogram analysis of T1 mapping and diffusion-weighted imaging (DWI) in predicting the grade, subtype, and proliferative activity of meningioma.

METHODS

This prospective study comprised 69 meningioma patients who underwent preoperative MRI including T1 mapping and DWI. The histogram metrics, including mean, median, maximum, minimum, 10th percentiles (C10), 90th percentiles (C90), kurtosis, skewness, and variance, of T1 and apparent diffusion coefficient (ADC) values were extracted from the whole tumour and peritumoural oedema using FeAture Explorer. The Mann-Whitney U test was used for comparison between low- and high-grade tumours. Receiver operating characteristic (ROC) curve and logistic regression analyses were performed to identify the differential diagnostic performance. The Kruskal-Wallis test was used to further classify meningioma subtypes. Spearman's rank correlation coefficients were calculated to analyse the correlations between histogram parameters and Ki-67 expression.

RESULTS

High-grade meningiomas showed significantly higher mean, maximum, C90, and variance of T1 (p = 0.001-0.009), lower minimum, and C10 of ADC (p = 0.013-0.028), compared to low-grade meningiomas. For all histogram parameters, the highest individual distinctive power was T1 C90 with an AUC of 0.805. The best diagnostic accuracy was obtained by combining the T1 C90 and ADC C10 with an AUC of 0.864. The histogram parameters differentiated 4/6 pairs of subtype pairs. Significant correlations were identified between Ki-67 and histogram parameters of T1 (C90, mean) and ADC (C10, kurtosis, variance).

CONCLUSION

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

KEY POINTS

• The histogram parameter based on T1 mapping and DWI is useful to preoperatively evaluate the grade, subtype, and proliferative activity of meningioma. • The combination of T1 C90 and ADC C10 showed the best performance for differentiating low- and high-grade meningiomas.

Whole-volume ADC histogram of the brain as an image biomarker in evaluating disease severity of neonatal hypoxic-ischemic encephalopathy

Purpose

To examine the diagnostic significance of the apparent diffusion coefficient (ADC) histogram in quantifying neonatal hypoxic ischemic encephalopathy (HIE).

Methods

An analysis was conducted on the MRI data of 90 HIE patients, 49 in the moderate-to-severe group, and the other in the mild group. The 3D Slicer software was adopted to delineate the whole brain region as the region of interest, and 22 ADC histogram parameters were obtained. The interobserver consistency of the two radiologists was assessed by the interclass correlation coefficient (ICC). The difference in parameters (ICC > 0.80) between the two groups was compared by performing the independent sample t-test or the Mann–Whitney U test. In addition, an investigation was conducted on the correlation between parameters and the neonatal behavioral neurological assessment (NBNA) score. The ROC curve was adopted to assess the efficacy of the respective significant parameters. Furthermore, the binary logistic regression was employed to screen out the independent risk factors for determining the severity of HIE.

Results

The ADCmean, ADCmin, ADCmax,10th−70th, 90th percentile of ADC values of the moderate-to-severe group were smaller than those of the mild group, while the group's variance, skewness, kurtosis, heterogeneity, and mode-value were higher than those of the mild group (P < 0.05). All the mentioned parameters, the ADCmean, ADCmin, and 10th−70th and 90th percentile of ADC displayed positive correlations with the NBNA score, mode-value and ADCmax displayed no correlations with the NBNA score, the rest showed negative correlations with the NBNA score (P < 0.05). The area under the curve (AUC) of variance was the largest (AUC = 0.977; cut-off 972.5, sensitivity 95.1%; specificity 87.8%). According to the logistic regression analysis, skewness, kurtosis, variance, and heterogeneity were independent risk factors for determining the severity of HIE (OR > 1, P < 0.05).

Conclusions

The ADC histogram contributes to the HIE diagnosis and is capable of indicating the diffusion information of the brain objectively and quantitatively. It refers to a vital method for assessing the severity of HIE.

Apparent diffusion coefficient histogram analysis to preoperative evaluate intracranial solitary fibrous tumor: Relationship to Ki-67 proliferation index

PURPOSE

To explore the value of apparent diffusion coefficient (ADC) histogram analysis in preoperative evaluating intracranial solitary fibrous tumor (SFT) and further investigate the relationship between ADC histogram parameters and the Ki-67 proliferation index.

METHODS

From January 2014 to March 2022, 37 patients with intracranial SFT (grade 2, n = 20; grade 3, n = 17) who underwent preoperative diffusion-weighted imaging were enrolled in this study. For each tumor, nine histogram parameters were automatically extracted and selected using MaZda software based on the axial ADC maps of the whole tumor, including mean, variance, skewness, kurtosis, as well as the 1st, 10th, 50th, 90th, and 99th percentile ADC (Perc.01, Perc.10, Perc.50, Perc.90, Perc.99). Differences in ADC histogram parameters between grade 2 and 3 intracranial SFT were compared. Receiver operating characteristic (ROC) curves were drawn to determine the diagnostic performance, and Pearson's correlation coefficient was used to investigate the relationship between these parameters and the Ki-67 proliferation index.

RESULTS

The mean, Perc.01, Perc.10, Perc.50, Perc.90, and Perc.99 were significantly lower in grade 3 than in grade 2 intracranial SFT (all P < 0.05). ROC analysis showed that these parameters can effectively distinguish between the two groups, with Perc.01 generating the best differentiation performance. Significant negative correlations were also observed between these parameters and the Ki-67 proliferation index (r = -0.436 ~ -0.522, all P < 0.05). However, there was no significant difference in variance, skewness, or kurtosis between the two groups (all P > 0.05).

CONCLUSIONS

ADC histogram analysis enables effective preoperative distinction of grade 2 and grade 3 intracranial SFT.

A radiomics feature-based nomogram to predict telomerase reverse transcriptase promoter mutation status and the prognosis of lower-grade gliomas

AIM

To explore the predictive value of the radiomics feature-based nomogram for predicting telomerase reverse transcriptase (TERT) promoter mutation status and prognosis of lower-grade gliomas (LGGs) non-invasively.

MATERIALS AND METHODS

One hundred and seventy-six LGG patients (123 in the training cohort and 53 in the validation cohort) were enrolled retrospectively. A total of 851 radiomics features were extracted from contrast-enhanced magnetic resonance imaging (MRI) images. The radiomics features were selected using the least absolute shrinkage and selection operator (LASSO) method and a rad-score was calculated. Multivariate logistic regression analysis was used to build a radiomics signature based on rad-score, participant's age, and gender, and a radiomics nomogram was used to represent this signature. The performance of the signature was evaluated by receiver operating characteristic (ROC) curve analysis, and the patient prognosis was stratified based on the TERT promoter mutation status and the radiomics signature.

RESULTS

Seven robust radiomics features were selected by LASSO and the radiomics signature showed good performance for predicting the TERT promoter mutation status, with an area under the curve (AUC) of 0.900 (0.832-0.946) and 0.873 (0.753-0.948) in the training and validation datasets. With a median overall survival time of 28.5 months, the radiomics signature stratified the LGG patients into two risk groups with significantly different prognosis (log-rank = 47.531, p<0.001).

CONCLUSION

The radiomics feature-based nomogram is a promising approach for predicting the TERT promoter mutation status preoperatively and evaluating the prognosis of lower-grade glioma patients non-invasively.

Grading of meningioma tumors based on analyzing tumor volumetric histograms obtained from conventional MRI and apparent diffusion coefficient images

Background

Our purpose was to evaluate the application of volumetric histogram parameters obtained from conventional MRI and apparent diffusion coefficient (ADC) images for grading the meningioma tumors.

Results

Tumor volumetric histograms of preoperative MRI images from 45 patients with the diagnosis of meningioma at different grades were analyzed to find the histogram parameters. Kruskal-Wallis statistical test was used for comparison between the parameters obtained from different grades. Multi-parametric regression analysis was used to find the model and parameters with high predictive value for the classification of meningioma. Mode; standard deviation on post-contrast T1WI, T2-FLAIR, and ADC images; kurtosis on post-contrast T1WI and T2-FLAIR images; mean and several percentile values on ADC; and post-contrast T1WI images showed significant differences among different tumor grades (P < 0.05). The multi-parametric linear regression showed that the ADC histogram parameters model had a higher predictive value, with cutoff values of 0.212 (sensitivity = 79.6%, specificity = 84.3%) and 0.180 (sensitivity = 70.9%, specificity = 80.8%) for differentiating the grade I from II, and grade II from III, respectively.

Conclusions

The multi-parametric model of volumetric histogram parameters in some of the conventional MRI series (i.e., post-contrast T1WI and T2-FLAIR images) along with the ADC images are appropriate for predicting the meningioma tumors’ grade.

Clinical and diffusion parameters may noninvasively predict TERT promoter mutation status in grade II meningiomas

BACKGROUND AND PURPOSE

Increasing evidence suggests that genomic and molecular markers need to be integrated in grading of meningioma. Telomerase reverse transcriptase promoter (TERTp) mutation is receiving attention due to its clinical relevance in the treatment of meningiomas. The predictive ability of conventional and diffusion MRI parameters for determining the TERTp mutation status in grade II meningiomas has yet been identified.

MATERIAL AND METHODS

In this study, 63 patients with surgically confirmed grade II meningiomas (56 TERTp wildtype, 7 TERTp mutant) were included. Conventional imaging features were qualitatively assessed. The maximum diameter, volume of the tumors and histogram parameters from the apparent diffusion coefficient (ADC) were assessed. Independent clinical and imaging risk factors for TERTp mutation were investigated using multivariable logistic regression. The discriminative value of the prediction models with and without imaging features was evaluated.

RESULTS

In the univariable regression, older age (odds ratio [OR] = 1.13, P = 0.005), larger maximum diameter (OR = 1.09, P = 0.023), larger volume (OR = 1.04, P = 0.014), lower mean ADC (OR = 0.02, P = 0.025), and lower ADC 10th percentile (OR = 0.01, P = 0.014) were predictors of TERTp mutation. In multivariable regression, age (OR = 1.13, P = 0.009) and ADC 10th percentile (OR = 0.01, P = 0.038) were independent predictors of variables for predicting the TERTp mutation status. The performance of the prediction model increased upon inclusion of imaging parameters (area under the curves of 0.86 and 0.91, respectively, without and with imaging parameters).

CONCLUSION

Older age and lower ADC 10th percentile may be useful parameters to predict TERTp mutation in grade II meningiomas.

Using arterial spin labeling blood flow and its histogram analysis to distinguish early-stage nasopharyngeal carcinoma from lymphoid hyperplasia

ABSTRACT

To investigate the feasibility of arterial spin labeling (ASL) blood flow (BF) and its histogram analysis to distinguish early-stage nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoid hyperplasia (NPLH).Sixty-three stage T1 NPC patients and benign NPLH patients underwent ASL on a 3.0-T magnetic resonance imaging system. BF histogram parameters were derived automatically, including the mean, median, maximum, minimum, kurtosis, skewness, and variance. Absolute values were obtained for skewness and kurtosis (absolute value of skewness [AVS] and absolute value of kurtosis [AVK], respectively). The Mann-Whitney U test, receiver operating characteristic curve, and multiple logistic regression models were used for statistical analysis.The mean, maximum, and variance of ASL BF values were significantly higher in early-stage NPC than in NPLH (all P < 0.0001), while the median and AVK values of early-stage NPC were also significantly higher than those of NPLH (all P < 0.001). No significant difference was found between the minimum and AVS values in early-stage NPC compared with NPLH (P = 0.125 and P = 0.084, respectively). The area under the curve (AUC) of the maximum was significantly higher than those of the mean and median (P < 0.05). The AUC of variance was significantly higher than those of the other parameters (all P < 0.05). Multivariate analysis showed that variance was the only independent predictor of outcome (P < 0.05).ASL BF and its histogram analysis could distinguish early-stage NPC from NPLH, and the variance value was a unique independent predictor.

Improving the characterization of meningioma microstructure in proton therapy from conventional apparent diffusion coefficient measurements using Monte Carlo simulations of diffusion MRI

PURPOSE

Proton therapy could benefit from noninvasively gaining tumor microstructure information, at both planning and monitoring stages. The anatomical location of brain tumors, such as meningiomas, often hinders the recovery of such information from histopathology, and conventional noninvasive imaging biomarkers, like the apparent diffusion coefficient (ADC) from diffusion-weighted MRI (DW-MRI), are nonspecific. The aim of this study was to retrieve discriminative microstructural markers from conventional ADC for meningiomas treated with proton therapy. These markers were employed for tumor grading and tumor response assessment.

METHODS

DW-MRIs from patients affected by meningioma and enrolled in proton therapy were collected before (n = 35) and 3 months after (n = 25) treatment. For the latter group, the risk of an adverse outcome was inferred by their clinical history. Using Monte Carlo methods, DW-MRI signals were simulated from packings of synthetic cells built with well-defined geometrical and diffusion properties. Patients' ADC was modeled as a weighted sum of selected simulated signals. The weights that best described a patient's ADC were determined through an optimization procedure and used to estimate a set of markers of tumor microstructure: diffusion coefficient (D), volume fraction (vf), and radius (R). Apparent cellularity (ρ_app ) was estimated from vf and R for an easier clinical interpretability. Differences between meningothelial and atypical subtypes, and low- and high-grade meningiomas were assessed with nonparametric statistical tests, whereas sensitivity and specificity with ROC analyses. Similar analyses were performed for patients showing low or high risk of an adverse outcome to preliminary evaluate response to treatment.

RESULTS

Significant (P < 0.05) differences in median ADC, D, vf, R, and ρ_app values were found when comparing meningiomas' subtypes and grades. ROC analyses showed that estimated microstructural parameters reached higher specificity than ADC for subtyping (0.93 for D and vf vs 0.80 for ADC) and grading (0.75 for R vs 0.67 for ADC). High- and low-risk patients showed significant differences in ADC and microstructural parameters. The skewness of ρ_app was the parameter with highest AUC (0.90) and sensitivity (0.75).

CONCLUSIONS

Matching measured with simulated ADC yielded a set of potential imaging markers for meningiomas grading and response monitoring in proton therapy, showing higher specificity than conventional ADC. These markers can provide discriminative information about spatial patterns of tumor microstructure implying important advantages for patient-specific proton therapy workflows.

A Comparative Study of 2 Different Segmentation Methods of ADC Histogram for Differentiation Genetic Subtypes in Lower-Grade Diffuse Gliomas

Background

To evaluate the diagnostic performance of apparent diffusion coefficient (ADC) histogram parameters for differentiating the genetic subtypes in lower-grade diffuse gliomas and explore which segmentation method (ROI-1, the entire tumor ROI; ROI2, the tumor ROI excluding cystic and necrotic portions) performs better.

Materials and Methods

We retrospectively evaluated 56 lower-grade diffuse gliomas and divided them into three categories: IDH-wild group (IDH^wt, 16cases); IDH mutant with the intact 1p or 19q group (IDH^mut/1p19q⁺, 18cases); and IDH mutant with the 1p/19q codeleted group (IDH^mut/1p19q^-, 22cases). Histogram parameters of ADC maps calculated with the two different ROI methods: ADCmean, min, max, mode, P5, P10, P25, P75, P90, P95, kurtosis, skewness, entropy, StDev, and inhomogenity were compared between these categories using the independent t test or Mann-Whitney U test. For statistically significant results, a receiver operating characteristic (ROC) curves were constructed, and the optimal cutoff value was determined by maximizing Youden's index. Area under the curve (AUC) results were compared using the method of Delong et al.

Results

The inhomogenity from the two different ROI methods for distinguishing IDH^wt gliomas from IDH^mut gliomas both showed the biggest AUC (0.788, 0.930), the optimal cutoff value was 0.229 (sensitivity, 81.3%; specificity, 75.0%) for the ROI-1 and 0.186 (sensitivity, 93.8%; specificity, 82.5%) for the ROI-2, and the AUC of the inhomogenity from the ROI-2 was significantly larger than that from another segmentation, but no significant differences were identified between the AUCs of other same parameters from the two different ROI methods. For the differentiaiton of IDH^mut/1p19q^- tumors and IDH^mut/1p19q⁺ tumors, with the ROI-1, the ADCmode showed the biggest AUC (AUC: 0.784; sensitivity, 61.1%; specificity, 90.9%), with the ROI-2, and the skewness performed best (AUC, 0.821; sensitivity, 81.8%; specificity, 77.8%), but no significant differences were identified between the AUCs of the same parameters from the two different ROI methods.

Conclusion

ADC values analyzed by the histogram method could help to classify the genetic subtypes in lower-grade diffuse gliomas, no matter which ROI method was used. Extracting cystic and necrotic portions from the entire tumor lesions is preferable for evaluating the difference of the intratumoral heterogeneity and classifying IDH-wild tumors, but not significantly beneficial to predicting the 1p19q genotype in the lower-grade gliomas.

Histological Grade of Meningioma: Prediction by Intravoxel Incoherent Motion Histogram Parameters

RATIONALE AND OBJECTIVES

To evaluate the usefulness of intravoxel incoherent motion (IVIM) histogram analysis for differentiating low-grade meningiomas (LGMs) and high-grade meningiomas (HGMs).

MATERIALS AND METHODS

Fifty-nine patients with pathologically confirmed meningiomas (45 LGMs and 14 HGMs) underwent IVIM MR imaging. Maps of IVIM parameters (perfusion fraction, f; true diffusion coefficient, D; and pseudo diffusion coefficient, D*), as well as of the apparent diffusion coefficient (ADC), were generated. Histogram analysis was performed using parametric values from all voxels in regions-of-interest manually drawn to encompass the whole tumor. The histogram results of ADC and IVIM parameters were compared using the Mann-Whitney U test. Area under the receiver operating characteristic curve (AUC) values were generated to evaluate how well each parameter could differentiate LGMs from HGMs. Spearman's rank correlation coefficients were used to evaluate correlations between histogram parameters and Ki-67 expression.

RESULTS

Compared to LGM, HGM showed significantly higher standard deviation (SD), variance, and coefficient of variation (CV) of ADC (p< 0.006-0.028; AUC, 0.693-0.748), D (p< 0.004-0.032; AUC, 0.670-0.752), and significantly higher CV of f (p< 0.005-0.024; AUC = 0.737). Means and percentiles of ADC and IVIM parameters did not differ significantly between LGM and HGM. Significant positive correlations were identified between Ki-67 and histogram parameters of ADC (SD, variance, kurtosis, skewness, and CV) and D (SD, variance, kurtosis, and CV), whereas no significant correlation with Ki-67 was shown for mean or percentiles of ADC and IVIM parameters.

CONCLUSION

Heterogeneity histogram parameters of ADC, D, and f may be useful for differentiating LGMs from HGMs.

Differentiation between nasopharyngeal carcinoma and lymphoma at the primary site using whole-tumor histogram analysis of apparent diffusion coefficient maps

INTRODUCTION

To determine the value of whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating nasopharyngeal carcinoma (NPC) from lymphoma (NPL) at the primary site METHOD AND MATERIALS: One hundred forty-seven patients with nasopharyngeal tumors (89 NPCs and 38 NPLs) who had undergone magnetic resonance imaging (MRI) and diffusion-weighted imaging were retrospectively analyzed. ADC histogram-derived parameters were compared between the NPC and NPL groups by using the Mann-Whitney U test. Receiver operating characteristic (ROC) curves of the histogram parameters were plotted for diagnostic accuracy. Sensitivity and specificity were calculated for each histogram parameter.

RESULTS

In whole-tumor histogram analysis, the mean, median, and 10th and 25th percentiles of ADC were all significantly higher in NPC than NPL (P = 0.045, P = 0.035, P = 0.005, and P = 0.016, respectively). Uniformity was significantly higher in NPC than NPL (P = 0.001). Skewness was significantly lower in NPC than NPL (P = 0.039). For the conventional ROI-based method, ADC_mean values were significantly higher in NPC than in NPL (P = 0.009). The ROC curve analysis showed that uniformity yielded the largest area under the curve (AUC = 0.768) for differentiating NPC from NPL among all ADC metrics, followed by 10th percentiles of ADC (AUC = 0.725); sensitivity and specificity were 76.5% and 71.4%, respectively.

CONCLUSION

Whole-tumor histogram analysis of ADC maps could be helpful for differentiating NPC from NPL.

Apparent diffusion coefficient values and non-homogeneity of diffusion in brain tumors in diffusion-weighted MRI

Background

The values that have been received from apparent diffusion coefficient (ADC) maps of diffusion-weighted magnetic resonance imaging (DW-MRI) might play a vital role in evaluating tumors and their grading scale.

Purpose

To investigate the predictive role of this heterogeneity in brain tumor pathologies and its correlation with Ki-67.

Material and Methods

A total of 124 patients with brain tumors underwent brain MRI with gadolinium injection. ADC and standard deviation of each lesion have been obtained from manual localization of the region of interest on the ADC map. A receiver operating characteristic analysis was conducted to determine the minimum cut-off values of the mean ADC and mean standard deviation of ADC maps having the highest sensitivity and specificity to differentiate high-grade and low-grade tumors.

Results

Mean ADC values in the region of interest were significantly lower for malignant tumors (grade IV and metastasis) than grade I brain tumors, while a higher mean standard deviation was observed. In a more detailed comparison of tumor groups, the mean standard deviation of the ADC for glioblastoma multiform was significantly higher than meningioma grade I ( P < 0.001) and metastasis was significantly higher than grade III and IV astrocytic tumors ( P = 0.004). The analysis of Ki-67 proliferation index and mean ADC values in gliomas showed a significant inverse correlation between the parameters (r = –0.0429, P < 0.001) and direct correlation between Ki-67 and mean standard deviation of the ADC (r = 0.551, P < 0.001). As an index for the ADC to differentiate high-grade and low-grade tumors, the cut-off values of 1.40*10−3 mm2/s for mean ADC and 45*10−3 mm2/s for mean standard deviation have the highest combination of sensitivity, specificity, and area under the curve.

Conclusion

The mean value and standard deviation of the ADC could be considered for differentiating between low-grade and high-grade brain tumors, as two available non-invasive methods.

Grading meningiomas utilizing multiparametric MRI with inclusion of susceptibility weighted imaging and quantitative susceptibility mapping

BACKGROUND AND PURPOSE

The ability to predict high-grade meningioma preoperatively is important for clinical surgical planning. The purpose of this study is to evaluate the performance of comprehensive multiparametric MRI, including susceptibility weighted imaging (SWI) and quantitative susceptibility mapping (QSM) in predicting high-grade meningioma both qualitatively and quantitatively.

METHODS

Ninety-two low-grade and 37 higher grade meningiomas in 129 patients were included in this study. Morphological characteristics, quantitative histogram analysis of QSM and ADC images, and tumor size were evaluated to predict high-grade meningioma using univariate and multivariate analyses. Receiver operating characteristic (ROC) analyses were performed on the morphological characteristics. Associations between Ki-67 proliferative index (PI) and quantitative parameters were calculated using Pearson correlation analyses.

RESULTS

For predicting high-grade meningiomas, the best predictive model in multivariate logistic regression analyses included calcification (β=0.874, P=0.110), peritumoral edema (β=0.554, P=0.042), tumor border (β=0.862, P=0.024), tumor location (β=0.545, P=0.039) for morphological characteristics, and tumor size (β=4×10^-5, P=0.004), QSM kurtosis (β=-5×10^-3, P=0.058), QSM entropy (β=-0.067, P=0.054), maximum ADC (β=-1.6×10^-3, P=0.003), ADC kurtosis (β=-0.013, P=0.014) for quantitative characteristics. ROC analyses on morphological characteristics resulted in an area under the curve (AUC) of 0.71 (0.61-0.81) for a combination of them. There were significant correlations between Ki-67 PI and mean ADC (r=-0.277, P=0.031), 25^th percentile of ADC (r=-0.275, P=0.032), and 50^th percentile of ADC (r=-0.268, P=0.037).

CONCLUSIONS

Although SWI and QSM did not improve differentiation between low and high-grade meningiomas, combining morphological characteristics and quantitative metrics can help predict high-grade meningioma.

Arterial Spin Labeling for Glioma Grade Discrimination: Correlations with IDH1 Genotype and 1p/19q Status

Since accurate grading of gliomas has important clinical value, the aim of this study is to evaluate the diagnostic efficacy of perfusion values derived from arterial spin labeling (ASL) to grade gliomas. In addition, the correlation between perfusion and isocitrate dehydrogenase 1 (IDH1) genotypes and chromosome arms 1p and 19q (1p/19q) status of gliomas was assessed. A total of 52 cases of supratentorial gliomas in adults who received ASL imaging were enrolled in this retrospective study. The cerebral blood flow (CBF) images derived from ASL and anatomical maps were normalized to the Montreal Neurological Institute coordinate system and matched. The mean CBF (meanCBF), the maximum CBF (maxCBF), and their relative values (rmeanCBF and rmaxCBF, respectively) were assessed in each case. The tumor grades, IDH1 genotypes, and 1p/19q status were diagnosed according to the 2016 WHO criteria. Receiver operating characteristic curves were performed to assess the efficacy of perfusion parameters for grading. Qualitatively, all gliomas were divided into high- and low-perfusion groups. The crosstabs chi-square test of independence was performed to calculate contingency coefficient (C) and Cramer V coefficient to assess the correlation between perfusion and IDH1 genotypes and 1p/19q status of gliomas. The rmaxCBF showed the best diagnostic efficacy; meanwhile, rmeanCBF had the best specificity for grade discrimination. In astrocytoma, there was a mild correlation between IDH1 genotypes and tumor perfusion with the Cramer's V coefficient of 0.378. There was no significant association between 1p/19q codeletion and perfusion in grade II and III gliomas.