Use of Diffusion Weighted Imaging in Differentiating Between Maligant and Benign Meningiomas. A Multicenter Analysis

Progression/Recurrence of Meningioma: An Imaging Review Based on Magnetic Resonance Imaging

Meningiomas are the most common primary central nervous system tumors. The preferred treatment is maximum safe resection, and the heterogeneity of meningiomas results in a variable prognosis. Progression/recurrence (P/R) can occur at any grade of meningioma and is a common adverse outcome after surgical treatment and a major cause of postoperative rehospitalization, secondary surgery, and mortality. Early prediction of P/R plays an important role in postoperative management, further adjuvant therapy, and follow-up of patients. Therefore, it is essential to thoroughly analyze the heterogeneity of meningiomas and predict postoperative P/R with the aid of noninvasive preoperative imaging. In recent years, the development of advanced magnetic resonance imaging technology and machine learning has provided new insights into noninvasive preoperative prediction of meningioma P/R, which helps to achieve accurate prediction of meningioma P/R. This narrative review summarizes the current research on conventional magnetic resonance imaging, functional magnetic resonance imaging, and machine learning in predicting meningioma P/R. We further explore the significance of tumor microenvironment in meningioma P/R, linking imaging features with tumor microenvironment to comprehensively reveal tumor heterogeneity and provide new ideas for future research.

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.

Correlation of the apparent diffusion coefficient with the standardized uptake value in meningioma of the skull plane using [68]Ga-DOTATOC PET/MRI

PURPOSE

To evaluate a correlation between an MRI-specific marker for cellular density [apparent diffusion coefficient (ADC)] and the expression of Somatostatin Receptors (SSTR) in patients with meningioma of the skull plane and orbital space.

METHODS

68 Ga-DOTATOC PET/MR imaging was performed in 60 Patients with suspected or diagnosed meningiomas of the skull base and eye socket. Analysis of ADC values succeeded in 32 patients. ADC values (ADC mean and ADC min ) were analyzed using a polygonal region of interest. Tracer-uptake of target lesions was assessed according to corresponding maximal (SUV max ) and mean (SUV mean ) values. Correlations between assessed parameters were evaluated using the Pearson correlation coefficient.

RESULTS

One out of 32 patients (3%) was diagnosed with lymphoma by histopathological examination and therefore excluded from further analysis. Median ADC mean amounted to 822 × 10 -5  mm²/s -1 (95% CI: 570-1497) and median ADC min was 493 × 10 -5 mm 2 /s -1 (95% CI: 162-783). There were no significant correlations between SUV max and ADC min (r = 0.60; P  = 0.76) or ADC mean (r = -0.52; P  = 0.79), respectively. However, Pearson's test showed a weak, inverse but insignificant correlation between ADC mean and SUV mean (r = -0.33; P  = 0.07).

CONCLUSION

The presented data displays no relevant correlations between increased SSTR expression and cellularity in patients with meningioma of the skull base. SSTR-PET and DWI thus may offer complementary information on tumor characteristics of meningioma.

Differentiating primary central nervous system lymphoma from glioblastoma by time-dependent diffusion using oscillating gradient

BACKGROUND

This study aimed to elucidate the impact of effective diffusion time setting on apparent diffusion coefficient (ADC)-based differentiation between primary central nervous system lymphomas (PCNSLs) and glioblastomas (GBMs) and to investigate the usage of time-dependent diffusion magnetic resonance imaging (MRI) parameters.

METHODS

A retrospective study was conducted involving 21 patients with PCNSLs and 66 patients with GBMs using diffusion weighted imaging (DWI) sequences with oscillating gradient spin-echo (Δeff = 7.1 ms) and conventional pulsed gradient (Δeff = 44.5 ms). In addition to ADC maps at the two diffusion times (ADC7.1 ms and ADC44.5 ms), we generated maps of the ADC changes (cADC) and the relative ADC changes (rcADC) between the two diffusion times. Regions of interest were placed on enhancing regions and non-enhancing peritumoral regions. The mean and the fifth and 95th percentile values of each parameter were compared between PCNSLs and GBMs. The area under the receiver operating characteristic curve (AUC) values were used to compare the discriminating performances among the indices.

RESULTS

In enhancing regions, the mean and fifth and 95th percentile values of ADC44.5 ms and ADC7.1 ms in PCNSLs were significantly lower than those in GBMs (p = 0.02 for 95th percentile of ADC44.5 ms, p = 0.04 for ADC7.1 ms, and p < 0.01 for others). Furthermore, the mean and fifth and 95th percentile values of cADC and rcADC were significantly higher in PCNSLs than in GBMs (each p < 0.01). The AUC of the best-performing index for ADC7.1 ms was significantly lower than that for ADC44.5 ms (p < 0.001). The mean rcADC showed the highest discriminating performance (AUC = 0.920) among all indices. In peritumoral regions, no significant difference in any of the three indices of ADC44.5 ms, ADC7.1 ms, cADC, and rcADC was observed between PCNSLs and GBMs.

CONCLUSIONS

Effective diffusion time setting can have a crucial impact on the performance of ADC in differentiating between PCNSLs and GBMs. The time-dependent diffusion MRI parameters may be useful in the differentiation of these lesions.

Differentiating brain metastasis from glioblastoma by time-dependent diffusion MRI

BACKGROUND

This study was designed to investigate the use of time-dependent diffusion magnetic resonance imaging (MRI) parameters in distinguishing between glioblastomas and brain metastases.

METHODS

A retrospective study was conducted involving 65 patients with glioblastomas and 27 patients with metastases using a diffusion-weighted imaging sequence with oscillating gradient spin-echo (OGSE, 50 Hz) and a conventional pulsed gradient spin-echo (PGSE, 0 Hz) sequence. In addition to apparent diffusion coefficient (ADC) maps from two sequences (ADC50Hz and ADC0Hz), we generated maps of the ADC change (cADC): ADC50Hz - ADC0Hz and the relative ADC change (rcADC): (ADC50Hz - ADC0Hz)/ ADC0Hz × 100 (%).

RESULTS

The mean and the fifth and 95th percentile values of each parameter in enhancing and peritumoral regions were compared between glioblastomas and metastases. The area under the receiver operating characteristic curve (AUC) values of the best discriminating indices were compared. In enhancing regions, none of the indices of ADC0Hz and ADC50Hz showed significant differences between metastases and glioblastomas. The mean cADC and rcADC values of metastases were significantly higher than those of glioblastomas (0.24 ± 0.12 × 10-3mm2/s vs. 0.14 ± 0.03 × 10-3mm2/s and 23.3 ± 9.4% vs. 14.0 ± 4.7%; all p < 0.01). In peritumoral regions, no significant difference in all ADC indices was observed between metastases and glioblastomas. The AUC values for the mean cADC (0.877) and rcADC (0.819) values in enhancing regions were significantly higher than those for ADC0Hz5th (0.595; all p < 0.001).

CONCLUSIONS

The time-dependent diffusion MRI parameters may be useful for differentiating brain metastases from glioblastomas.

Review of meningioma diagnosis and management

Meningiomas are the most common intracranial tumors in adult patients. Although the majority of meningiomas are diagnosed as benign, approximately 20% of cases are high-grade tumors that require significant clinical treatment. The gold standard for grading central nervous system tumors comes from the World Health Organization Classification of Tumors of the central nervous system. Treatment options also depend on the location, imaging, and histopathological features of the tumor. This review will cover diagnostic strategies for meningiomas, including 2021 updates to the World Health Organization's grading of meningiomas. Meningioma treatment plans are variable and highly dependent on tumor grading. This review will also update the reader on developments in the treatment of meningiomas, including surgery, radiation therapy and monoclonal antibody treatment.

Imaging Characteristics of Meningiomas

Contemporary neuroimaging of meningiomas has largely relied on computed tomography, and more recently magnetic resonance imaging. While these modalities are frequently used in nearly all clinical settings where meningiomas are treated for the routine diagnosis and follow-up of these tumors, advances in neuroimaging have provided novel opportunities for prognostication and treatment planning (including both surgical planning and radiotherapy planning). These include perfusion MRIs, and positron emission tomography (PET) imaging modalities. Here we will summarize the contemporary uses for neuroimaging in meningiomas, and future applications of novel, cutting edge imaging techniques that may be routinely implemented in the future to enable more precise treatment of these challenging tumors.

Predicting the grade of meningiomas by clinical-radiological features: A comparison of precontrast and postcontrast MRI

OBJECTIVES

Postcontrast magnetic resonance imaging (MRI) is important for the differentiation between low-grade (WHO I) and high-grade (WHO II/III) meningiomas. However, nephrogenic systemic fibrosis and cerebral gadolinium deposition are major concerns for postcontrast MRI. This study aimed to develop and validate an accessible risk-scoring model for this differential diagnosis using the clinical characteristics and radiological features of precontrast MRI.

METHODS

From January 2019 to October 2021, a total of 231 meningioma patients (development cohort n = 137, low grade/high grade, 85/52; external validation cohort n = 94, low-grade/high-grade, 60/34) were retrospectively included. Fourteen types of demographic and radiological characteristics were evaluated by logistic regression analyses in the development cohort. The selected characteristics were applied to develop two distinguishing models using nomograms, based on full MRI and precontrast MRI. Their distinguishing performances were validated and compared using the external validation cohort.

RESULTS

One demographic characteristic (male), three precontrast MRI features (intratumoral cystic changes, lobulated and irregular shape, and peritumoral edema), and one postcontrast MRI feature (absence of a dural tail sign) were independent predictive factors for high-grade meningiomas. The area under the receiver operating characteristic (ROC) curve (AUC) values of the two distinguishing models (precontrast-postcontrast nomogram vs. precontrast nomogram) in the development cohort were 0.919 and 0.898 and in the validation cohort were 0.922 and 0.878. DeLong's test showed no statistical difference between the AUC values of the two distinguishing models (p = 0.101).

CONCLUSIONS

An accessible risk-scoring model based on the demographic characteristics and radiological features of precontrast MRI is sufficient to distinguish between low-grade and high-grade meningiomas, with a performance equal to that of a full MRI, based on radiological features.

Local ablation of hepatocellular carcinoma by interstitial brachytherapy: prediction of outcome by diffusion-weighted imaging

BACKGROUND

Interstitial brachytherapy (iBT) has become a viable treatment option in the therapy of early and intermediate stage hepatocellular carcinoma (HCC). Prognostic imaging tools to predict patient outcome are missing.

PURPOSE

To assess the predictive value of baseline diffusion-weighted imaging in HCC before iBT with regard to local tumor control and overall survival (OS).

MATERIAL AND METHODS

We retrospectively identified 107 patients who underwent iBT for HCC from 2011 to 2018 from our database. Apparent diffusion coefficient (ADC) values for each treated lesion were analyzed in region of interest measurements. Additionally, explorative combined ratios adjusting total measured lesion area and mean measured lesion area per patient by ADC values were calculated. Measurements underwent a univariate and multivariate Cox regression analysis. The log rank test was then used to verify prognostic cutoff levels for median survival time.

RESULTS

A total of 189 lesions in 81 patients were measured. Median survival of patients was 46.0 months. Neither ADC parameter was indicative of local tumor control. Lesion size >5 cm was associated with lower local tumor control (hazard ratio [HR]=4.292, 95% confidence interval [CI]=1.285-14.331; P = 0.018). Average measured lesion area divided by ADC_min (ADC_{area mean, min}) was identified to independently predict OS (HR=1.994, 95% CI=1.172-3.392; P = 0.011). A cutoff based on the variable's median (0.29 × 10-4 AU) identified patients with poor outcome (OS 36 vs. 61 months) for lower ADC_{area mean, min} values as verified by the log-rank test (P = 0.040).

CONCLUSION

Pre-treatment ADC_{area mean, min} may serve as an independent predictor of OS in patients with HCC undergoing iBT.

Radiographic Differentiation of Secretory Meningiomas and WHO Grade 2 Meningiomas: When Atypical Features Are Not Always Predictive of Atypical Tumors

OBJECTIVE

Secretory meningioma (SM) is a rare subtype of World Health Organization (WHO) grade 1 meningioma, associated with significant peritumoral brain edema (PTBE). Because of this, SM may be mistaken preoperatively to be a WHO grade 2 meningioma (G2M). In this study, we identified radiographic features to differentiate these 2 tumor types preoperatively to help inform surgical decision-making.

METHODS

We performed a retrospective review of all patients with histologically confirmed intracranial SM and G2M at a single institution from 2000 to 2019. Relevant clinic, demographic and radiographic data were collected. We performed a stepwise multivariable logistic regression to identify independent predictors of SM.

RESULTS

A total of 43 SM and 140 G2M patients were included in this study. In multivariable analysis, severe PTBE, meaning edema size greater than tumor size (odds ratio [OR] 4.44, P = 0.01), tumor hyperintensity on fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging sequences (OR 7.80, P = 0.002), and higher normalized apparent diffusion coefficient (nADC) values (OR 1.54, P < 0.001) were strong predictors of SM. Conversely, larger tumor volume (OR 1.79 per 10 mL volume increase, P < 0.001) and cystic component (OR 12.50, P = 0.007) correlated with G2M.

CONCLUSIONS

In this study, we found that preoperative FLAIR hyperintensity, severe PTBE, and higher nADC values correlated with SM pathology, and larger size and cystic component were associated with G2M. Accurate identification of SM on preoperative imaging may provide surgeons useful information in decision-making.

Preoperative Apparent Diffusion Coefficient Values for Differentiation between Low and High Grade Meningiomas: An Updated Systematic Review and Meta-Analysis

The meta-analysis aimed to compare the preoperative apparent diffusion coefficient (ADC) values between low-grade meningiomas (LGMs) and high-grade meningiomas (HGMs). Medline, Cochrane, Scopus, and Embase databases were screened up to January 2022 for studies investigating the ADC values of meningiomas. The study endpoint was the reported ADC values for LGMs and HGMs. Further subgroup analyses between 1.5T and 3T MRI scanners, ADC threshold values, ADC in different histological LGMs, and correlation coefficients (r) between ADC and Ki-67 were also performed. The quality of studies was evaluated by the quality assessment of diagnostic accuracy studies (QUADAS-2). A χ2-based test of homogeneity was performed using Cochran’s Q statistic and inconsistency index (I2). Twenty-five studies with a total of 1552 meningiomas (1102 LGMs and 450 HGMs) were included. The mean ADC values (×10−3 mm2/s) were 0.92 and 0.79 for LGMs and HGMs, respectively. Compared with LGMs, significantly lower mean ADC values for HGMs were observed with a pooled difference of 0.13 (p < 0.00001). The results were consistent in both 1.5T and 3T MRI scanners. For ADC threshold values, pooled sensitivity of 69%, specificity of 82%, and AUC of 0.84 are obtained for differentiation between LGMs and HGMs. The mean ADC (×10−3 mm2/s) in different histological LGMs ranged from 0.87 to 1.22. Correlation coefficients (r) of mean ADC and Ki-67 ranged from −0.29 to −0.61. Preoperative ADC values are a useful tool for differentiating between LGMs and HGMs. Results of this study provide valuable information for planning treatments in meningiomas.

Use of advanced neuroimaging and artificial intelligence in meningiomas

Anatomical cross‐sectional imaging methods such as contrast‐enhanced MRI and CT are the standard for the delineation, treatment planning, and follow‐up of patients with meningioma. Besides, advanced neuroimaging is increasingly used to non‐invasively provide detailed insights into the molecular and metabolic features of meningiomas. These techniques are usually based on MRI, e.g., perfusion‐weighted imaging, diffusion‐weighted imaging, MR spectroscopy, and positron emission tomography. Furthermore, artificial intelligence methods such as radiomics offer the potential to extract quantitative imaging features from routinely acquired anatomical MRI and CT scans and advanced imaging techniques. This allows the linking of imaging phenotypes to meningioma characteristics, e.g., the molecular‐genetic profile. Here, we review several diagnostic applications and future directions of these advanced neuroimaging techniques, including radiomics in preclinical models and patients with meningioma.

Nomogram based on MRI can preoperatively predict brain invasion in meningioma

Predicting brain invasion preoperatively should help to guide surgical decision-making and aid the prediction of meningioma grading and prognosis. However, only a few imaging features have been identified to aid prediction. This study aimed to develop and validate an MRI-based nomogram to predict brain invasion by meningioma. In this retrospective study, 658 patients were examined via routine MRI before undergoing surgery and were diagnosed with meningioma by histopathology. Least absolute shrinkage and selection operator (LASSO) regularization was used to determine the optimal combination of clinical characteristics and MRI features for predicting brain invasion by meningiomas. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to determine the discriminatory ability. Furthermore, a nomogram was constructed using the optimal MRI features, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Eighty-one patients with brain invasion and 577 patients without invasion were enrolled. According to LASSO regularization, tumour shape, tumour boundary, peritumoral oedema, and maximum diameter were independent predictors of brain invasion. The model showed good discriminatory ability for predicting brain invasion in meningiomas, with an AUC of 0.905 (95% CI, 0.871-0.940) vs 0.898 (95% CI, 0.849-0.947) and sensitivity of 93.0% vs 92.6% in the training vs validation cohorts. Our predictive model based on MRI features showed good performance and high sensitivity for predicting the risk of brain invasion in meningiomas and can be applied in the clinical setting.

Pretreatment Apparent Diffusion Coefficient Cannot Predict Histopathological Features and Response to Neoadjuvant Radiochemotherapy in Rectal Cancer: A Meta-Analysis

AIM

Our purpose was to perform a systemic literature review and meta-analysis regarding use of apparent diffusion coefficient (ADC) for prediction of histopathological features in rectal cancer (RC) and to prove if ADC can predict treatment response to neoadjuvant radiochemotherapy (NARC) in RC.

METHODS

MEDLINE library, EMBASE, Cochrane, and SCOPUS database were screened for associations between ADC and histopathology and/or treatment response in RC up to June 2020. Authors, year of publication, study design, number of patients, mean value, and standard deviation of ADC were acquired. The methodological quality of the collected studies was checked according to the Quality Assessment of Diagnostic Studies instrument. The meta-analysis was undertaken by using the RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used to account the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated.

RESULTS

Overall, 37 items (2,015 patients) were included. ADC values of tumors with different T and N stages and grades overlapped strongly. ADC cannot distinguish RC with a high- and low-carcinoembryonic antigen level. Regarding KRAS status, ADC cannot discriminate mutated and wild-type RC. ADC did not correlate significantly with expression of vascular endothelial growth factor and hypoxia-inducible factor 1a. ADC correlates with Ki 67, with the calculated correlation coefficient: -0.52. The ADC values in responders and nonresponders overlapped significantly.

CONCLUSION

ADC correlates moderately with expression of Ki 67 in RC. ADC cannot discriminate tumor stages, grades, and KRAS status in RC. ADC cannot predict therapy response to NARC in RC.

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.

Differentiating Between Low- and High-grade Glioma Tumors Measuring Apparent Diffusion Coefficient Values in Various Regions of the Brain

Objectives

Our study aimed to apply the apparent diffusion coefficient (ADC) values to quantify the differences between low- and high-grade glioma tumors.

Methods

We conducted a multicenter, retrospective study between September to December 2019. Magnetic resonance imaging (MRI) diffusion-weighted images (DWIs), and the pathologic findings of 56 patients with glioma tumors (low grade = 28 and high grade = 28) were assessed to measure the ADC values in the tumor center, tumor edema, boundary area between tumor with normal tissue, and inside the healthy hemisphere. These values were compared between the two groups, and cut-off values were calculated using the receiver operating characteristic curve.

Results

We saw significant differences between the mean ADC values measured in the tumor center and edema between high- and low-grade tumors (p< 0.005). The ADC values in the boundary area between tumors with normal tissue and inside healthy hemisphere did not significantly differ in the groups. The ADC values at tumor center and edema were higher than 1.12 × 10^-3 mm²/s (sensitivity = 100% and specificity = 96.0%) and 1.15 × 10^-3 mm²/s (sensitivity = 75.0% and specificity = 64.0%), respectively, could be classified as low-grade tumors.

Conclusions

The ADC values from the MRI DWIs in the tumor center and edema could be used as an appropriate method for investigating the differences between low- and high-grade glioma tumors. The ADC values in the boundary area and healthy tissues had no diagnostic values in grading the glioma tumors.

Intracranial Abnormalities with Diffusion Restriction

Multiple pathologic conditions can cause changes in the random movement of water, which can be detected with diffusion-weighted imaging (DWI). DWI plays a powerful clinical role in detecting restricted diffusion associated with acute brain infarction. Other disorders can also result in restricted diffusion. This article focuses on showing examples of common and uncommon disorders that have restricted diffusion secondary to cytotoxic and/or intramyelinic edema. These disorders include ischemia, infection, noninfectious demyelinating diseases, genetic mutations affecting metabolism, acquired metabolic disorders, toxic or drug exposures, neoplasms and tumorlike lesions, radiation treatment, trauma, and denervation.

Association of apparent diffusion coefficient with Ki-67 proliferation index, progesterone-receptor status and various histopathological parameters, and its utility in predicting the high grade in meningiomas

BACKGROUND

Accurate preoperative determination of the histological grade and cellular proliferative potential of meningioma by non-invasive imaging is of paramount importance.

PURPOSE

To evaluate the utility of apparent diffusion coefficient (ADC) in determining the histological grade of meningioma, and to investigate the correlation of ADC with Ki-67 proliferation index (PI), progesterone receptor (PR) status, and a number of other histopathological parameters.

MATERIAL AND METHODS

Histopathologically confirmed 94 meningioma patients (72 low-grade, 22 high-grade) who had undergone preoperative diffusion-weighted imaging were retrospectively evaluated. ADC values were obtained by manually drawing the regions of interest (ROIs) within the solid components of the tumor. The relationship between ADC and Ki-67 values, PR status, and multiple histopathological parameters were investigated, and the ADC values of high-grade and low-grade meningiomas were compared. Independent sample t-test, Mann-Whitney U test, receiver operating characteristic, Pearson correlation, and multiple logistic regression analysis were used for statistical assessment.

RESULTS

All ADC and rADC values were significantly lower in high-grade meningiomas than in low-grade meningiomas (all P < 0.05). ADC values showed significantly negative correlations with Ki-67 and mitotic index (P < 0.001 for each). Numerous ADC parameters were significantly lower in meningiomas demonstrating hypercellularity and necrosis features (P < 0.05). ADC values did not show a significant correlation with PR score (all P > 0.05).

CONCLUSION

ADC can be utilized as a reliable imaging biomarker for predicting the proliferative potential and histological grade in meningiomas.

Differentiating between non-functioning pituitary macroadenomas and sellar meningiomas using ADC

INTRODUCTION AND AIM

It is difficult to distinguish between non-functioning pituitary macroadenomas (NFPMAs) and sellar meningiomas because of their overlapping imaging manifestations on routine MRI, especially in cases of meningiomas growing into the saddle. Here, we aimed to differentiate between these two tumors using apparent diffusion coefficient (ADC) values and MRI characteristics.

METHODS

A total of 60 NFPMA and 52 sellar meningioma cases confirmed by the pathological analysis were retrospectively reviewed. All patients were examined via routine MRI and diffusion-weighted imaging (DWI) before undergoing surgery. The clinical characteristics, MRI characteristics, and max ADC (ADCmax), average ADC (ADCmean), and minimum ADC (ADCmin) values were compared between the two tumors via Chi-square test and two sample t-tests. Receiver operating characteristic (ROC) curve and binary logistic regression analyses were conducted to determine the discrimination ability.

RESULTS

The ADCmax, ADCmean, and ADCmin values were significantly higher in NFPMAs compared to sellar meningiomas (P < 0.001 for all). Among ADC values, ADCmax demonstrated good performance with an AUC of 0.896 (95% CI, 0.823-0.969) and accuracy of 88.7%. A cut-off value of 0.97 × 10-3 mm2/s was used for ADCmax for differentiation between tumors. A combination of ADCmax values and clinicoradiological features showed the best discrimination ability for differential diagnosis between the two tumors, with an AUC of 0.981 (95% CI, 0.958-1.000) and accuracy of 96.9%.

CONCLUSION

A combination of ADCmax and clinicoradiological features demonstrates good discrimination ability and high accuracy for differentiation between NFPMAs and sellar meningiomas, and is a potential quantitative tool to aid in the selection of surgical techniques.

MRI predictors for brain invasion in meningiomas

BACKGROUND AND PURPOSE

In the 2016 revision of the World Health Organization classification of central nervous system tumours, brain invasion was added as an independent histological criterion for the diagnosis of a World Health Organization grade II atypical meningioma. The aim of this study was to assess whether magnetic resonance imaging characteristics can predict brain invasion for meningiomas.

MATERIALS AND METHODS

We conducted a retrospective review of all meningiomas resected at our institution between 2005 and 2016 which had preoperative magnetic resonance imaging and included brain tissue within the pathology specimen. One hundred meningiomas were included in the study, 60 of which had histopathological brain invasion, 40 of which did not. Magnetic resonance imaging characteristics of tumours were evaluated for potential predictors of brain invasion. Tumour location, size, perilesional oedema, contour, cerebrospinal fluid cleft, peritumoral cyst, dural venous sinus invasion, bone invasion, hyperostosis and the presence of enlarged pial arteries and veins were evaluated. Data were analysed using conventional chi-square, Fisher's exact test and logistic regression.

RESULTS

The volume of peritumoral oedema was significantly higher in the brain-invasive meningioma group compared to the non-brain-invasive group. The presence of a complete cleft was a rare finding that was only found in non-brain-invasive meningiomas. The presence of enlarged pial feeding arteries was a rare finding that was only found in brain-invasive meningiomas.

CONCLUSIONS

An increased volume of perilesional oedema is associated with the likelihood of brain invasion for meningiomas.

ADC values of benign and high grade meningiomas and associations with tumor cellularity and proliferation - A systematic review and meta-analysis

INTRODUCTION

The aim of the present systematic review and meta-analysis was to compare the reported ADC values in different meningiomas and to analyze associations between ADC and cell count and proliferation activity in this tumor entity.

METHOD

MEDLINE library and SCOPUS database were screened for papers investigating ADC values of meningiomas up November 2019. The first primary endpoint of the systematic review was the reported ADC mean value of the meningioma groups. The second primary endpoint was the correlation coefficient between ADC values and proliferation index Ki 67 and cellularity.

RESULTS

For the discrimination analysis between benign and high grade meningioma 17 studies were suitable. There were 766 grade I tumors and 289 high grade meningiomas. The calculated mean ADC value of the benign grade I tumors was 0.93 × 10^-3mm²/s [95%-Confidence interval 0.84;1.03] and the mean value of the high-grade tumors was 0.77 × 10^-3mm²/s [95%-Confidence interval 0.73-0.80]. The pooled correlation coefficient between ADC and the proliferation index Ki 67 was r = -0.36 [95% CI -0.43; -0.28]. The pooled correlation coefficient between ADC and cellularity was r = -0.43 [95% CI -0.61; - 0.26].

CONCLUSION

No validated ADC threshold can be recommended for distinguishing benign from high grade meningiomas. Only a moderate inverse correlation was identified between ADC values and tumor microstructure in meningiomas and, therefore, ADC might not accurately enough to predict proliferation potential and cellularity in this entity.

Histogram Analysis of ADC Maps for Differentiating Brain Metastases From Different Histological Types of Lung Cancers

PURPOSE

Our study aimed to investigate the role of histogram analysis derived from apparent diffusion coefficient (ADC) maps in brain metastases (BMs) from lung cancer for differentiating histological subtype.

METHODS

A total of 61 BMs (45 non-small cell lung cancer [NSCLC] comprising 32 adenocarcinoma [AC], 13 squamous cell carcinoma [SCC], and 16 small-cell lung cancer [SCLC]) in 50 patients with histopathologically confirmed lung cancer were retrospectively included in this study. Pretreatment cranial diffusion-weighted imaging was performed, and the corresponding ADC maps were generated. Regions of interest were drawn on solid components of the BM on all slices of the ADC maps to obtain parameters, including ADC_max, ADC_mean, ADC_min, ADC_median, ADC_range, skewness, kurtosis, entropy, ADC₁₀, ADC₂₅, ADC₇₅, and ADC₉₀. Apparent diffusion coefficient histogram parameters were compared among histological type groups. Kruskal-Wallis, Mann-Whitney U, chi-square tests, and receiver-operating characteristic (ROC) curve were used for statistical assessment.

RESULTS

ADC_min, ADC₁₀, and ADC₂₅ were found to be significantly different among AC, SCC, and SCLC groups; these parameters were higher for AC group, moderate for SCC group, and significantly lower for SCLC group. Skewness and kurtosis were not significantly different among all groups. The ROC analysis for differentiating BMs of NSCLC from SCLC showed that ADC₂₅ achieved the highest area under the curve at 0.922 with 93.02% sensitivity and 81.25% specificity.

CONCLUSION

Apparent diffusion coefficient histogram analysis of BMs from lung cancer has significant prognostic value in differentiating histological subtypes of lung cancer.

Multi-parametric qualitative and quantitative MRI assessment as predictor of histological grading in previously treated meningiomas

PURPOSE

Meningiomas are mainly benign tumors, though a considerable proportion shows aggressive behaviors histologically consistent with atypia/anaplasia. Histopathological grading is usually assessed through invasive procedures, which is not always feasible due to the inaccessibility of the lesion or to treatment contraindications. Therefore, we propose a multi-parametric MRI assessment as a predictor of meningioma histopathological grading.

METHODS

Seventy-three patients with 74 histologically proven and previously treated meningiomas were retrospectively enrolled (42 WHO I, 24 WHO II, 8 WHO III) and studied with MRI including T2 TSE, FLAIR, Gradient Echo, DWI, and pre- and post-contrast T1 sequences. Lesion masks were segmented on post-contrast T1 sequences and rigidly registered to ADC maps to extract quantitative parameters from conventional DWI and intravoxel incoherent motion model assessing tumor perfusion. Two expert neuroradiologists assessed morphological features of meningiomas with semi-quantitative scores.

RESULTS

Univariate analysis showed different distributions (p < 0.05) of quantitative diffusion parameters (Wilcoxon rank-sum test) and morphological features (Pearson's chi-square; Fisher's exact test) among meningiomas grouped in low-grade (WHO I) and higher grade forms (WHO II/III); the only exception consisted of the tumor-brain interface. A multivariate logistic regression, combining all parameters showing statistical significance in the univariate analysis, allowed discrimination between the groups of meningiomas with high sensitivity (0.968) and specificity (0.925). Heterogeneous contrast enhancement and low ADC were the best independent predictors of atypia and anaplasia.

CONCLUSION

Our multi-parametric MRI assessment showed high sensitivity and specificity in predicting histological grading of meningiomas. Such an assessment may be clinically useful in characterizing lesions without histological diagnosis. Key points • When surgery and biopsy are not feasible, parameters obtained from both conventional and diffusion-weighted MRI can predict atypia and anaplasia in meningiomas with high sensitivity and specificity. • Low ADC values and heterogeneous contrast enhancement are the best predictors of higher grade meningioma.

Predictive Value of Early Post-Treatment Diffusion-Weighted MRI for Recurrence or Tumor Progression of Head and Neck Squamous Cell Carcinoma Treated with Chemo-Radiotherapy

Aims: To investigate the predictive capacity of early post-treatment diffusion-weighted magnetic resonance imaging (MRI) for recurrence or tumor progression in patients with no tumor residue after chemo-radiotherapy (CRT) for head and neck squamous cell carcinoma, and, to assess the predictive capacity of pre-treatment diffusion-weighted MRI for persistent tumor residue post-CRT. Materials and Method: A single center cohort study was performed in one French hospital. All patients with squamous cell carcinoma receiving CRT (no surgical indication) were included. Two diffusion-weighted MRI were performed: one within 8 days before CRT and one 3 months after completing CRT with determination of median tumor apparent diffusion coefficient (ADC). Main outcome: The primary endpoint was progression-free survival. Results: 59 patients were included prior to CRT and 46 (78.0%) completed CRT. A post-CRT tumor residue was found in 19/46 (41.3%) patients. In univariate analysis, initial ADC was significantly lower in patients with residue post CRT (0.56 ± 0.11 versus 0.79 ± 0.13; p < 0.001). When initial ADC was dichotomized at the median, initial ADC lower than 0.7 was significantly more frequent in patients with residue post CRT (73.7% versus 11.1%, p < 0.0001). In multivariate analysis, only initial ADC lower than 0.7 was significantly associated with tumor residue (OR = 22.6; IC [4.9–103.6], p < 0.0001). Among 26 patients without tumor residue after CRT and followed up until 12 months, 6 (23.1%) presented recurrence or progression. Only univariate analysis was performed due to a small number of events. The only factor significantly associated with disease progression or early recurrence was the delta ADC (p = 0.0009). When ADC variation was dichotomized at the median, patients with ADC variation greater than 0.7 had time of disease-free survival significantly longer than patients with ADC variation lower than 0.7 (377.5 [286–402] days versus 253 [198–370], p < 0.0001). Conclusion and relevance: Diffusion-weighted MRI could be a technique that enables differentiation of patients with high potential for early recurrence for whom intensive post-CRT monitoring is mandatory. Prospective studies with more inclusions would be necessary to validate our results.

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.

Differentiating microcystic meningioma from atypical meningioma using diffusion-weighted imaging

PURPOSE

Microcystic meningioma (MCM) appears similar to atypical meningioma(AM) as per conventional diagnostic imaging. However, considering their different recurrence rate and prognosis, accurate differential diagnosis is essential for determine the appropriate treatment strategy. The aim of the study was to differentiate MCM from AM by diffusion-weighted imaging (DWI), in order to provide the basis for accurate preoperative diagnosis.

METHODS

The preoperative clinical data, conventional MRI and DWI data of 15 MCM and 30 AM cases were retrospectively analyzed. The average apparent diffusion coefficient (ADC_mean), minimum ADC (ADC_min) and normalized ADC (nADC) between MCM and AM were compared using two sample t-tests. The value of ADC_mean, ADC_min and nADC in the differential diagnosis of MCM and AM were calculated by the receiver operating curve (ROC) analysis.

RESULTS

The ADC_mean (1.06 ± 0.10 vs 0.80 ± 0.11 × 10^-3 mm²/s; P < 0.001), ADC_min (0.99 ± 0.10 vs 0.74 ± 0.12 × 10^-3 mm²/s; P < 0.001) and nADC (1.45 ± 0.17 vs 1.07 ± 0.17; P < .0001) were significantly higher in MCM compared to AM. ADC_mean of 0.91 × 10^-3 mm²/s showed an optimum area under the ROC curve of 0.967 ± 0.022, and distinguished between MCM and AM with 86.67% sensitivity, 100% specificity and 88.89% accuracy. In addition, its positive and negative predictive values were 96.29% and 77.78% respectively.

CONCLUSIONS

DWI can differentially diagnose MCM and AM, and ADC_mean is a potential quantitative tool that can improve preoperative diagnosis of both tumors.

Atypical meningiomas

Meningiomas are the most common primary CNS tumor in adults, representing a third of brain lesions. Their clinical presentation varies greatly, ranging from asymptomatic incidental tumor to fatal tumor. The majority of meningiomas are benign, and gross total resection can achieve very low recurrence rates, with radiation therapy or radiosurgery reserved for recurrences or residual tumor that grows during serial imaging. At the other end of the spectrum, malignant meningiomas, although comprising just 1% of meningiomas, have been recognized to exhibit aggressive behavior that ultimately proves lethal regardless of the extent of resection or whether adjuvant radiation and chemotherapy are utilized. Over the past 2 decades, there has been pathologic recognition of a third type of meningioma known as "atypical," with borderline histologic and clinical features between benign and malignant meningioma. Here we review the clinical features, treatment, and outcomes of atypical meningiomas, with a focus on the impact of extent of resection and radiation therapy on the long-term recurrence rate of completely and incompletely resected atypical meningiomas.

Modern day imaging of meningiomas

Meningiomas are the most common primary tumors of the central nervous system and as such they are often encountered at neuroimaging. Fortunately, meningiomas are readily diagnosed with anatomic computed tomography and magnetic resonance imaging. While conventional imaging is the mainstay for initial diagnosis and delineating tumor for treatment planning and posttreatment follow-up, the last couple of decades have given rise to advanced physiologic and metabolic imaging techniques that serve as powerful tools in the management of meningioma. These modern approaches are allowing imaging to expand its utility to include extraction of biologic and potentially prognostic information that will ultimately improve care for meningioma patients.

Diagnostic performance of diffusion and perfusion MRI in differentiating high from low-grade meningiomas: A systematic review and meta-analysis

OBJECTIVES

The purpose of the present meta-analysis and systematic review was to evaluate the currently published data on the potential role of perfusion (PWI) and diffusion (DWI) weighted imaging for the assessment of meningioma grade.

PATIENTS AND METHODS

A search of MEDLINE and relative reference lists was conducted to identify all the eligible studies assessing the diagnostic performance of DWI and PWI in grading meningiomas. Meta-Disc and Rev-Man were used for the statistical analysis. Methodological quality and risk of bias were assessed with the use of the updated Quality assessment of the diagnostic accuracy (QUADAS-2) tool. Pooled sensitivity, specificity and area under the summary receiver operating characteristic curve were calculated individually for DWI and PWI to demonstrate the diagnostic performance of each modality.

RESULTS

Fourteen studies with 1063 patients were included. The 8 studies evaluating DWI showed a pooled sensitivity of 80% (95% CI, 74%-86%) and a pooled specificity of 76% (95% CI, 72%-79%). As for the 6 remaining studies concerning PWI, the pooled sensitivity and specificity were found 80% (95% CI, 71%-88%) and 91% (95% CI, 87%-94%), respectively. The area under the SROC curve was 0.94 (95% CI) for PWI and 0.91 (95% CI) for DWI. The comparison of the two AUCs showed that neither technique was superior with regards to the diagnostic performance.

CONCLUSIONS

The current evidence proves that both techniques are efficient at differentiating high from low-grade meningiomas.

Differentiation Between Benign and Nonbenign Meningiomas by Using Texture Analysis From Multiparametric MRI

BACKGROUND

It is difficult to prospectively differentiate between benign (World Health Organization [WHO] I) and nonbenign (WHO II and III) meningiomas.

PURPOSE

To evaluate the feasibility of preoperative differentiation between benign and nonbenign meningiomas by using texture analysis from multiparametric MR data.

STUDY TYPE

Retrospective.

SUBJECTS

In all, 184 patients with meningioma (139 benign and 45 nonbenign) were included as the training cohort and 79 patients with meningioma (60 benign and 19 nonbenign) were included as the external validation cohort.

FIELD STRENGTH/SEQUENCE

T₁ -weighted, T₂ -weighted, and contrast-enhanced T₁ -weighted imaging were performed on 1.5 or 3.0T MR systems from two centers.

ASSESSMENT

Tumor segmentation and radiological characteristic (RC) evaluation were performed by experienced radiologists. The texture features were extracted from preprocessed images and combined with RCs, and then the combined features were reduced by using a two-step feature selection. Three single-sequence models and a multiparametric MRI (the combination of single sequences) model were constructed and then evaluated with the external validation cohort.

STATISTICAL TESTS

Area under receiver operating characteristic curve (AUC), accuracy (Acc), f1-score (F1), sensitivity (Sen), and specificity (Spec), were calculated to quantify the performance of the models.

RESULTS

Among the four texture models, the multiparametric MRI model demonstrated the best performance for differentiating between benign and nonbenign meningiomas in both the training and external validation cohorts (AUC 0.91, Acc 89%, F1 0.88, Sen 0.93, and Spec 0.87 in the training cohort; AUC 0.83, Acc 80%, F1 0.77, Sen 0.84, and Spec 0.78 in the validation cohort).

DATA CONCLUSION

Nonbenign meningiomas might be preoperatively differentiated from benign meningiomas by using texture analysis from multiparametric MR data.

LEVEL OF EVIDENCE

3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1810-1820.

Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study

BACKGROUND

Grading of meningiomas is important in the choice of the most effective treatment for each patient.

PURPOSE

To determine the diagnostic accuracy of a deep convolutional neural network (DCNN) in the differentiation of the histopathological grading of meningiomas from MR images.

STUDY TYPE

Retrospective.

POPULATION

In all, 117 meningioma-affected patients, 79 World Health Organization [WHO] Grade I, 32 WHO Grade II, and 6 WHO Grade III.

FIELD STRENGTH/SEQUENCE

1.5 T, 3.0 T postcontrast enhanced T1 W (PCT1 W), apparent diffusion coefficient (ADC) maps (b values of 0, 500, and 1000 s/mm2 ).

ASSESSMENT

WHO Grade II and WHO Grade III meningiomas were considered a single category. The diagnostic accuracy of the pretrained Inception-V3 and AlexNet DCNNs was tested on ADC maps and PCT1 W images separately. Receiver operating characteristic curves (ROC) and area under the curve (AUC) were used to asses DCNN performance.

STATISTICAL TEST

Leave-one-out cross-validation.

RESULTS

The application of the Inception-V3 DCNN on ADC maps provided the best diagnostic accuracy results, with an AUC of 0.94 (95% confidence interval [CI], 0.88-0.98). Remarkably, only 1/38 WHO Grade II-III and 7/79 WHO Grade I lesions were misclassified by this model. The application of AlexNet on ADC maps had a low discriminating accuracy, with an AUC of 0.68 (95% CI, 0.59-0.76) and a high misclassification rate on both WHO Grade I and WHO Grade II-III cases. The discriminating accuracy of both DCNNs on postcontrast T1 W images was low, with Inception-V3 displaying an AUC of 0.68 (95% CI, 0.59-0.76) and AlexNet displaying an AUC of 0.55 (95% CI, 0.45-0.64).

DATA CONCLUSION

DCNNs can accurately discriminate between benign and atypical/anaplastic meningiomas from ADC maps but not from PCT1 W images.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1152-1159.

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.

Diagnostic accuracy of diffusion weighted MRI in differentiating benign and malignant meningiomas

Objective

To determine the diagnostic accuracy of DWI in differentiating benign and malignant meningiomas keeping histopathology as gold standard.

Methods

This was a descriptive analytical study conducted at Radiology Department, DUHS/Dr. Ruth K. M. Pfau Civil Hospital Karachi, from August 2016 to March 2018.It included152 patients clinically suspected of meningioma on conventional neuroimaging. Imaging features of DWI were compared with histopathology findings. The diagnostic accuracy of DWI was calculated in terms of sensitivity, specificity, accuracy, PPV and NPV using histopathology as gold standard.

Results

There were 59 male and 93 female patients with mean age of 55.38±9.8 years. Mean duration of sign and symptoms was 5.67±2.57 months. Out of 152 patients, 117(77%) and 35(23%) were differentiated into benign and malignant meningiomas respectively by DWI while 135(88.82%) and17(11.18%) patients were diagnosed respectively on histopathology. The sensitivity, specificity, PPV, NPV and accuracy of DWI of 84.4%, 82.3%, 97.4%, 40%, and 84.2% respectively keeping histopathology as gold standard.

Conclusion

DWI features along with calculation of ADC values is a reliable non-invasive technique for differentiating benign and malignant meningiomas. However the low negative predictive value necessitates the use of histopathology.

Can amide proton transfer-weighted imaging differentiate tumor grade and predict Ki-67 proliferation status of meningioma?

OBJECTIVES

To determine the utility of the amide proton transfer-weighted MR imaging in differentiating the WHO grade and predict proliferative activity of meningioma.

METHODS

Fifty-three patients with WHO grade I meningiomas and 26 patients with WHO grade II meningiomas underwent conventional and APT-weighted sequences on a 3.0 Tesla MR before clinical intervention. The APT-weighted (APTw) parameters in the solid tumor region were obtained and compared between two grades using the t test; the receiver operating characteristic (ROC) curve was used to assess the best parameter for predicting the grade of meningiomas. Pearson's correlation coefficient was calculated between the APTw_max and Ki-67 labeling index in meningiomas.

RESULTS

The APTw_max and APTw_mean values were not significantly different between WHO grade I and grade II meningiomas (p = 0.103 and p = 0.318). The APTw_min value was higher and the APTw_max-min value was lower in WHO grade II meningiomas than in WHO grade I tumors (p = 0.027 and p = 0.019). But the APTw_min was higher and the APTw_max-min was lower in microcystic meningiomas than in WHO grade II meningiomas (p = 0.001 and p = 0.006). The APTw_min combined with APTw_max-min showed the best diagnostic performance in predicting the grade of meningiomas with an AUC of 0.772. The APTw_max value was positively correlated with Ki-67 labeling index (r = 0.817, p < 0.001) in meningiomas; the regression equation for the Ki-67 labeling index (%) (Y) and APTw_max (%) (X) was Y = 4.9 × X - 12.4 (R² = 0.667, p < 0.001).

CONCLUSION

As a noninvasive imaging method, the ability of APTw-MR imaging in differentiating the grade of meningiomas is limited, but the technology can be used to predict the proliferative activity of meningioma.

KEY POINTS

• The APTw _min value was higher and the APTw _max-min value was lower in WHO grade II meningioma than in grade I tumors. • The APTw _min value was higher and the APTw _max-min value was lower in microcystic meningiomas than in WHO grade II meningiomas. • The APTw _max value was positively correlated with meningioma proliferation index.

Prediction of Recurrence in Parasagittal and Parafalcine Meningiomas: Added Value of Diffusion-Weighted Magnetic Resonance Imaging

BACKGROUND

Parasagittal and parafalcine (PSPF) meningiomas recur more frequently than other intracranial meningiomas owing to the difficulty in achieving gross total resection. The present study investigated the preoperative magnetic resonance imaging (MRI) features for the prediction of progression/recurrence (P/R) in benign PSPF meningiomas with an emphasis on the apparent diffusion coefficient (ADC) values.

METHODS

We retrospectively investigated the preoperative MRI features for the prediction of P/R in benign (World Health Organization grade I) PSPF meningiomas. Only patients who had undergone preoperative and postoperative MRI follow-up studies for ≥1 year were included. From October 2006 to December 2015, 48 patients with a diagnosis of benign PSPF meningioma were included (median follow-up period, 42.5 months). Of these 48 patients, 12 (25%) developed P/R (median time to P/R, 23 months).

RESULTS

PSPF meningiomas in male patients, subtotal resection, large tumor diameter, high diffusion-weighted imaging signal, and lower ADC values or ratios were significantly associated with P/R (P < 0.05). The cutoff points of the ADC value and ADC ratio for the prediction of P/R were 0.83 × 10^-3 mm²/second and 0.99, with an area under the curve of 0.82 and 0.83, respectively (P = 0.001). On multivariate Cox proportional hazards analysis, male sex and low ADC values (<0.83 × 10^-3 mm²/second) were high-risk factors for P/R, with a hazard ratio of 12.37 and 30.2, respectively (P < 0.05). Kaplan-Meier analysis showed that lower ADC values and ratios predicted for significantly shorter progression-free survival (P < 0.05).

CONCLUSIONS

The preoperative ADC values and ratios for the prediction of P/R offer additional valuable information for the treatment planning for PSPF meningiomas.

Apparent diffusion coefficient as an effective index for the therapeutic efficiency of brain chemoradiotherapy for brain metastases from lung cancer

BACKGROUND

The potential of apparent diffusion coefficient (ADC) value alteration before and after chemoradiotherapy as a potential monitor for therapeutic efficiency of treatment for brain metastases from lung cancer were discussed.

METHOD

Thirty lung cancer patients with brain metastases, conventional magnetic resonance imaging (MRI) examination and diffusion weighted imaging (DWI) were performed one week before chemoradiotherapy and after one treatment cycle and two treatment cycles. 43 tumor lesions were divided into effective group and invalid group according to the changes of the tumor size. The differences in ADC values at different time points before and after treatment in each treatment group were analyzed.

RESULT

The maximum diameter of the tumor was no difference after one treatment cycle, but decreased after two treatment cycles. ADC values significantly increased after both one and two treatment cycles. In effective group, the ADC values were significantly increased after one and two treatment cycles. While, there are no difference in invalid group after one treatment cycle but decreased after two treatment cycles. ΔADC values in effective group after one and two treatment cycles were both significantly higher than those in the invalid group. ROC curve analysis then revealed that the area under the curve (AUC) of ΔADC after one treatment was 0.872.

CONCLUSION

ADC values in brain metastases from lung cancer can help monitor and dynamically observe the therapeutic efficiency of whole brain chemoradiotherapy.

Diffusion profiling of tumor volumes using a histogram approach can predict proliferation and further microarchitectural features in medulloblastoma

BACKGROUND

Medulloblastomas are the most common central nervous system tumors in childhood. Treatment and prognosis strongly depend on histology and transcriptomic profiling. However, the proliferative potential also has prognostical value. Our study aimed to investigate correlations between histogram profiling of diffusion-weighted images and further microarchitectural features.

MATERIAL AND METHODS

Seven patients (age median 14.6 years, minimum 2 years, maximum 20 years; 5 male, 2 female) were included in this retrospective study. Using a Matlab-based analysis tool, histogram analysis of whole apparent diffusion coefficient (ADC) volumes was performed.

RESULTS

ADC entropy revealed a strong inverse correlation with the expression of the proliferation marker Ki67 (r = - 0.962, p = 0.009) and with total nuclear area (r = - 0.888, p = 0.044). Furthermore, ADC percentiles, most of all ADCp90, showed significant correlations with Ki67 expression (r = 0.902, p = 0.036).

DISCUSSION AND CONCLUSION

Diffusion histogram profiling of medulloblastomas provides valuable in vivo information which potentially can be used for risk stratification and prognostication. First of all, entropy revealed to be the most promising imaging biomarker. However, further studies are warranted.

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

Low grade meningiomas have better prognosis than high grade meningiomas. The aim of this study was to measure apparent diffusion coefficient (ADC) histogram analysis parameters in different meningiomas in a large multicenter sample and to analyze the possibility of several parameters for predicting tumor grade and proliferation potential. Overall, 148 meningiomas from 7 institutions were evaluated in this retrospective study. Grade 1 lesions were diagnosed in 101 (68.2%) cases, grade 2 in 41 (27.7%) patients, and grade 3 in 6 (4.1%) patients. All tumors were investigated by MRI (1.5 T scanner) by using diffusion weighted imaging (b values of 0 and 1000 s/mm²). For every lesion, the following parameters were calculated: mean ADC, maximum ADC, minimum ADC, median ADC, mode ADC, ADC percentiles P10, P25, P75, P90, kurtosis, skewness, and entropy. The comparison of ADC values was performed by Mann–Whitney-U test. Correlation between different ADC parameters and KI 67 was calculated by Spearman's rank correlation coefficient. Grade 2/3 meningiomas showed statistically significant lower ADC histogram analysis parameters in comparison to grade 1 tumors, especially ADC median. A threshold value of 0.82 for ADC median to predict tumor grade was estimated (sensitivity = 82.2%, specificity = 63.8%, accuracy = 76.4%, positive and negative predictive values were 83% and 62.5%, respectively).

All ADC parameters except maximum ADC showed weak significant correlations with KI 67, especially ADC P25 (P = −.340, P = .0001).

The diagnostic value of using combined MR diffusion tensor imaging parameters to differentiate between low- and high-grade meningioma

OBJECTIVE

The purpose of this study was to examine whether the combined use of MR diffusion tensor imaging (DTI) parameters [DTI-apparent diffusion coefficient (ADC), fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD)] could provide a more accurate diagnosis in differentiating between low-grade and atypical/anaplastic (high-grade) meningioma.

METHODS

Pathologically proven 45 meningioma patients [32 low-grade, 13 high-grade (11 atypical and 2 anaplastic)] who had received DTI before surgery were assessed retrospectively by 2 independent observers. For each lesion, MR DTI parameters (ADCmin, ADCmax, ADCmean, FA, AD, and RD) and ratios (rADCmin, rADCmax, rADCmean, rFA, rAD, and rRD) were calculated. When differentiating between low- and high-grade meningioma, the optimum cutoff values of all MR DTI parameters were determined by using receiver operating characteristic (ROC) analysis. Area under the curve (AUC) was measured with combined ROC analysis for different combinations of MR DTI parameters in order to identify the model combination with the best diagnostic accuracy in differentiation between low and high-grade meningioma.

RESULTS

Although the ADCmin, ADCmax, ADCmean, AD, RD, rADCmin, rADCmax, rADCmean, rAD, and rRD values of high-grade meningioma were significantly low (p = 0.007, p = 0.045, p = 0.035, p = 0.045, p = 0.003, p = 0.02, p = 0.03, p = 0.03, p = 0.045, and p = 0.01, respectively), when compared with low-grade meningioma, their FA and rFA values were significantly high (p = 0.007 and p = 0.01, respectively). For all MR DTI parameters, the highest individual distinctive power was RD with AUC of 0.778. The best diagnostic accuracy in differentiating between low- and high-grade meningioma was obtained by combining the ADCmin, RD, and FA parameters with 0.962 AUC.

CONCLUSION

This study shows that combined MR DTI parameters consisting of ADCmin, RD, and FA can differentiate high-grade from low-grade meningioma with a diagnostic accuracy of 96.2%. Advances in knowledge: To the best of our knowledge, this is the first study reporting that a combined use of all MR DTI parameters provides higher diagnostic accuracy for the differentiation of low- from high-grade meningioma. Our study shows that any of the model combinations was superior to use of any individual MR DTI parameters for differentiation between low and high-grade meningioma. A combination of ADCmin, RD, and FA was found to be the best model for differentiating low-grade from high-grade meningioma and sensitivity, specificity, and AUC values were found to be 92.3%, 100%, and 0.96, respectively. Thus, a combination of MR DTI parameters can provide more accurate diagnostic information when differentiation between low and high-grade meningioma.

Diagnostic value of apparent diffusion coefficient in differentiating between high-grade gliomas and brain metastases

Background High-grade gliomas (HGGs) and brain metastases (BMs) can display similar imaging characteristics on conventional MRI. In HGGs, the peritumoral edema may be infiltrated by the malignant cells, which was not observed in BMs. Purpose To determine whether the apparent diffusion coefficient values could differentiate HGGs from BMs. Material and Methods Fifty-seven patients underwent conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) before treatment. The minimum and mean ADC in the enhancing tumor (ADC_min, ADC_mean) and the minimum ADC in the peritumoral region (ADC_edema) were measured from ADC maps. To determine whether there was a statistical difference between groups, ADC values were compared. A receiver operating characteristic (ROC) curve analysis was used to determine the cutoff ADC value for distinguishing between HGGs and BMs. Results The mean ADC_min values in the intratumoral regions of HGGs were significantly higher than those in BMs. No differences were observed between groups regarding ADC_mean values. The mean ADC_min values in the peritumoral edema of HGGs were significantly lower than those in BMs. According to ROC curve analysis, a cutoff value of 1.332 × 10^-3mm²/s for the ADC_edema generated the best combination of sensitivity (95%) and specificity (84%) for distinguishing between HGGs and BMs. The same value showed a sensitivity of 95.6% and a specificity of 100% for distinguishing between GBMs and BMs. Conclusion ADC values from DWI were found to distinguish between HGGs and solitary BMs. The peritumoral ADC values are better than the intratumoral ADC values in predicting the tumor type.

Cystic Meningiomas: Correlation between Radiologic and Histopathologic Features

Background

Tumors with cysts often correlate with gliomas, metastatic tumors, or hemangioblastomas, which require differentiation.

Methods

Thirty-eight cases of cyst associated-meningioma based on preoperative radiologic studies and histologic confirmations were reviewed from November 1998 to July 2017.

Results

A total of 395 cases of meningioma were observed in the 20 years, and surgical treatment of intracranial meningioma was performed in 120 cases. Thirty-eight (9.6%) cases of cyst associated meningiomas were analyzed. Nauta type I was the most common type of cyst (39.5%) and the most frequent histopathological subtype was meningothelial type (36.8%).

Conclusion

Statistically there were no significant associations between meningioma histopathological type and associated cysts; however, the rate of World Health Organization grade II was higher in cyst associated meningiomas than in unrelated meningiomas. This correlation was weak, in accordance with the meningioma grade.

Apparent diffusion coefficients in prostate cancer: correlation with molecular markers Ki-67, HIF-1α and VEGF

Prostate cancer (PCa) is the second most common cancer in men. The Gleason score (GS) and biomarkers play important roles in the diagnosis and treatment of patients with PCa. The purpose of this study was to investigate the relationship between the apparent diffusion coefficient (ADC) and the molecular markers Ki-67, hypoxia-inducible factor-1α (HIF-1α) and vascular endothelial growth factor (VEGF) in PCa. Thirty-nine patients with 39 lesions, who had been diagnosed with PCa, were enrolled in this study. All patients underwent diffusion-weighted magnetic resonance imaging (DW-MRI) (b = 800 s/mm² ). The expression of Ki-67, HIF-1α and VEGF was assessed by immunohistochemistry. Statistical analysis was applied to analyze the association between ADC and prostate-specific antigen (PSA), GS and the expression of Ki-67, HIF-1α and VEGF. The group differences in ADC among different grades of Ki-67, HIF-1α and VEGF were also analyzed. The mean ± standard deviation of ADC was (0.76 ± 0.27) × 10^-3  mm² /s. ADC correlated negatively with PSA and GS (p < 0.05). The Ki-67 staining index (SI), HIF-1α expression and VEGF expression in PCa were correlated inversely with ADC, controlling for age (r = -0.332, p < 0.05; r = -0.662, p < 0.0005; and r = -0.714, p < 0.0005, respectively). ADC showed a significant difference among different grades of Ki-67 (F = 9.164, p = 0.005), HIF-1α (F = 40.333, p < 0.0005) and VEGF (F = 22.048, p < 0.0005). In conclusion, ADC was correlated with PSA, GS, and Ki-67, HIF-1α and VEGF expression in patients with PCa. ADC may be used to evaluate tumor proliferation, hypoxia and angiogenesis in PCa.

Radiographic prediction of meningioma grade by semantic and radiomic features

OBJECTIVES

The clinical management of meningioma is guided by tumor grade and biological behavior. Currently, the assessment of tumor grade follows surgical resection and histopathologic review. Reliable techniques for pre-operative determination of tumor grade may enhance clinical decision-making.

METHODS

A total of 175 meningioma patients (103 low-grade and 72 high-grade) with pre-operative contrast-enhanced T1-MRI were included. Fifteen radiomic (quantitative) and 10 semantic (qualitative) features were applied to quantify the imaging phenotype. Area under the curve (AUC) and odd ratios (OR) were computed with multiple-hypothesis correction. Random-forest classifiers were developed and validated on an independent dataset (n = 44).

RESULTS

Twelve radiographic features (eight radiomic and four semantic) were significantly associated with meningioma grade. High-grade tumors exhibited necrosis/hemorrhage (ORsem = 6.6, AUCrad = 0.62-0.68), intratumoral heterogeneity (ORsem = 7.9, AUCrad = 0.65), non-spherical shape (AUCrad = 0.61), and larger volumes (AUCrad = 0.69) compared to low-grade tumors. Radiomic and sematic classifiers could significantly predict meningioma grade (AUCsem = 0.76 and AUCrad = 0.78). Furthermore, combining them increased the classification power (AUCradio = 0.86). Clinical variables alone did not effectively predict tumor grade (AUCclin = 0.65) or show complementary value with imaging data (AUCcomb = 0.84).

CONCLUSIONS

We found a strong association between imaging features of meningioma and histopathologic grade, with ready application to clinical management. Combining qualitative and quantitative radiographic features significantly improved classification power.

Correlation Between Aquaporin 4 Expression and Different DWI Parameters in Grade I Meningioma

PURPOSE

Diffusion-weighted imaging (DWI) measures water diffusion in biological tissues. Cellular water transport depends on aquaporins (AQPs). The expression of aquaporins might differ in several pathologic disorders. Therefore, the aim of this study was to evaluate the associations between AQP4 expression and different DWI parameters in meningioma.

PROCEDURES

Twenty-three patients with meningioma grade I were included in this retrospective study. DWI was obtained with three b values (0; 500; 1000) using a 1.5-T device. ADC_mean, ADC_min, ADC_max, and true diffusion coefficients (D) were obtained in every patient. Aquaporin 4 expression was quantified immunohistochemically in four immunoreactivity levels.

RESULTS

The estimated DWI parameters (mean value ± standard deviation, 10^-3 mm² s^-1) of the tumors were as follows: ADC_min 0.67 ± 0.16, ADC_mean 0.94 ± 0.23, ADC_max 1.29 ± 0.50, and D 0.65 ± 0.23. The mean level of the AQP4 expression was 2.02 ± 0.75 points. A statistically significant correlation between AQP4 expression and ADC_max was identified (r = 0.508, p = 0.013). No significant correlations between AQP4 and other DWI parameters were found.

CONCLUSIONS

A clear correlation between AQP4 expression and ADC_max values in grade I meningioma was identified. There were no significant correlations between AQP4 expression and other DWI parameters, such as ADC_min, ADC_mean, and D.

Associations between apparent diffusion coefficient (ADC) and KI 67 in different tumors: a meta-analysis. Part 1: ADCmean

Diffusion weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique based on measure of water diffusion in tissues. This diffusion can be quantified by apparent diffusion coefficient (ADC). Some reports indicated that ADC can reflect tumor proliferation potential. The purpose of this meta-analysis was to provide evident data regarding associations between ADC and KI 67 in different tumors. Studies investigating the relationship between ADC and KI 67 in different tumors were identified. MEDLINE library was screened for associations between ADC and KI 67 in different tumors up to April 2017. Overall, 42 studies with 2026 patients were identified. The following data were extracted from the literature: authors, year of publication, number of patients, tumor type, and correlation coefficients. Associations between ADC and KI 67 were analyzed by Spearman's correlation coefficient. The reported Pearson correlation coefficients in some studies were converted into Spearman correlation coefficients. The pooled correlation coefficient between ADC_mean and KI 67 for all included tumors was ρ = -0.44. Furthermore, correlation coefficient for every tumor entity was calculated. The calculated correlation coefficients were as follows: ovarian cancer: ρ = -0.62, urothelial carcinomas: ρ = -0.56, cerebral lymphoma: ρ = -0.55, neuroendocrine tumors: ρ = -0.52, glioma: ρ = -0.51, lung cancer: ρ = -0.50, prostatic cancer: ρ = -0.43, rectal cancer: ρ = -0.42, pituitary adenoma:ρ = -0.44, meningioma, ρ = -0.43, hepatocellular carcinoma: ρ = -0.37, breast cancer: ρ = -0.22.

Cystic meningioma: radiological, histological, and surgical particularities in 43 patients

BACKGROUND

The presence of cysts is a rare occurrence for intracranial meningiomas in adults. We report our experience in a large consecutive series of cystic meningiomas.

METHOD

We prospectively collected data for a dedicated database of cystic meningioma cases between January 2004 and December 2011 in two tertiary neurosurgical centers. Studied data included preoperative imaging, surgical records, and pathology reports.

RESULTS

Among 1214 surgeries for intracranial meningioma, we identified 43 cases of cystic meningioma, corresponding to an incidence of 3.5 %. The most common localization was the hemispheric convexity (17/43 cases). Twenty-eight patients had intratumoral cysts, nine peritumoral, and five mixed intra and extratumoral. In 29 patients with available diffusion imaging, ADC coefficients were significantly lower in grade II-III tumors compared to grade I (p = 0.01). Complete resection of the cystic components was possible in 27/43 patients (63 %); partial resection in 4/43 (9 %); in 6/43 (14 %) cyst resection was not possible but multiple biopsies were performed from the cystic walls; in another 6/43 (14 %) the cystic wall was not identified during surgery. Cells with neoplastic features were identified within the cyst walls at pathology in 26/43 cases (60 %). All patients were followed-up for 24 months; long-term follow-up was available only in 32 patients for an average period of 49 months (range, 36-96 months). No recurrence requiring surgery was observed.

CONCLUSIONS

Cystic meningiomas are rare. Cells with neoplastic features are often identified within the cyst walls. Complete cyst resection is recommendable when considered technically feasible and safe.