Histogram Analysis of Diffusion Weighted Imaging in Low-Grade Gliomas: in vivo Characterization of Tumor Architecture and Corresponding Neuropathology

MRI radiomics model for predicting TERT promoter mutation status in glioblastoma

BACKGROUND AND PURPOSE

The presence of TERT promoter mutations has been associated with worse prognosis and resistance to therapy for patients with glioblastoma (GBM). This study aimed to determine whether the combination model of different feature selections and classification algorithms based on multiparameter MRI can be used to predict TERT subtype in GBM patients.

METHODS

A total of 143 patients were included in our retrospective study, and 2553 features were obtained. The datasets were randomly divided into training and test sets in a ratio of 7:3. The synthetic minority oversampling technique was used to achieve data balance. The Pearson correlation coefficients were used for dimension reduction. Three feature selections and five classification algorithms were used to model the selected features. Finally, 10-fold cross validation was applied to the training dataset.

RESULTS

A model with eight features generated by recursive feature elimination (RFE) and linear discriminant analysis (LDA) showed the greatest diagnostic performance (area under the curve values for the training, validation, and testing sets: 0.983, 0.964, and 0.926, respectively), followed by relief and random forest (RF), analysis of variance and RF. Furthermore, the relief was the optimal feature selection for separately evaluating those five classification algorithms, and RF was the most preferable algorithm for separately assessing the three feature selectors. ADC entropy was the parameter that made the greatest contribution to the discrimination of TERT mutations.

CONCLUSIONS

Radiomics model generated by RFE and LDA mainly based on ADC entropy showed good performance in predicting TERT promoter mutations in GBM.

Multi-Modal Learning for Predicting the Genotype of Glioma

The isocitrate dehydrogenase (IDH) gene mutation is an essential biomarker for the diagnosis and prognosis of glioma. It is promising to better predict glioma genotype by integrating focal tumor image and geometric features with brain network features derived from MRI. Convolutional neural networks show reasonable performance in predicting IDH mutation, which, however, cannot learn from non-Euclidean data, e.g., geometric and network data. In this study, we propose a multi-modal learning framework using three separate encoders to extract features of focal tumor image, tumor geometrics and global brain networks. To mitigate the limited availability of diffusion MRI, we develop a self-supervised approach to generate brain networks from anatomical multi-sequence MRI. Moreover, to extract tumor-related features from the brain network, we design a hierarchical attention module for the brain network encoder. Further, we design a bi-level multi-modal contrastive loss to align the multi-modal features and tackle the domain gap at the focal tumor and global brain. Finally, we propose a weighted population graph to integrate the multi-modal features for genotype prediction. Experimental results on the testing set show that the proposed model outperforms the baseline deep learning models. The ablation experiments validate the performance of different components of the framework. The visualized interpretation corresponds to clinical knowledge with further validation. In conclusion, the proposed learning framework provides a novel approach for predicting the genotype of glioma.

Performance comparison of different medical image fusion algorithms for clinical glioma grade classification with advanced magnetic resonance imaging (MRI)

Non-invasive glioma grade classification is an exciting area in neuroimaging. The primary purpose of this study is to investigate the performance of different medical image fusion algorithms for glioma grading purposes by fusing advanced Magnetic Resonance Imaging (MRI) images. Ninety-six subjects underwent an Apparent diffusion coefficient (ADC) map and Susceptibility-weighted imaging (SWI) MRI scan. After preprocessing, the different medical image fusion methods used to fuse ADC maps and SWI were Principal Component Analysis (PCA), Structure-Aware, Discrete Cosine Harmonic Wavelet Transform (DCHWT), Deep-Convolutional Neural network (DNN), Dual-Discriminator conditional generative adversarial network (DDcGAN), and Laplacian Re-Decomposition (LRD). The Entropy, standard deviation (STD), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and Relative Signal Contrast (RSC) were calculated for qualitative and quantitative analysis. We found high fused image quality with LRD and DDcGAN methods. Further quantitative analysis showed that RSCs in fused images in Low-Grade glioma (LGG) were significantly higher than RSCs in High-Grade glioma (HGG) with PCA, DCHWT, LRD, and DDcGAN. The Receiver Operating Characteristic (ROC) curve test highlighted that LRD and DDcGAN have the highest performance for glioma grade classification. Our work suggests using the DDcGAN and LRD networks for glioma grade classification by fusing ADC maps and SWI images.

The value of whole-volume apparent diffusion coefficient histogram analysis in preoperatively distinguishing intracranial solitary fibrous tumor and transitional meningioma

Purpose

To investigate the value of whole-volume apparent diffusion coefficient (ADC) histogram analysis in preoperatively distinguishing intracranial solitary fibrous tumors (SFT) from transitional meningiomas (TM), thereby assisting the establishment of the treatment protocol.

Methods

Preoperative diffusion-weighted imaging datasets of 24 patients with SFT and 28 patients with TM were used to extract whole-volume ADC histogram parameters, including variance, skewness, kurtosis, and mean, as well as 1st (AP1), 10th (AP10), 50th (AP50), 90th (AP90), and 99th (AP99) percentiles of ADC using MaZda software. The independent t-test or Mann-Whitney U test was used to compare the differences between ADC histogram parameters of SFT and TM. Receiver operating characteristic (ROC) curves were generated to evaluate the performance of significant ADC histogram parameters. Spearman's correlation coefficients were calculated to evaluate correlations between these parameters and the Ki-67 expression levels.

Results

SFT exhibited significantly higher variance, and lower AP1 and AP10 (all P < 0.05) than TM. The best diagnostic performance was obtained by variance, with an area under the ROC curve of 0.848 (0.722-0.933). However, there was no significant difference in skewness, kurtosis, mean, or other percentiles of ADC between the two groups (all P > 0.05). Significant correlations were also observed between the Ki-67 proliferation index and variance (r = 0.519), AP1 (r = -0.425), and AP10 (r = -0.372) (all P < 0.05).

Conclusion

Whole-volume ADC histogram analysis is a feasible tool for non-invasive preoperative discrimination between intracranial SFT and TM, with variance being the most promising prospective parameter.

Apparent diffusion coefficient values effectively predict cell proliferation and determine oligodendroglioma grade

This study aims to evaluate the value of conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) values in differentiating oligodendroglioma of various grades and explore the correlation between ADC and Ki-67. The preoperative MRI data of 99 patients with World Health Organization (WHO) grades 2 (n = 42) and 3 (n = 57) oligodendroglioma confirmed by surgery and pathology were retrospectively analyzed. Conventional MRI features, ADCmean, ADCmin, and normalized ADC (nADC) were compared between the two groups. A receiver operating characteristic curve was used to evaluate each parameter's diagnostic efficacy in differentiating the two tumor types. Each tumor's Ki-67 proliferation index was also measured to explore its relationship with the ADC value. Compared with WHO2 grade tumors, WHO3 grade tumors had a larger maximum diameter and more significant cystic degeneration/necrosis, edema, and moderate/severe enhancement (all P < 0.05). The ADCmin, ADCmean, and nADC values of the WHO3 and WHO2 grade tumors were significantly different, and the ADCmin value most accurately distinguished the two tumor types, yielding an area under the curve value of 0.980. When 0.96 × 10^-3 mm²/s was used as the differential diagnosis threshold, the sensitivity, specificity, and accuracy of the two groups were 100%, 93.00%, and 96.96%, respectively. The ADCmin (r = -0.596), ADCmean (r =  - 0.590), nADC (r =  - 0.577), and Ki-67 proliferation index values had significantly negative correlations (all P < 0.05). Conventional MRI features and ADC values are beneficial in the noninvasive prediction of the WHO grade and tumor proliferation rate of oligodendroglioma.

Application of Apparent Diffusion Coefficient Histogram Metrics for Differentiation of Pediatric Posterior Fossa Tumors : A Large Retrospective Study and Brief Review of Literature

PURPOSE

This study aimed to evaluate the application of apparent diffusion coefficient (ADC) histogram analysis to differentiate posterior fossa tumors (PFTs) in children.

METHODS

A total of 175 pediatric patients with PFT, including 75 pilocytic astrocytomas (PA), 59 medulloblastomas, 16 ependymomas, and 13 atypical teratoid rhabdoid tumors (ATRT), were analyzed. Tumors were visually assessed using DWI trace and conventional MRI images and manually segmented and post-processed using parametric software (pMRI). Furthermore, tumor ADC values were normalized to the thalamus and cerebellar cortex. The following histogram metrics were obtained: entropy, minimum, 10th, and 90th percentiles, maximum, mean, median, skewness, and kurtosis to distinguish the different types of tumors. Kruskal Wallis and Mann-Whitney U tests were used to evaluate the differences. Finally, receiver operating characteristic (ROC) curves were utilized to determine the optimal cut-off values for differentiating the various PFTs.

RESULTS

Most ADC histogram metrics showed significant differences between PFTs (p < 0.001) except for entropy, skewness, and kurtosis. There were significant pairwise differences in ADC metrics for PA versus medulloblastoma, PA versus ependymoma, PA versus ATRT, medulloblastoma versus ependymoma, and ependymoma versus ATRT (all p < 0.05). Our results showed no significant differences between medulloblastoma and ATRT. Normalized ADC data showed similar results to the absolute ADC value analysis. ROC curve analysis for normalized ADC_median values to thalamus showed 94.9% sensitivity (95% CI: 85-100%) and 93.3% specificity (95% CI: 87-100%) for differentiating medulloblastoma from ependymoma.

CONCLUSION

ADC histogram metrics can be applied to differentiate most types of posterior fossa tumors in children.

Discrimination between microcystic meningioma and atypical meningioma using whole-lesion apparent diffusion coefficient histogram analysis

AIM

To explore the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in discriminating microcystic meningioma (MCM) from atypical meningioma (AM).

MATERIALS AND METHODS

Clinical and preoperative MRI data of 20 patients with MCM and 26 patients with AM were analysed retrospectively. Whole-lesion apparent diffusion coefficient (ADC) histogram analysis was performed on each patient's lesion to obtain histogram parameters, including mean, variance, skewness, kurtosis, the 1st (ADCp1), 10th (ADCp10), 50th (ADCp50), 90th (ADCp90), and 99th (ADCp99) percentiles of ADC. The differences between the ADC histogram parameters of the two tumours were compared, and the receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of statistically significant parameters in distinguishing the two tumours.

RESULTS

The mean, ADCp1, ADCp10, ADCp50, and ADCp90 of MCM were greater than those of AM, and significant differences were observed in these parameters between MCM and AM (all p<0.05). ROC analysis showed that the mean had the highest area under the curve value (AUC) in distinguishing the two tumours (AUC = 0.852), when using 120.46 × 10^-6 mm²/s as the optimal threshold, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for discriminating the two groups were 84.6%, 75%, 80.4%, 81.5%, and 78.9%, respectively.

CONCLUSION

Histogram analysis based on whole-lesion ADC maps was useful for discriminating between MCM from AM preoperatively, with the mean being the most promising potential parameter.

Diffusion Weighted Imaging in Gliomas: A Histogram-Based Approach for Tumor Characterization

Simple Summary

Glioma represent approximately one-third of all brain tumors. Although they differ clinically, histologically and genetically, they often are not distinguishable by morphological magnetic resonance imaging (MRI) diagnostics. We therefore investigated in this retrospective study whether diffusion weighted imaging (DWI) using a radiomic approach could provide complementary information with respect to tumor differentiation and cell proliferation, as well as the underlying genetic and epigenetic tumor profile. We identified several histogram features that could facilitate presurgical tumor grading and potentially enable one to draw conclusions about tumor characteristics on a cellular and subcellular scale.

Abstract

(1) Background: Astrocytic gliomas present overlapping appearances in conventional MRI. Supplementary techniques are necessary to improve preoperative diagnostics. Quantitative DWI via the computation of apparent diffusion coefficient (ADC) histograms has proven valuable for tumor characterization and prognosis in this regard. Thus, this study aimed to investigate (I) the potential of ADC histogram analysis (HA) for distinguishing low-grade gliomas (LGG) and high-grade gliomas (HGG) and (II) whether those parameters are associated with Ki-67 immunolabelling, the isocitrate-dehydrogenase-1 (IDH1) mutation profile and the methylguanine-DNA-methyl-transferase (MGMT) promoter methylation profile; (2) Methods: The ADC-histograms of 82 gliomas were computed. Statistical analysis was performed to elucidate associations between histogram features and WHO grade, Ki-67 immunolabelling, IDH1 and MGMT profile; (3) Results: Minimum, lower percentiles (10th and 25th), median, modus and entropy of the ADC histogram were significantly lower in HGG. Significant differences between IDH1-mutated and IDH1-wildtype gliomas were revealed for maximum, lower percentiles, modus, standard deviation (SD), entropy and skewness. No differences were found concerning the MGMT status. Significant correlations with Ki-67 immunolabelling were demonstrated for minimum, maximum, lower percentiles, median, modus, SD and skewness; (4) Conclusions: ADC HA facilitates non-invasive prediction of the WHO grade, tumor-proliferation rate and clinically significant mutations in case of astrocytic gliomas.

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.

Differentiation of intracranial solitary fibrous tumor/hemangiopericytoma from atypical meningioma using apparent diffusion coefficient histogram analysis

This study aimed to investigate the value of apparent diffusion coefficient (ADC) histogram analysis in differentiating intracranial solitary fibrous tumor/hemangiopericytoma (SFT/HPC) from atypical meningioma (ATM). Retrospective analyzed the clinical, magnetic resonance imaging, and pathological data of 20 and 25 patients with SFT/HPC and ATM, respectively. Histogram analysis was performed on the axial ADC images using MaZda software, and nine histogram parameters were obtained, including mean, variance, skewness, kurtosis, and the 1^st (ADC1), 10^th (ADC10), 50^th (ADC50), 90^th (ADC90), and 99^th (ADC99) percentile ADC. Differences in ADC histogram parameters between SFT/HPC and ATM were compared by an independent t test or Mann-Whitney U test, while the statistically significant histogram parameters were further analyzed by drawing receiver operating characteristic (ROC) curves to evaluate the differential diagnostic performance. Among the nine ADC histogram parameters we extracted, the mean, ADC1, ADC10, ADC50, and ADC90 in the SFT/HPC group were greater than those of ATM, and significant differences were observed (all P < 0.05). ROC analysis showed that the ADC1 generated the highest area under the curve (AUC) value of 0.920 in distinguishing the two tumors, when using 91.00 as the optimal threshold. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value in distinguishing between SFT/HPC and ATM were 84.00%, 85.00%, 84.44%, 87.50%, and 81.00%, respectively. ADC histogram analysis can be a reliable tool to differentiate between SFT/HPC and ATM, with the ADC1 being the most promising potential parameter.

Noninvasive differentiation of molecular subtypes of adult non-enhancing glioma using MRI perfusion and diffusion parameters

Background

Nonenhancing glioma typically have a favorable outcome, but approximately 19–44% have a highly aggressive course due to a glioblastoma genetic profile. The aim of this retrospective study is to use physiological MRI parameters of both perfusion and diffusion to distinguish the molecular profiles of glioma without enhancement at presentation.

Methods

Ninety-nine patients with nonenhancing glioma were included, in whom molecular status (including 1p/19q codeletion status and IDH mutation) and preoperative MRI (T2w/FLAIR, dynamic susceptibility-weighted, and diffusion-weighted imaging) were available. Tumors were segmented semiautomatically using ITK-SNAP to derive whole tumor histograms of relative Cerebral Blood Volume (rCBV) and Apparent Diffusion Coefficient (ADC). Tumors were divided into three clinically relevant molecular profiles: IDH mutation (IDHmt) with (n = 40) or without (n = 41) 1p/19q codeletion, and (n = 18) IDH-wildtype (IDHwt). ANOVA, Kruskal-Wallis, and Chi-Square analyses were performed using SPSS.

Results

rCBV (mean, median, 75^th and 85^th percentile) and ADC (mean, median, 15^th and 25^th percentile) showed significant differences across molecular profiles (P < .01). Posthoc analyses revealed that IDHwt and IDHmt 1p/19q codeleted tumors showed significantly higher rCBV compared to IDHmt 1p/19q intact tumors: mean rCBV (mean, SD) 1.46 (0.59) and 1.35 (0.39) versus 1.08 (0.31), P < .05. Also, IDHwt tumors showed significantly lower ADC compared to IDHmt 1p/19q codeleted and IDHmt 1p/19q intact tumors: mean ADC (mean, SD) 1.13 (0.23) versus 1.27 (0.15) and 1.45 (0.20), P < .001).

Conclusions

A combination of low ADC and high rCBV, reflecting high cellularity and high perfusion respectively, separates IDHwt from in particular IDHmt 1p/19q intact glioma.

Reverse Engineering Glioma Radiomics to Conventional Neuroimaging

A novel radiological research field pursuing comprehensive quantitative image, namely “Radiomics,” gained traction along with the advancement of computational technology and artificial intelligence. This novel concept for analyzing medical images brought extensive interest to the neuro-oncology and neuroradiology research community to build a diagnostic workflow to detect clinically relevant genetic alteration of gliomas noninvasively. Although quite a few promising results were published regarding MRI-based diagnosis of isocitrate dehydrogenase (IDH) mutation in gliomas, it has become clear that an ample amount of effort is still needed to render this technology clinically applicable. At the same time, many significant insights were discovered through this research project, some of which could be “reverse engineered” to improve conventional non-radiomic MR image acquisition. In this review article, the authors aim to discuss the recent advancements and encountering issues of radiomics, how we can apply the knowledge provided by radiomics to standard clinical images, and further expected technological advances in the realm of radiomics and glioma.

Whole-Lesion Apparent Diffusion Coefficient Histogram Analysis: Significance for Discriminating Lung Cancer from Pulmonary Abscess and Mycobacterial Infection

Simple Summary

Diffusion-weighted magnetic resonance imaging (DWI) can differentiate malignant from benign pulmonary nodules and masses. However, it is difficult to differentiate pulmonary abscesses and mycobacterium infections (PAMIs) from lung cancers because PAMIs show restricted diffusion in DWI. The purpose of this study was to establish the role of ADC histogram for differentiating lung cancer from PAMI. There were 41 lung cancers and 19 PAMIs. Parameters more than 60% of AUC were ADC, maximal ADC, mean ADC, median ADC, most frequency ADC, kurtosis of ADC, and volume of lesion. There were significant differences between lung cancer and PAMI in ADC, mean ADC, median ADC, and most frequency ADC. ADC histogram has the potential to be a valuable tool to differentiate PAMI from lung cancer.

Abstract

Diffusion-weighted magnetic resonance imaging (DWI) can differentiate malignant from benign pulmonary nodules. However, it is difficult to differentiate pulmonary abscesses and mycobacterial infections (PAMIs) from lung cancers because PAMIs show restricted diffusion in DWI. The study purpose is to establish the role of ADC histogram for differentiating lung cancer from PAMI. There were 41 lung cancers (25 adenocarcinomas, 16 squamous cell carcinomas), and 19 PAMIs (9 pulmonary abscesses, 10 mycobacterial infections). Parameters more than 60% of the area under the ROC curve (AUC) were ADC, maximal ADC, mean ADC, median ADC, most frequency ADC, kurtosis of ADC, and volume of lesion. There were significant differences between lung cancer and PAMI in ADC, mean ADC, median ADC, and most frequency ADC. The ADC (1.19 ± 0.29 × 10⁻³ mm²/s) of lung cancer obtained from a single slice was significantly lower than that (1.44 ± 0.54) of PAMI (p = 0.0262). In contrast, mean, median, or most frequency ADC of lung cancer which was obtained in the ADC histogram was significantly higher than the value of each parameter of PAMI. ADC histogram could discriminate PAMIs from lung cancers by showing that AUCs of several parameters were more than 60%, and that several parameters of ADC of PAMI were significantly lower than those of lung cancer. ADC histogram has the potential to be a valuable tool to differentiate PAMI from lung cancer.

Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study

Purpose

To evaluate isocitrate dehydrogenase (IDH) status in clinically diagnosed grade II~IV glioma patients using the 2016 World Health Organization (WHO) classification based on MRI parameters.

Materials and Methods

One hundred and seventy-six patients with confirmed WHO grade II~IV glioma were retrospectively investigated as the study set, including lower-grade glioma (WHO grade II, n = 64; WHO grade III, n = 38) and glioblastoma (WHO grade IV, n = 74). The minimum apparent diffusion coefficient (ADCmin) in the tumor and the contralateral normal-appearing white matter (ADCn) and the rADC (ADCmin to ADCn ratio) were defined and calculated. Intraclass correlation coefficient (ICC) analysis was carried out to evaluate interobserver and intraobserver agreement for the ADC measurements. Interobserver agreement for the morphologic categories was evaluated by Cohen’s kappa analysis. The nonparametric Kruskal-Wallis test was used to determine whether the ADC measurements and glioma subtypes were related. By univariable analysis, if the differences in a variable were significant (P<0.05) or an image feature had high consistency (ICC >0.8; κ >0.6), then it was chosen as a predictor variable. The performance of the area under the receiver operating characteristic curve (AUC) was evaluated using several machine learning models, including logistic regression, support vector machine, Naive Bayes and Ensemble. Five evaluation indicators were adopted to compare the models. The optimal model was developed as the final model to predict IDH status in 40 patients with glioma as the subsequent test set. DeLong analysis was used to compare significant differences in the AUCs.

Results

In the study set, six measured variables (rADC, age, enhancement, calcification, hemorrhage, and cystic change) were selected for the machine learning model. Logistic regression had better performance than other models. Two predictive models, model 1 (including all predictor variables) and model 2 (excluding calcification), correctly classified IDH status with an AUC of 0.897 and 0.890, respectively. The test set performed equally well in prediction, indicating the effectiveness of the trained classifier. The subgroup analysis revealed that the model predicted IDH status of LGG and GBM with accuracy of 84.3% (AUC = 0.873) and 85.1% (AUC = 0.862) in the study set, and with the accuracy of 70.0% (AUC = 0.762) and 70.0% (AUC = 0.833) in the test set, respectively.

Conclusion

Through the use of machine-learning algorithms, the accurate prediction of IDH-mutant versus IDH-wildtype was achieved for adult diffuse gliomas via noninvasive MR imaging characteristics, including ADC values and tumor morphologic features, which are considered widely available in most clinical workstations.

Neuroimaging in the Era of the Evolving WHO Classification of Brain Tumors, From the AJR Special Series on Cancer Staging

The inclusion of molecular and genetic information with histopathologic information defines the framework for brain tumor classification and grading. This framework is reflected in the major restructuring of the WHO brain tumor classification system in 2016 and in numerous subsequent proposed updates reflecting ongoing developments in understanding the impact of tumor genotype on classification and grading. This incorporation of molecular and genetic features improves tumor diagnosis and prediction of tumor behavior and response to treatment. Neuroimaging is essential for the noninvasive assessment of pretreatment tumor grading and for identification and determination of therapeutic efficacy. Use of conventional neuroimaging and physiologic imaging techniques, such as diffusion- and perfusion-weighted MRI, can increase diagnostic confidence before and after treatment. Although the use of neuroimaging to consistently determine tumor genetics is not yet robust, promising developments are on the horizon. Given the complexity of the brain tumor microenvironment, the development and implementation of a standardized reporting system can aid in conveying to radiologists, referring providers, and patients important information about brain tumor response to treatment. The purpose of this article is to review the current state and role of neuroimaging in this continuously evolving field.

miRNA-193a-3p Regulates the AKT2 Pathway to Inhibit the Growth and Promote the Apoptosis of Glioma Cells by Targeting ALKBH5

Emerging evidence indicates that microRNA (miR)-193a-3p is involved in the tumor progression of various cancers. However, the biological functions and precise molecular mechanisms of miR-193a-3p in gliomas have not been well documented. Accordingly, this study focused on the tumor suppressor role and molecular mechanisms of miR-193a-3p in glioma cells. miR-193a-3p expression was determined by qRT-PCR in glioma tissues and cell lines. U251 and U87 glioma cells were transfected with a miR-193a-3p mimic. The effects of miR-193a-3p on cell growth and apoptosis were investigated using MTT, colony-forming, and flow cytometry assays. Overexpression of miR-193a-3p in U87 cells also significantly suppressed tumorigenicity and induced apoptosis in the xenograft mouse model. Luciferase assays were conducted to determine if ALKBH5 is a direct target of miR-193a-3p in glioma cells. Immunoprecipitation was used to explore the interaction between ALKBH5 and RAC-serine/threonine-protein kinase 2 (AKT2) in glioma cells. miR-193a-3p was downregulated in glioma tissues and cell lines. miR-193a-3p treatment suppressed proliferation and promoted apoptosis in both U251 and U87 cells. Bioinformatics analysis and luciferase reporter assay identified a novel miR-193a-3p target, ALKBH5. Notably, the antitumor effect of miR-193a-3p transfection in glioma cells may be due to the miR-193a-3p–induced inhibition of AKT2 expression caused by the suppression of ALKBH5 expression. Furthermore, immunoprecipitation indicated that ALKBH5 physically interacted with AKT2 through an RNA-independent mechanism in glioma cells. miR-193a-3p directly targets ALKBH5 to inhibit the growth and promote the apoptosis of glioma cells by suppressing the AKT2 pathway both in vitro and in vivo, and the physical interaction between ALKBH5 and AKT2 is essential for suppressing cell apoptosis by upregulating miR-193a-3p in glioma cells. Our study revealed that the antitumor effects of miR-193a-3p on glioma cells is due to ALKBH5 mediation of the AKT2-induced intrinsic apoptosis signaling pathway.

Quartile histogram assessment of glioma malignancy using high b-value diffusion MRI with a continuous-time random-walk model

The purpose of this study is to investigate the feasibility of using a continuous-time random-walk (CTRW) diffusion model, together with a quartile histogram analysis, for assessing glioma malignancy by probing tissue heterogeneity as well as cellularity. In this prospective study, 91 patients (40 females, 51 males) with histopathologically proven gliomas underwent MRI at 3 T. The cohort included 42 grade II (GrII), 19 grade III (GrIII) and 29 grade IV (GrIV) gliomas. Echo-planar diffusion-weighted imaging was conducted using 17 b-values (0-4000 s/mm² ). Three CTRW model parameters, including an anomalous diffusion coefficient D_m , and two parameters related to temporal and spatial diffusion heterogeneity α and β, respectively, were obtained. The mean parameter values within the tumor regions of interest (ROIs) were computed by utilizing the first quartile of the histograms as well as the full ROI for comparison. A Bonferroni-Holm-corrected Mann-Whitney U-test was used for the group comparisons. Individual and combinations of the CTRW parameters were evaluated for the characterization of gliomas with a receiver operating characteristic analysis. All first-quartile mean CTRW parameters yielded significant differences (p-values < 0.05) between pair-wise comparisons of GrII (D_m : 1.14 ± 0.37 μm² /ms; α: 0.904 ± 0.03, β: 0.913 ± 0.06), GrIII (D_m : 0.88 ± 0.21 μm² /ms; α: 0.888 ± 0.01, β: 0.857 ± 0.06) and GrIV gliomas (D_m : 0.73 ± 0.22 μm² /ms; α: 0.878 ± 0.01; β: 0.791 ± 0.07). The highest sensitivity, specificity, accuracy and area-under-the-curve of using the combinations of the first-quartile parameters were 84.2%, 78.5%, 75.4% and 0.76 for GrII and GrIII classification; 86.2%, 89.4%, 75% and 0.76 for GrIII and GrIV classification; and 86.2%, 85.7%, 84.5% and 0.90 for GrII and GrIV classification, respectively. Quartile-based analysis produced higher accuracy and area-under-the-curve than the full ROI-based analysis in all classifications. The CTRW diffusion model, together with a quartile-based histogram analysis, offers a new way for probing tumor structural heterogeneity at a subvoxel level, and has potential for in vivo assessment of glioma malignancy to complement histopathology.

Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review

BACKGROUND

Determination of isocitrate dehydrogenase (IDH) status and, if IDH-mutant, assessing 1p19q codeletion are an important component of diagnosis of World Health Organization grades II/III or lower-grade gliomas. This has led to research into noninvasively correlating imaging features ("radiomics") with genetic status.

PURPOSE

Our aim was to perform a diagnostic test accuracy systematic review for classifying IDH and 1p19q status using MR imaging radiomics, to provide future directions for integration into clinical radiology.

DATA SOURCES

Ovid (MEDLINE), Scopus, and the Web of Science were searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy guidelines.

STUDY SELECTION

Fourteen journal articles were selected that included 1655 lower-grade gliomas classified by their IDH and/or 1p19q status from MR imaging radiomic features.

DATA ANALYSIS

For each article, the classification of IDH and/or 1p19q status using MR imaging radiomics was evaluated using the area under curve or descriptive statistics. Quality assessment was performed with the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the radiomics quality score.

DATA SYNTHESIS

The best classifier of IDH status was with conventional radiomics in combination with convolutional neural network-derived features (area under the curve  = 0.95, 94.4% sensitivity, 86.7% specificity). Optimal classification of 1p19q status occurred with texture-based radiomics (area under the curve  = 0.96, 90% sensitivity, 89% specificity).

LIMITATIONS

A meta-analysis showed high heterogeneity due to the uniqueness of radiomic pipelines.

CONCLUSIONS

Radiogenomics is a potential alternative to standard invasive biopsy techniques for determination of IDH and 1p19q status in lower-grade gliomas but requires translational research for clinical uptake.

Diffusion-weighted MR imaging histogram analysis in HIV positive and negative patients with primary central nervous system lymphoma as a predictor of outcome and tumor proliferation

INTRODUCTION

Ki-67 expression, a marker of tumor proliferation, is considered a prognostic factor in primary CNS lymphoma (PCNSL). Apparent diffusion coefficient (ADC) parameters have also been proposed as imaging biomarkers for tumor progression and proliferative activity in various malignancies. The aim of this study is to investigate the correlation between ADC parameters, Ki-67 expression, overall survival (OS) and progression free survival (PFS) in PCNSL.

MATERIALS AND METHODS

Patients diagnosed with PCNSL at MD Anderson Cancer Center between Mar 2000 and Jul 2016 and at Ben Taub Hospital between Jan 2012 and Dec 2016 were retrospectively studied. Co-registered ADC maps and post-contrast images underwent whole tumor segmentation. Normalized ADC parameters (nADC) were calculated as the ratio to normal white matter. Percentiles of nADC were calculated and were correlated with Ki-67 using Pearson's correlation coefficient and clinical outcomes (OS and PFS) using Cox proportional hazards models.

RESULTS

Selection criteria yielded 90 patients, 23 patients living with HIV (PLWH) and 67 immunocompetent patients. Above median values for nADC_mean, nADC₁₅, nADC₇₅ and nADC₉₅ were associated with improved OS in all patients (p < 0.05). Above median values for nADC_min, nADC_mean, nADC₁, nADC₅ and kurtosis were associated with improved PFS in all patients (p < 0.05). In patients with available Ki-67 expression data (n = 22), nADC_mean, nADC₁₅ and nADC₇₅ inversely correlated with Ki-67 expression (p < 0.05). For PLWH, there was no correlation between ADC parameters and Ki-67 expression or clinical outcomes.

CONCLUSIONS

ADC histogram analysis can predict tumor proliferation and survival in immunocompetent patients with PCNSL, but with limited utility in PLWH.