Brain T1ρ mapping for grading and IDH1 gene mutation detection of gliomas: a preliminary study

Quantifying H&E staining results, grading and predicting IDH mutation status of gliomas using hybrid multi-dimensional MRI

OBJECTIVE

To assess the performance of hybrid multi-dimensional magnetic resonance imaging (HM-MRI) in quantifying hematoxylin and eosin (H&E) staining results, grading and predicting isocitrate dehydrogenase (IDH) mutation status of gliomas.

MATERIALS AND METHODS

Included were 71 glioma patients (mean age, 50.17 ± 13.38 years; 35 men). HM-MRI images were collected at five different echo times (80-200 ms) with seven b-values (0-3000 s/mm2). A modified three-compartment model with very-slow, slow and fast diffusion components was applied to calculate HM-MRI metrics, including fractions, diffusion coefficients and T2 values of each component. Pearson correlation analysis was performed between HM-MRI derived fractions and H&E staining derived percentages. HM-MRI metrics were compared between high-grade and low-grade gliomas, and between IDH-wild and IDH-mutant gliomas. Using receiver operational characteristic (ROC) analysis, the diagnostic performance of HM-MRI in grading and genotyping was compared with mono-exponential models.

RESULTS

HM-MRI metrics FDvery-slow and FDslow demonstrated a significant correlation with the H&E staining results (p < .05). Besides, FDvery-slow showed the highest area under ROC curve (AUC = 0.854) for grading, while Dslow showed the highest AUC (0.845) for genotyping. Furthermore, a combination of HM-MRI metrics FDvery-slow and T2Dslow improved the diagnostic performance for grading (AUC = 0.876).

DISCUSSION

HM-MRI can aid in non-invasive diagnosis of gliomas.

Adding quantitative T1rho-weighted imaging to conventional MRI improves specificity and sensitivity for differentiating malignant from benign breast lesions

OBJECTIVES

To investigate the feasibility of T1rho-weighted imaging in differentiating malignant from benign breast lesions and to explore the additional value of T1rho to conventional MRI.

MATERIALS AND METHODS

We prospectively enrolled consecutive women with breast lesions who underwent preoperative T1rho-weighted imaging, diffusion-weighted imaging, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) between November 2021 and July 2023. The T1rho, apparent diffusion coefficient (ADC), and semi-quantitative parameters from DCE-MRI were obtained and compared between benign and malignant groups. The diagnostic performance was analyzed and compared using receiver operating characteristic (ROC) curves and the Delong Test.

RESULTS

This study included 113 patients (74 malignant and 39 benign lesions). The mean T1rho value in the benign group (92.61 ± 22.10 ms) was significantly higher than that in the malignant group (72.18 ± 16.37 ms) (P < 0.001). The ADC value and time to peak (TTP) value in the malignant group (1.13 ± 0.45 and 269.06 ± 106.01, respectively) were lower than those in the benign group (1.57 ± 0.45 and 388.30 ± 81.13, respectively) (all P < 0.001). T1rho combined with ADC and TTP showed good diagnostic performance with an area under the curve (AUC) of 0.896, a sensitivity of 81.0%, and a specificity of 87.1%. The specificity and sensitivity of the combination of T1rho, ADC, and TTP were significantly higher than those of the combination of ADC and TTP (87.1% vs. 84.6%, P < 0.005; 81.0% vs. 77.0%, P < 0.001).

CONCLUSION

T1rho-weighted imaging was a feasible MRI sequence for differentiating malignant from benign breast lesions. The combination of T1rho, ADC and TTP could achieve a favorable diagnostic performance with improved specificity and sensitivity, T1rho could serve as a supplementary approach to conventional MRI.

Multiparametric study for glioma grading with FLAIR, ADC map, eADC map, T1 map, and SWI images

PURPOSE

This paper is a preliminary attempt to compare the diagnostic efficiency and performance of Fluid-attenuated inversion recovery (FLAIR), apparent diffusion coefficient (ADC) map, exponential ADC (eADC) map, T₁ map, and Susceptibility-weighted image (SWI) for glioma grading and combine these image data pairs to compare the diagnostic performance of different image data pairs for glioma grading.

MATERIAL AND METHODS

Fifty-nine patients underwent FLAIR, ADC map, eADC map, Variable flip-angle (VFA) spoiled gradient recalled echo (SPGR) method, and SWI MRI imaging. The T₁ map was reconstructed by the VFA-SPGR method. The average Relative Signal Contrast (RSC) and receiver operating characteristic curve (ROC) was calculated in a different image. The multivariate binary logistic regression model combined different image data pairs.

RESULTS

The average RSC of SWI and ADC maps in high-grade glioma is significantly lower than RSCs in low-grade. The average RSC of the eADC map and T₁ maps increased with glioma grade. No significant difference was detected between low and high-grade glioma on FLAIR images. The AUC for low and high-grade glioma differentiation on ADC maps, eADC maps, T₁ map, and SWI were calculated 0.781, 0.864, 0.942, and 0.904, respectively. Also, by adding different image data, diagnostic performance was increased.

CONCLUSION

Interestingly, the T₁ map and SWI image have the potential to use in the clinic for glioma grading purposes due to their high performance. Also, the eADC map+T₁ map and T₁ map+SWI image weights have the highest diagnostic performance for glioma grading.

The Prognostic Role of Peritumoral Edema in Patients with Newly Diagnosed Glioblastoma: A Retrospective Analysis

OBJECTIVE

Previous studies on glioblastomas (GBMs) have not reached a consensus on peritumoral edema (PTE)'s influence on survival. This study evaluated the PTE index's prognostic role in newly diagnosed GBMs using a well-designed method.

METHODS

Selected patients were reviewed after a rigorous screening process. Their general information was obtained from electronic medical records. The imaging metrics (MTD, TTM, TTE) representing tumor diameter, laterality, and PTE extent were obtained by manual measurement in Syngo FastView software. The PTE index was a ratio of TTE to MTD. Multiple variables were evaluated using analysis of variance and Cox regression model.

RESULTS

Of 143 patients, 62 were included in this study. MGMT promoter methylation and tumor laterality were both independent prognostic factors (p = 0.020, 0.042; HR = 0.272, 2.630). The lateral tumors' index was higher than that of the medial tumors (57.7% vs. 42.6%, p = 0.027). Low-index tumors were located in relatively medial positions compared with high-index tumors (TTM, 4.9 vs. 12.8, p = 0.032). This finding indicated that the PTE index tended to increase with tumor laterality. Moreover, the patients with low-index tumors had a significant survival disadvantage in the univariate analysis but not in the multivariate analysis (p = 0.023, 0.220). However, further analysis found that the combination of tumor laterality and PTE statistically stratified the survival outcome. The patients with lateral high-index tumors survived significantly longer (p = 0.022, HR = 1.927).

CONCLUSIONS

In contrast with the previous studies, this study recommends combining PTE and tumor laterality for survival stratification in newly diagnosed GBMs.

Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach

Purpose

Preoperative prediction of isocitrate dehydrogenase 1 (IDH1) mutation in lower-grade gliomas (LGGs) is crucial for clinical decision-making. This study aimed to examine the predictive value of a machine learning approach using qualitative and quantitative MRI features to identify the IDH1 mutation in LGGs.

Materials and Methods

A total of 102 LGG patients were allocated to training (n = 67) and validation (n = 35) cohorts and were subject to Visually Accessible Rembrandt Images (VASARI) feature extraction (23 features) from conventional multimodal MRI and radiomics feature extraction (56 features) from apparent diffusion coefficient maps. Feature selection was conducted using the maximum Relevance Minimum Redundancy method and 0.632+ bootstrap method. A machine learning model to predict IDH1 mutation was then established using a random forest classifier. The predictive performance was evaluated using receiver operating characteristic (ROC) curves.

Results

After feature selection, the top 5 VASARI features were enhancement quality, deep white matter invasion, tumor location, proportion of necrosis, and T1/FLAIR ratio, and the top 10 radiomics features included 3 histogram features, 3 gray-level run-length matrix features, and 3 gray-level size zone matrix features and one shape feature. Using the optimal VASARI or radiomics feature sets for IDH1 prediction, the trained model achieved an area under the ROC curve (AUC) of 0.779 ± 0.001 or 0.849 ± 0.008 on the validation cohort, respectively. The fusion model that integrated outputs of both optimal VASARI and radiomics models improved the AUC to 0.879.

Conclusion

The proposed machine learning approach using VASARI and radiomics features can predict IDH1 mutation in LGGs.

Quantitative T1ρ MRI of the Head and Neck Discriminates Carcinoma and Benign Hyperplasia in the Nasopharynx

BACKGROUND AND PURPOSE

T1ρ imaging is a new quantitative MR imaging pulse sequence with the potential to discriminate between malignant and benign tissue. In this study, we evaluated the capability of T1ρ imaging to characterize tissue by applying T1ρ imaging to malignant and benign tissue in the nasopharynx and to normal tissue in the head and neck.

MATERIALS AND METHODS

Participants with undifferentiated nasopharyngeal carcinoma and benign hyperplasia of the nasopharynx prospectively underwent T1ρ imaging. T1ρ measurements obtained from the histogram analysis for nasopharyngeal carcinoma in 43 participants were compared with those for benign hyperplasia and for normal tissue (brain, muscle, and parotid glands) in 41 participants using the Mann-Whitney U test. The area under the curve of significant T1ρ measurements was calculated and compared using receiver operating characteristic analysis and the Delong test, respectively. A P < . 05 indicated statistical significance.

RESULTS

There were significant differences in T1ρ measurements between nasopharyngeal carcinoma and benign hyperplasia and between nasopharyngeal carcinoma and normal tissue (all, P < . 05). Compared with benign hyperplasia, nasopharyngeal carcinoma showed a lower T1ρ mean (62.14 versus 65.45 × ms), SD (12.60 versus 17.73 × ms), and skewness (0.61 versus 0.76) (all P < .05), but no difference in kurtosis (P = . 18). The T1ρ SD showed the highest area under the curve of 0.95 compared with the T1ρ mean (area under the curve  = 0.72) and T1ρ skewness (area under the curve  = 0.72) for discriminating nasopharyngeal carcinoma and benign hyperplasia (all, P < .05).

CONCLUSIONS

Quantitative T1ρ imaging has the potential to discriminate malignant from benign and normal tissue in the head and neck.

Three-Dimensional GRE T1ρ mapping of the brain using tailored variable flip-angle scheduling

PURPOSE

To introduce a new approach called tailored variable flip-angle (VFA) scheduling for SNR-efficient 3D T_1ρ mapping of the brain using a magnetization-prepared gradient-echo sequence.

METHODS

Simulations were used to assess the relative SNR efficiency, quantitative accuracy, and spatial blurring of tailored VFA scheduling for T_1ρ mapping of brain tissue compared with magnetization-prepared angle-modulated partitioned k-space spoiled gradient-echo snapshots (MAPSS), a state-of-the-art technique for accurate 3D gradient-echo T_1ρ mapping. Simulations were also used to calculate optimal imaging parameters for tailored VFA scheduling versus MAPSS, without and with nulling of CSF. Four participants were imaged at 3T MRI to demonstrate the feasibility of tailored VFA scheduling for T_1ρ mapping of the brain. Using MAPSS as a reference standard, in vivo data were used to validate the relative SNR efficiency and quantitative accuracy of the new approach.

RESULTS

Tailored VFA scheduling can provide a 2-fold to 4-fold gain in the SNR of the resulting T_1ρ map as compared with MAPSS when using identical sequence parameters while limiting T_1ρ quantification errors to 2% or less. In vivo whole-brain 3D T_1ρ maps acquired with tailored VFA scheduling had superior SNR efficiency than is achievable with MAPSS, and the SNR efficiency improved with a greater number of views per segment.

CONCLUSIONS

Tailored VFA scheduling is an SNR-efficient GRE technique for 3D T_1ρ mapping of the brain that provides increased flexibility in choice of imaging parameters compared with MAPSS, which may benefit a variety of applications.