Texture analysis of diffusion weighted imaging for the evaluation of glioma heterogeneity based on different regions of interest

Non-contrast-enhanced magnetic resonance urography for measuring split kidney function in pediatric patients with hydronephrosis: comparison with renal scintigraphy

BACKGROUND

Split kidney function (SKF) is critical for treatment decision in pediatric patients with hydronephrosis and is commonly measured using renal scintigraphy (RS). Non-contrast-enhanced magnetic resonance urography (NCE-MRU) is increasingly used in clinical practice. This study aimed to investigate the feasibility of using NCE-MRU as an alternative to estimate SKF in pediatric patients with hydronephrosis, compared to RS.

METHODS

Seventy-five pediatric patients with hydronephrosis were included in this retrospective study. All patients underwent NCE-MRU and RS within 2 weeks. Kidney parenchyma volume (KPV) and texture analysis parameters were obtained from T2-weighted (T2WI) in NCE-MRU. The calculated split KPV (SKPV) percent and texture analysis parameters percent of left kidney were compared with the RS-determined SKF.

RESULTS

SKPV showed a significant positive correlation with SKF (r = 0.88, p < 0.001), while inhomogeneity was negatively correlated with SKF (r =  - 0.68, p < 0.001). The uncorrected and corrected prediction models of SKF were established using simple and multiple linear regression. Bland-Altman plots demonstrated good agreement of both predictive models. The residual sum of squares of the corrected prediction model was lower than that of the uncorrected model (0.283 vs. 0.314) but not statistically significant (p = 0.662). Subgroup analysis based on different MR machines showed correlation coefficients of 0.85, 0.95, and 0.94 between SKF and SKPV for three different scanners, respectively (p < 0.05 for all).

CONCLUSIONS

NCE-MRU can be used as an alternative method for estimating SKF in pediatric patients with hydronephrosis when comparing with RS. Specifically, SKPV proves to be a simple and universally applicable indicator for predicting SKF.

Novel 3D magnetic resonance fingerprinting radiomics in adult brain tumors: a feasibility study

OBJECTIVES

To test the feasibility of using 3D MRF maps with radiomics analysis and machine learning in the characterization of adult brain intra-axial neoplasms.

METHODS

3D MRF acquisition was performed on 78 patients with newly diagnosed brain tumors including 33 glioblastomas (grade IV), 6 grade III gliomas, 12 grade II gliomas, and 27 patients with brain metastases. Regions of enhancing tumor, non-enhancing tumor, and peritumoral edema were segmented and radiomics analysis with gray-level co-occurrence matrices and gray-level run-length matrices was performed. Statistical analysis was performed to identify features capable of differentiating tumors based on type, grade, and isocitrate dehydrogenase (IDH1) status. Receiver operating curve analysis was performed and the area under the curve (AUC) was calculated for tumor classification and grading. For gliomas, Kaplan-Meier analysis for overall survival was performed using MRF T1 features from enhancing tumor region.

RESULTS

Multiple MRF T1 and T2 features from enhancing tumor region were capable of differentiating glioblastomas from brain metastases. Although no differences were identified between grade 2 and grade 3 gliomas, differentiation between grade 2 and grade 4 gliomas as well as between grade 3 and grade 4 gliomas was achieved. MRF radiomics features were also able to differentiate IDH1 mutant from the wild-type gliomas. Radiomics T1 features for enhancing tumor region in gliomas correlated to overall survival (p < 0.05).

CONCLUSION

Radiomics analysis of 3D MRF maps allows differentiating glioblastomas from metastases and is capable of differentiating glioblastomas from metastases and characterizing gliomas based on grade, IDH1 status, and survival.

KEY POINTS

• 3D MRF data analysis using radiomics offers novel tissue characterization of brain tumors. • 3D MRF with radiomics offers glioma characterization based on grade, IDH1 status, and overall patient survival.

Glioma Grading Prediction Using Multiparametric Magnetic Resonance Imaging-based Radiomics Combined with Proton Magnetic Resonance Spectroscopy and Diffusion Tensor Imaging

PURPOSE

To evaluate the efficacy of three-dimensional (3D) segmentation-based radiomics analysis of multiparametric MRI combined with proton magnetic resonance spectroscopy (¹ H-MRS) and diffusion tensor imaging (DTI) in glioma grading.

METHOD

A total of 100 patients with histologically confirmed gliomas (grade II-IV) were examined using conventional MRI, ¹ H-MRS, and DTI. Tumor segmentations of T1-weighted imaging (T1WI), contrast-enhanced T1WI (T1WI+C), T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) mapping, and fractional anisotropy (FA) mapping were performed. In total, 396 radiomics features were extracted and reduced using basic tests and least absolute shrinkage and selection operator (LASSO) regression. The selected features of each sequence were combined, and logistic regression with ten-fold cross-validation was applied to develop the grading model. Sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were compared. The model developed from the training set was applied to the test set to measure accuracy. One optimal grading quantitative parameter was selected for each ¹ H-MRS and DTI analysis. A radiomics nomogram model including radiomics signature, quantitative parameters, and clinical features was developed.

RESULTS

T1WI+C exhibited the highest grading efficacy among single sequences (AUC, 0.92; sensitivity, 0.89; specificity, 0.85), but the efficacy of the combined model was higher (AUC, 0.97; sensitivity, 0.94; specificity, 0.91). The AUCs of all models exhibited high accuracy, and no significant differences were observed in AUCs between the training and test sets. The visualized nomogram was developed based on the combined radiomics signature and choline (Cho)/N-acetyl aspartate (NAA) from ¹ H-MRS.

CONCLUSION

Multiparametric MRI can be used to predict the pathological grading of HGG and LGG by combining radiomics features with quantitative parameters. The visualized nomogram may provide an intuitive assessment tool in clinical practice.

CLINICAL TRIAL REGISTRATION

This trial was not registered, as it was a retrospective study and was approved by the local institutional review board. This article is protected by copyright. All rights reserved.

Comparison of In Vivo and Ex Vivo Magnetic Resonance Imaging in a Rat Model for Glioblastoma-Associated Epilepsy

Magnetic resonance imaging (MRI) is frequently used for preclinical treatment monitoring in glioblastoma (GB). Discriminating between tumors and tumor-associated changes is challenging on in vivo MRI. In this study, we compared in vivo MRI scans with ex vivo MRI and histology to estimate more precisely the abnormal mass on in vivo MRI. Epileptic seizures are a common symptom in GB. Therefore, we used a recently developed GB-associated epilepsy model from our group with the aim of further characterizing the model and making it useful for dedicated epilepsy research. Ten days after GB inoculation in rat entorhinal cortices, in vivo MRI (T2w and mean diffusivity (MD)), ex vivo MRI (T2w) and histology were performed, and tumor volumes were determined on the different modalities. The estimated abnormal mass on ex vivo T2w images was significantly smaller compared to in vivo T2w images, but was more comparable to histological tumor volumes, and might be used to estimate end-stage tumor volumes. In vivo MD images displayed tumors as an outer rim of hyperintense signal with a core of hypointense signal, probably reflecting peritumoral edema and tumor mass, respectively, and might be used in the future to distinguish the tumor mass from peritumoral edema—associated with reactive astrocytes and activated microglia, as indicated by an increased expression of immunohistochemical markers—in preclinical models. In conclusion, this study shows that combining imaging techniques using different structural scales can improve our understanding of the pathophysiology in GB.

Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors

PURPOSE

This is a radiomics study investigating the ability of texture analysis of MRF maps to improve differentiation between intra-axial adult brain tumors and to predict survival in the glioblastoma cohort.

METHODS

Magnetic resonance fingerprinting (MRF) acquisition was performed on 31 patients across 3 groups: 17 glioblastomas, 6 low-grade gliomas, and 8 metastases. Using regions of interest for the solid tumor and peritumoral white matter on T1 and T2 maps, second-order texture features were calculated from gray-level co-occurrence matrices and gray-level run length matrices. Selected features were compared across the three tumor groups using Wilcoxon rank-sum test. Receiver operating characteristic curve analysis was performed for each feature. Kaplan-Meier method was used for survival analysis with log rank tests.

RESULTS

Low-grade gliomas and glioblastomas had significantly higher run percentage, run entropy, and information measure of correlation 1 on T1 than metastases (p < 0.017). The best separation of all three tumor types was seen utilizing inverse difference normalized and homogeneity values for peritumoral white matter in both T1 and T2 maps (p < 0.017). In solid tumor T2 maps, lower values in entropy and higher values of maximum probability and high-gray run emphasis were associated with longer survival in glioblastoma patients (p < 0.05). Several texture features were associated with longer survival in glioblastoma patients on peritumoral white matter T1 maps (p < 0.05).

CONCLUSION

Texture analysis of MRF-derived maps can improve our ability to differentiate common adult brain tumors by characterizing tumor heterogeneity, and may have a role in predicting outcomes in patients with glioblastoma.

Progress of intravoxel incoherent motion diffusion-weighted imaging in liver diseases

Traditional magnetic resonance (MR) diffusion-weighted imaging (DWI) uses a single exponential model to obtain the apparent diffusion coefficient to quantitatively reflect the diffusion motion of water molecules in living tissues, but it is affected by blood perfusion. Intravoxel incoherent motion (IVIM)-DWI utilizes a double-exponential model to obtain information on pure water molecule diffusion and microcirculatory perfusion-related diffusion, which compensates for the insufficiency of traditional DWI. In recent years, research on the application of IVIM-DWI in the diagnosis and treatment of hepatic diseases has gradually increased and has achieved considerable progress. This study mainly reviews the basic principles of IVIM-DWI and related research progress in the diagnosis and treatment of hepatic diseases.

A simple model for glioma grading based on texture analysis applied to conventional brain MRI

Accuracy of glioma grading is fundamental for the diagnosis, treatment planning and prognosis of patients. The purpose of this work was to develop a low-cost and easy-to-implement classification model which distinguishes low-grade gliomas (LGGs) from high-grade gliomas (HGGs), through texture analysis applied to conventional brain MRI. Different combinations of MRI contrasts (T1Gd and T2) and one segmented glioma region (necrotic and non-enhancing tumor core, NCR/NET) were studied. Texture features obtained from the gray level size zone matrix (GLSZM) were calculated. An under-sampling method was proposed to divide the data into different training subsets and subsequently extract complementary information for the creation of distinct classification models. The sensitivity, specificity and accuracy of the models were calculated, and the best model explicitly reported. The best model included only three texture features and reached a sensitivity, specificity and accuracy of 94.12%, 88.24% and 91.18%, respectively. According to the features of the model, when the NCR/NET region was studied, HGGs had a more heterogeneous texture than LGGs in the T1Gd images, and LGGs had a more heterogeneous texture than HGGs in the T2 images. These novel results partially contrast with results from the literature. The best model proved to be useful for the classification of gliomas. Complementary results showed that the heterogeneity of gliomas depended on the MRI contrast studied. The chosen model stands out as a simple, low-cost, easy-to-implement, reproducible and highly accurate glioma classifier. Importantly, it should be accessible to populations with reduced economic and scientific resources.

Grading Gliomas Capability: Comparison between Visual Assessment and Apparent Diffusion Coefficient (ADC) Value Measurement on Diffusion-Weighted Imaging (DWI)

Background:

To compare diagnostic accuracy between DWI visual scale assessment and ADC value measurement of solid portion of the tumor in grading gliomas.

Methods:

This retrospective study included 38 patients who had pathologically proven gliomas between January 2013 and August 2018 with 18 low grade and 20 high grade tumors. All patients underwent MRI and biopsy. Two readers reviewed DWI visual scale independently. Disagreement was resolved by consensus. One reviewer measured ADC value of entire solid part of the tumor in single axial slice with greatest dimension of tumor which was chosen by consensus. Two data sets of visual scale and ADC value were analyzed and comparison of diagnostic accuracy in glioma grading was done by using area under the curve (AUC) of receiver operating characteristic curve (ROC).

Results:

Visual scale and ADC value could be used to distinguish between low and high grade gliomas with a statistically significant difference. (P-value 0.002 and <0.001). Almost all high grade gliomas had visual scale 5. The sensitivity, specificity, PPV NPV and accuracy were 50%, 100%, 100% , 64.3%,73.68% respectively. The cutoff level for the ADC value was determined to be 1119.48 x10^-6 mm²/s in differentiation between low and high grade gliomas with the sensitivity, specificity, PPV, NPV, accuracy of 90%, 88.89% , 90%, 88.9% and 89.47% respectively. There was no statistically significant difference(P-value = 0.163).

Conclusion:

Both Visual scale and ADC value were capable of differentiating between low and high grade gliomas. Although visual scale may not replace ADC measurement, larger scale prospective study is needed for validate this initial result.

Accuracy of magnetic resonance imaging texture analysis in differentiating low-grade from high-grade gliomas: systematic review and meta-analysis

OBJECTIVES

Texture analysis (TA) is a method used for quantifying the spatial distributions of intensities in images using scanning software. MRI TA could be applied to grade gliomas. This meta-analysis was performed for assessing the accuracy of MRI TA in differentiating low-grade gliomas from high-grade ones.

METHODS

PubMed, Cochrane Library, Science Direct and Embase were searched for identifying suitable studies from their inception to 1 September 2018. The quality of the studies was evaluated on the basis of the Quality Assessment of Diagnostic Accuracy Studies guidelines. We estimated the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic OR (DOR) using the summary receiver operating characteristic (SROC) for identifying the accuracy of MRI TA in grading gliomas. Fagan nomogram was applied for assessing the clinical utility of TA.

RESULTS

Six studies including 440 patients were included and analysed. The pooled sensitivity, specificity, PLR, NLR and DOR with 95% CIs were 0.93 (95% CI 0.88 to 0.96), 0.86 (95% CI 0.81 to 0.89), 6.4 (95% CI 4.8 to 8.6), 0.08 (95% CI 0.05 to 0.15) and 78 (95% CI 39 to 156), respectively. The SROC curve showed an area under the curve of 0.96 (95% CI 0.93 to 0.97). Deeks test confirmed no significant publication bias in all studies. Fagan nomogram revealed that the post-test probability increased by 43% in patients with positive pre-test.

CONCLUSIONS

The findings of this meta-analysis suggested that MRI TA has high accuracy in differentiating low-grade gliomas from high-grade ones. A standardised methodology is warranted to guide the use of this technique for clinical decision-making.

The Continuing Evolution of Molecular Functional Imaging in Clinical Oncology: The Road to Precision Medicine and Radiogenomics (Part II)

The present era of precision medicine sees "cancer" as a consequence of molecular derangements occurring at the commencement of the disease process, with morphological changes happening much later in the process of tumourigenesis. Conventional imaging techniques, such as computed tomography (CT), ultrasound (US) and magnetic resonance imaging (MRI) play an integral role in the detection of disease at the macroscopic level. However, molecular functional imaging (MFI) techniques entail the visualisation and quantification of biochemical and physiological processes occurring during tumourigenesis. MFI has the potential to play a key role in heralding the transition from the concept of "one-size-fits-all" treatment to "precision medicine". Integration of MFI with other fields of tumour biology such as genomics has spawned a novel concept called "radiogenomics", which could serve as an indispensable tool in translational cancer research. With recent advances in medical image processing, such as texture analysis, deep learning and artificial intelligence, the future seems promising; however, their clinical utility remains unproven at present. Despite the emergence of novel imaging biomarkers, the majority of these require validation before clinical translation is possible. In this two part review, we discuss the systematic collaboration across structural, anatomical and molecular imaging techniques that constitute MFI. Part I reviews positron emission tomography, radiogenomics, AI, and optical imaging, while part II reviews MRI, CT and ultrasound, their current status, and recent advances in the field of precision oncology.

Texture analysis on conventional MRI images accurately predicts early malignant transformation of low-grade gliomas

OBJECTIVES

Texture analysis performed on MRI images can provide additional quantitative information that is invisible to human assessment. This study aimed to evaluate the feasibility of texture analysis on preoperative conventional MRI images in predicting early malignant transformation from low- to high-grade glioma and compare its utility to histogram analysis alone.

METHODS

A total of 68 patients with low-grade glioma (LGG) were included in this study, 15 of which showed malignant transformation. Patients were randomly divided into training (60%) and testing (40%) sets. Texture analyses were performed to obtain the most discriminant factor (MDF) values for both training and testing data. Receiver operating characteristic (ROC) curve analyses were performed on MDF values and 9 histogram parameters in the training data to obtain cutoff values for determining the correct rates of discrimination between two groups in the testing data.

RESULTS

The ROC analyses on MDF values resulted in an area under the curve (AUC) of 0.90 (sensitivity 85%, specificity 84%) for T2w FLAIR, 0.92 (86%, 94%) for ADC, 0.96 (97%, 84%) for T1w, and 0.82 (78%, 75%) for T1w + Gd and correctly discriminated between the two groups in 93%, 100%, 93%, and 92% of cases in testing data, respectively. In the astrocytoma subgroup, AUCs were 0.92 (88%, 83%) for T2w FLAIR and 0.90 (92%, 74%) for T1w + Gd and correctly discriminated two groups in 100% and 92% of cases. The MDF outperformed all 9 of the histogram parameters.

CONCLUSION

Texture analysis on conventional preoperative MRI images can accurately predict early malignant transformation of LGGs, which may guide therapeutic planning.

KEY POINTS

• Texture analysis performed on MRI images can provide additional quantitative information that is invisible to human assessment. • Texture analysis based on conventional preoperative MR images can accurately predict early malignant transformation from low- to high-grade glioma. • Texture analysis is a clinically feasible technique that may provide an alternative and effective way of determining the likelihood of early malignant transformation and help guide therapeutic decisions.

Diagnostic accuracy of MRI texture analysis for grading gliomas

PURPOSE

Texture analysis (TA) can quantify variations in surface intensity or patterns, including some that are imperceptible to the human visual system. The purpose of this study was to determine the diagnostic accuracy of radiomic based filtration-histogram TA to differentiate high-grade from low-grade gliomas by assessing tumor heterogeneity.

METHODS

Patients with a histopathological diagnosis of glioma and preoperative 3T MRI imaging were included in this retrospective study. A region of interest was manually delineated on post-contrast T1 images. TA was performed using commercially available research software. The histogram parameters including mean, standard deviation, entropy, mean of the positive pixels, skewness, and kurtosis were analyzed at spatial scaling factors ranging from 0 to 6 mm. The parameters were correlated with WHO glioma grade using Spearman correlation. Areas under the curve (AUC) were calculated using ROC curve analysis to distinguish tumor grades.

RESULTS

Of a total of 94 patients, 14 had low-grade gliomas and 80 had high-grade gliomas. Mean, SD, MPP, entropy and kurtosis each showed significant differences between glioma grades for different spatial scaling filters. Low and high-grade gliomas were best-discriminated using mean of 2 mm fine texture scale, with a sensitivity and specificity of 93% and 86% (AUC of 0.90).

CONCLUSIONS

Quantitative measurement of heterogeneity using TA can discriminate high versus low-grade gliomas. Radiomic data of texture features can provide complementary diagnostic information for gliomas.