Whole-tumor histogram analysis of diffusion and perfusion metrics for noninvasive pediatric glioma grading

Su, Yan; Kang, Jie; Lin, Xiang; She, Dejun; Guo, Wei; Zhou, Xing; Yang, Xiefeng; Cao, Dairong

doi:10.1007/s00234-023-03145-6

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…On the other hand, in recent years, there have been significant advancements in image postprocessing techniques that offer a wealth of additional information extracted from conventional T2-weighted, FLAIR, pre-and postcontrast T1-weighted sequences [18]. These radiomic-based methods showed high precision for the prediction of tumor grade, IDH mutation, and 1p19q codeletion status in adults [12], [13] as well as tumor grade [19] and BRAF mutation status [20] in children. However, the signal changes on most conventional MRI sequences lack biological specificity, which limits the accuracy of noninvasive glioma characterization.…”

Section: Introductionmentioning

confidence: 99%

Utilizing the Amide Proton Transfer Technique to Characterize Diffuse Gliomas Based on the WHO 2021 Classification of CNS Tumors

Filimonova,

Pashkov,

Borisov

et al. 2023

Preprint

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Purpose Diffuse gliomas present a significant challenge for healthcare systems globally. While brain MRI plays a vital role in diagnosis, prognosis, and treatment monitoring, accurately characterizing gliomas using conventional MRI techniques alone is challenging. In this study, we explored the potential of utilizing the amide proton transfer (APT) technique alone or in combination with other quantitative MRI sequences to predict tumor grade and type based on the WHO 2021 Classification of CNS Tumors. Methods Forty-two adult patients with histopathologically confirmed brain gliomas were included in the study. They underwent 3T MRI imaging, which involved APT, arterial spin labeling (ASL), and diffusion-weighted imaging sequences. Multinomial and binary logistic regression models were employed to classify patients into clinically relevant groups based on MRI findings and demographic variables. Results We found that the best model for tumor grade classification included patient age along with APT values. The highest sensitivity (88%) was observed for Grade 4 tumors, while Grade 3 tumors showed the highest specificity (79%). For tumor type classification, our model incorporated four predictors: APT values, necrosis, and the presence of hemorrhage. The glioblastoma group had the highest sensitivity and specificity (87%), whereas balanced accuracy was the lowest for astrocytomas, indicating that the model performs better at detecting patients with glioblastoma rather than astrocytomas. Conclusion The APT technique shows great potential for noninvasive evaluation of diffuse gliomas. The changes in the classification of gliomas as per the WHO 2021 version of the CNS Tumor Classification did not affect its usefulness in predicting tumor grade or type.

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Section: Introductionmentioning

confidence: 99%

Utilizing the Amide Proton Transfer Technique to Characterize Diffuse Gliomas Based on the WHO 2021 Classification of CNS Tumors

Filimonova,

Pashkov,

Borisov

et al. 2023

Preprint

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Whole-tumor histogram analysis of postcontrast T1-weighted and apparent diffusion coefficient in predicting the grade and proliferative activity of adult intracranial ependymomas

Liu,

Han,

Wang

et al. 2024

Neuroradiology

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The impact of diffusion and perfusion-weighted imaging on glioma grading

Dinh Hieu,

Duy Hung,

Thanh Dung

et al. 2024

Oncologie

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Objectives Determining the grade of a glioma is extremely important for treatment planning and prognosis prediction. The study aimed to evaluate the usefulness of multiparametric perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI) in preoperative glioma grading. Methods In this retrospective study, 63 individuals with brain tumors histologically confirmed, of which 23 had low-grade gliomas (LGGs) and 40 had high-grade gliomas (HGGs) were involved. We conducted this paper on apparent diffusion coefficient (ADC) maps using the entire tumor volume method, allowing us to use all ADC values of the tumor. Small-sample regions of interest (ROIs) were drawn to collect parameters of relative cerebral blood flow (rCBF), cerebral blood flow (CBF), and relative cerebral blood volume (rCBV), from both the tumor core and peritumoral edema. The PWI and DWI metrics were compared to identify the most accurate distinguishing HGGs and LGGs, analyze receiver operating characteristics (ROC), and evaluate the diagnostic performance using solitary parameters and combined. Results In diffusion MRI, there were significant differences in minimum ADC and mean ADC between LGGs and HGGs (p<0.05), with the larger area under the curve (AUC) of 0.898 found for mean ADC at a cut-off value of 1.275, with sensitivity of 82.6 % and specificity of 90 %. The maximum ADC value did not differ significantly (p>0.05). All perfusion parameters in both the tumor core and peritumoral edema area were significantly greater values in cases of HGG compared to LGG (p<0.001), with the highest AUC of 0.946 found for solid tumor rCBV value (rCBVt), the cut-off is 3.585, sensitivity of 85 % and specificity of 100 %. Combining mean ADC and rCBVt provided an excellent AUC of 0.975, a sensitivity of 92.5 %, and a specificity of 91.3 % for differentiating between HGGs and LGGs. Conclusions Perfusion and diffusion MRI are valuable in discriminating between high-grade and low-grade gliomas, with the major criterion in the decision-making process being the combined mean ADC and rCBVt parameters.

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Whole-tumor histogram analysis of diffusion and perfusion metrics for noninvasive pediatric glioma grading

Cited by 3 publications

References 28 publications

Utilizing the Amide Proton Transfer Technique to Characterize Diffuse Gliomas Based on the WHO 2021 Classification of CNS Tumors

Utilizing the Amide Proton Transfer Technique to Characterize Diffuse Gliomas Based on the WHO 2021 Classification of CNS Tumors

Whole-tumor histogram analysis of postcontrast T1-weighted and apparent diffusion coefficient in predicting the grade and proliferative activity of adult intracranial ependymomas

The impact of diffusion and perfusion-weighted imaging on glioma grading

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