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10.06.2024 | Original Article

Radiomics Analysis of Quantitative Maps from Synthetic MRI for Predicting Grades and Molecular Subtypes of Diffuse Gliomas

verfasst von: Danlin Lin, Jiehong Liu, Chao Ke, Haolin Chen, Jing Li, Yuanyao Xie, Jianhua Ma, Xiaofei Lv, Yanqiu Feng

Erschienen in: Clinical Neuroradiology | Ausgabe 4/2024

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Abstract

Purpose

To investigate the feasibility of using radiomics analysis of quantitative maps from synthetic MRI to preoperatively predict diffuse glioma grades, isocitrate dehydrogenase (IDH) subtypes, and 1p/19q codeletion status.

Methods

Data from 124 patients with diffuse glioma were used for analysis (n = 87 for training, n = 37 for testing). Quantitative T1, T2, and proton density (PD) maps were obtained using synthetic MRI. Enhancing tumour (ET), non-enhancing tumour and necrosis (NET), and peritumoral edema (PE) regions were segmented followed by manual fine-tuning. Features were extracted using PyRadiomics and then selected using Levene/T, BorutaShap and maximum relevance minimum redundancy algorithms. A support vector machine was adopted for classification. Receiver operating characteristic curve analysis and integrated discrimination improvement analysis were implemented to compare the performance of different radiomics models.

Results

Radiomics models constructed using features from multiple tumour subregions (ET + NET + PE) in the combined maps (T1 + T2 + PD) achieved the highest AUC in all three prediction tasks, among which the AUC for differentiating lower-grade and high-grade diffuse gliomas, predicting IDH mutation status and predicting 1p/19q codeletion status were 0.92, 0.95 and 0.86 respectively. Compared with those constructed on individual T1, T2, and PD maps, the discriminant ability of radiomics models constructed on the combined maps separately increased by 11, 17 and 10% in predicting glioma grades, 35, 52 and 19% in predicting IDH mutation status, and 16, 15 and 14% in predicting 1p/19q codeletion status (p < 0.05).

Conclusion

Radiomics analysis of quantitative maps from synthetic MRI provides a new quantitative imaging tool for the preoperative prediction of grades and molecular subtypes in diffuse gliomas.
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Metadaten
Titel
Radiomics Analysis of Quantitative Maps from Synthetic MRI for Predicting Grades and Molecular Subtypes of Diffuse Gliomas
verfasst von
Danlin Lin
Jiehong Liu
Chao Ke
Haolin Chen
Jing Li
Yuanyao Xie
Jianhua Ma
Xiaofei Lv
Yanqiu Feng
Publikationsdatum
10.06.2024
Verlag
Springer Berlin Heidelberg
Erschienen in
Clinical Neuroradiology / Ausgabe 4/2024
Print ISSN: 1869-1439
Elektronische ISSN: 1869-1447
DOI
https://doi.org/10.1007/s00062-024-01421-3

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