Regional and Volumetric Parameters for Diffusion-Weighted WHO Grade II and III Glioma Genotyping: A Method Comparison

Prediction of TERT mutation status in gliomas using conventional MRI radiogenomic features

Objective

Telomerase reverse transcriptase (TERT) promoter mutation status in gliomas is a key determinant of treatment strategy and prognosis. This study aimed to analyze the radiogenomic features and construct radiogenomic models utilizing medical imaging techniques to predict the TERT promoter mutation status in gliomas.

Methods

This was a retrospective study of 304 patients with gliomas. T1-weighted contrast-enhanced, apparent diffusion coefficient, and diffusion-weighted imaging MRI sequences were used for radiomic feature extraction. A total of 3,948 features were extracted from MRI images using the FAE software. These included 14 shape features, 18 histogram features, 24 gray level run length matrix, 14 gray level dependence matrix, 16 gray level run length matrix, 16 gray level size zone matrix (GLSZM), 5 neighboring gray tone difference matrix, and 744 wavelet transforms. The dataset was randomly divided into training and testing sets in a ratio of 7:3. Three feature selection methods and six classification algorithms were used to model the selected features. Predictive performance was evaluated using receiver operating characteristic curve analysis.

Results

Among the evaluated classification algorithms, the combination model of recursive feature elimination (RFE) with linear regression (LR) using six features showed the best diagnostic performance (area under the curve: 0.733, 0.562, and 0.633 in the training, validation, and testing sets, respectively). The next best-performing models were naive Bayes, linear discriminant analysis, autoencoder, and support vector machine. Regarding the three feature selection algorithms, RFE showed the most consistent performance, followed by relief and ANOVA. T1-enhanced entropy and GLSZM derived from T1-enhanced images were identified as the most critical radiomics features for distinguishing TERT promoter mutation status.

Conclusion

The LR and LRLasso models, mainly based on T1-enhanced entropy and GLSZM, showed good predictive ability for TERT promoter mutations in gliomas using radiomics models.

Isocitrate Dehydrogenase 1/2 Wildtype Adult Astrocytoma with WHO Grade 2/3 Histological Features: Molecular Re-Classification, Prognostic Factors, Clinical Outcomes

BACKGROUND

Isocitrate Dehydrogenase 1/2 (IDH 1/2)-wildtype (WT) astrocytomas constitute a heterogeneous group of tumors and have undergone a series of diagnostic reclassifications over time. This study aimed to investigate molecular markers, clinical, imaging, and treatment factors predictive of outcomes in WHO grade 2/3 IDH-WT astrocytomas ('early glioblastoma').

METHODOLOGY

Patients with WHO grade 2/3 IDH-WT astrocytomas were identified from the hospital archives. They were cross-referenced with the electronic medical records systems, including neuroimaging. The expert neuro-pathology team retrieved data on molecular markers-MGMT, TERT, IDH, and EGFR. Tumors with a TERT mutation and/or EGFR amplification were reclassified as glioblastoma.

RESULTS

Fifty-four patients were identified. Sixty-three percent of the patients could be conclusively reclassified as glioblastoma based on either TERT mutation, EGFR amplification, or both. On imaging, 65% showed gadolinium enhancement on MRI. Thirty-nine patients (72%) received long-course radiotherapy, of whom 64% received concurrent chemotherapy. The median follow-up of the group was 16 months (range: 2-90), and the median overall survival (OS) was 17.3 months. The 2-year OS of the whole cohort was 31%. On univariate analysis, older age, worse performance status (PS), and presence versus absence of contrast enhancement on diagnostic MRI were statistically significant for poorer OS.

CONCLUSION

IDH-WT WHO grade 2/3 astrocytomas are a heterogeneous group of tumors with poor clinical outcomes. The majority can be reclassified as glioblastoma, based on current WHO classification criteria, but further understanding of the underlying biology of these tumors and the discovery of novel targeted agents are needed for better outcomes.

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

Purpose

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

Material and methods

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

Results

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

Conclusions

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

Early MRI Predictors of Relapse in Primary Central Nervous System Lymphoma Treated with MATRix Immunochemotherapy

Primary Central Nervous System Lymphoma (PCNSL) is a highly malignant brain tumour. We investigated dynamic changes in tumour volume and apparent diffusion coefficient (ADC) measurements for predicting outcome following treatment with MATRix chemotherapy in PCNSL. Patients treated with MATRix (n = 38) underwent T1 contrast-enhanced (T1CE) and diffusion-weighted imaging (DWI) before treatment, after two cycles and after four cycles of chemotherapy. Response was assessed using the International PCNSL Collaborative Group (IPCG) imaging criteria. ADC histogram parameters and T1CE tumour volumes were compared among response groups, using one-way ANOVA testing. Logistic regression was performed to examine those imaging parameters predictive of response. Response after two cycles of chemotherapy differed from response after four cycles; of the six patients with progressive disease (PD) after four cycles of treatment, two (33%) had demonstrated a partial response (PR) or complete response (CR) after two cycles. ADC_mean at baseline, T1CE at baseline and T1CE percentage volume change differed between response groups (0.005 < p < 0.038) and were predictive of MATRix treatment response (area under the curve: 0.672-0.854). Baseline ADC and T1CE metrics are potential biomarkers for risk stratification of PCNSL patients early during remission induction therapy with MATRix. Standard interim response assessment (after two cycles) according to IPCG imaging criteria does not reliably predict early disease progression in the context of a conventional treatment approach.

Apparent Diffusion Coefficient in the Differentiation of Common Pediatric Brain Tumors in the Posterior Fossa: Different Region-of-Interest Selection Methods for Time Efficiency, Measurement Reproducibility, and Diagnostic Utility

OBJECTIVES

This study aimed to explore the diagnostic ability of apparent diffusion coefficient (ADC) values obtained from different region of interest (ROI) measurements in tumor parenchyma for differentiating posterior fossa tumors (PFTs) and the correlations between ADC values and Ki-67.

METHODS

Seventy-three pediatric patients with PFTs who underwent conventional diffusion-weighted imaging were recruited in this study. Five different ROIs were manually drawn by 2 radiologists (ROI-polygon, ROI-3 sections, ROI-3-5 ovals, ROI-more ovals, and ROI-whole). The interreader/intrareader repeatability, time required, diagnostic ability, and Ki-67 correlation analysis of the ADC values based on these ROI strategies were calculated.

RESULTS

Both interreader and intrareader reliabilities were excellent for ADC values among the different ROI strategies (intraclass correlation coefficient, 0.899-0.992). There were statistically significant differences in time consumption among the 5 ROI selection methods ( P < 0.001). The time required for the ROI-3-5 ovals was the shortest (32.23 ± 5.14 seconds), whereas the time required for the ROI-whole was the longest (204.52 ± 92.34 seconds). The diagnostic efficiency of the ADC values showed no significant differences among the different ROI measurements ( P > 0.05). The ADC value was negatively correlated with Ki-67 ( r = -0.745 to -0.798, all P < 0.0001).

CONCLUSIONS

The ROI-3-5 ovals method has the best interobserver repeatability, the shortest amount of time spent, and the best diagnostic ability. Thus, it is considered an effective measurement to produce ADC values in the evaluation of pediatric PFTs.

Noninvasively evaluating the grade and IDH mutation status of gliomas by using mono-exponential, bi-exponential diffusion-weighted imaging and three-dimensional pseudo-continuous arterial spin labeling

OBJECTIVES

To noninvasively assess the diagnostic performance of diffusion-weighted imaging (DWI), bi-exponential intravoxel incoherent motion imaging (IVIM) and three-dimensional pseudo-continuous arterial spin labeling (3D pCASL) in differentiating lower-grade gliomas (LGGs) from high-grade gliomas (HGGs), and predicting the isocitrate dehydrogenase (IDH) mutation status.

MATERIALS AND METHODS

Ninety-five patients with pathologically confirmed grade 2-4 gliomas with preoperative DWI, IVIM and 3D pCASL were enrolled in this study. The Student's t test and Mann-Whitney U test were used to evaluate differences in parameters of DWI, IVIM and 3D pCASL between LGG and HGG as well as between mutant and wild-type IDH in grade 2 and 3 diffusion astrocytoma; receiver operator characteristic (ROC) analysis was used to assess the diagnostic performance.

RESULTS

The value of ADC_mean, ADC_min, D_mean and D_min in HGGs were lower than in LGGs, while the value of CBF_mean and CBF_max in HGGs were higher than in LGGs. In ROC analysis, the AUC values of D_mean, D_min and CBF_max were 0.827, 0.878 and 0.839, respectively. The combination of CBF_max and D_min displayed the highest diagnostic performance to distinguish LGGs from HGGs, with AUC 0.906, sensitivity 82.4 %, and specificity 86.4 %. In grades 2 and 3 diffusion astrocytoma patients, ADC_min, D_mean, D_min, CBF_mean and CBF_max showed significant differences between IDH_mut and IDH_wt group (p < 0.05, 0.001, 0.001, 0.01 and 0.001, respectively) and the AUC values were 0. 709, 0.849, 0.919, 0.755 and 0.873, respectively. Similarly, the combination of CBF_max and D_min demonstrated the highest AUC value (0.938) in prediction IDH mutation status, with sensitivity 92.9 %, and specificity 95.5 %.

CONCLUSION

The combination of IVIM and 3D pCASL can be used in prediction histologic grade and IDH mutation status of glioma noninvasively.

Combining Multi-Shell Diffusion with Conventional MRI Improves Molecular Diagnosis of Diffuse Gliomas with Deep Learning

The WHO classification since 2016 confirms the importance of integrating molecular diagnosis for prognosis and treatment decisions of adult-type diffuse gliomas. This motivates the development of non-invasive diagnostic methods, in particular MRI, to predict molecular subtypes of gliomas before surgery. At present, this development has been focused on deep-learning (DL)-based predictive models, mainly with conventional MRI (cMRI), despite recent studies suggesting multi-shell diffusion MRI (dMRI) offers complementary information to cMRI for molecular subtyping. The aim of this work is to evaluate the potential benefit of combining cMRI and multi-shell dMRI in DL-based models. A model implemented with deep residual neural networks was chosen as an illustrative example. Using a dataset of 146 patients with gliomas (from grade 2 to 4), the model was trained and evaluated, with nested cross-validation, on pre-operative cMRI, multi-shell dMRI, and a combination of the two for the following classification tasks: (i) IDH-mutation; (ii) 1p/19q-codeletion; and (iii) three molecular subtypes according to WHO 2021. The results from a subset of 100 patients with lower grades gliomas (2 and 3 according to WHO 2016) demonstrated that combining cMRI and multi-shell dMRI enabled the best performance in predicting IDH mutation and 1p/19q codeletion, achieving an accuracy of 75 ± 9% in predicting the IDH-mutation status, higher than using cMRI and multi-shell dMRI separately (both 70 ± 7%). Similar findings were observed for predicting the 1p/19q-codeletion status, with the accuracy from combining cMRI and multi-shell dMRI (72 ± 4%) higher than from each modality used alone (cMRI: 65 ± 6%; multi-shell dMRI: 66 ± 9%). These findings remain when we considered all 146 patients for predicting the IDH status (combined: 81 ± 5% accuracy; cMRI: 74 ± 5%; multi-shell dMRI: 73 ± 6%) and for the diagnosis of the three molecular subtypes according to WHO 2021 (combined: 60 ± 5%; cMRI: 57 ± 8%; multi-shell dMRI: 56 ± 7%). Together, these findings suggest that combining cMRI and multi-shell dMRI can offer higher accuracy than using each modality alone for predicting the IDH and 1p/19q status and in diagnosing the three molecular subtypes with DL-based models.

ADC for Differentiation between Posttreatment Changes and Recurrence in Head and Neck Cancer: A Systematic Review and Meta-analysis

BACKGROUND

Previous studies reported that the ADC values of recurrent head and neck cancer lesions are lower than those of posttreatment changes, however, the utility of ADC to differentiate them has not been definitively summarized and established.

PURPOSE

Our aim was to evaluate the diagnostic benefit of ADC calculated from diffusion-weighted imaging in differentiating recurrent lesions from posttreatment changes in head and neck cancer.

DATA SOURCES

MEDLINE, Scopus, and EMBASE data bases were searched for studies.

STUDY SELECTION

The review identified 6 prospective studies with a total of 365 patients (402 lesions) who were eligible for the meta-analysis.

DATA ANALYSIS

Forest plots were used to assess the mean difference in ADC values. Heterogeneity among the studies was evaluated using the Cochrane Q test and the I² statistic.

DATA SYNTHESIS

Among included studies, the overall mean of ADC values of recurrent lesions was 1.03 × 10^-3mm²/s and that of the posttreatment changes was 1.51 × 10^-3mm²/s. The ADC value of recurrence was significantly less than that of posttreatment changes in head and neck cancer (pooled mean difference: -0.45; 95% CI, -0.59-0.32, P < .0001) with heterogeneity among studies. The threshold of ADC values between recurrent lesions and posttreatment changes was suggested to be 1.10 × 10^-3mm²/s.

LIMITATIONS

Given the heterogeneity of the data of the study, the conclusions should be interpreted with caution.

CONCLUSIONS

The ADC values in recurrent head and neck cancers are lower than those of posttreatment changes, and the threshold of ADC values between them was suggested.

Filtration-Histogram Based Magnetic Resonance Texture Analysis (MRTA) for the Distinction of Primary Central Nervous System Lymphoma and Glioblastoma

Primary central nervous system lymphoma (PCNSL) has variable imaging appearances, which overlap with those of glioblastoma (GBM), thereby necessitating invasive tissue diagnosis. We aimed to investigate whether a rapid filtration histogram analysis of clinical MRI data supports the distinction of PCNSL from GBM. Ninety tumours (PCNSL n = 48, GBM n = 42) were analysed using pre-treatment MRI sequences (T₁-weighted contrast-enhanced (T₁CE), T₂-weighted (T₂), and apparent diffusion coefficient maps (ADC)). The segmentations were completed with proprietary texture analysis software (TexRAD version 3.3). Filtered (five filter sizes SSF = 2-6 mm) and unfiltered (SSF = 0) histogram parameters were compared using Mann-Whitney U non-parametric testing, with receiver operating characteristic (ROC) derived area under the curve (AUC) analysis for significant results. Across all (n = 90) tumours, the optimal algorithm performance was achieved using an unfiltered ADC mean and the mean of positive pixels (MPP), with a sensitivity of 83.8%, specificity of 8.9%, and AUC of 0.88. For subgroup analysis with >1/3 necrosis masses, ADC permitted the identification of PCNSL with a sensitivity of 96.9% and specificity of 100%. For T₁CE-derived regions, the distinction was less accurate, with a sensitivity of 71.4%, specificity of 77.1%, and AUC of 0.779. A role may exist for cross-sectional texture analysis without complex machine learning models to differentiate PCNSL from GBM. ADC appears the most suitable sequence, especially for necrotic lesion distinction.