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12.10.2018 | Neuro

Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour

verfasst von: Changliang Su, Jingjing Jiang, Shun Zhang, Jingjing Shi, Kaibin Xu, Nanxi Shen, Jiaxuan Zhang, Li Li, Lingyun Zhao, Ju Zhang, Yuanyuan Qin, Yong Liu, Wenzhen Zhu

Erschienen in: European Radiology | Ausgabe 4/2019

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Abstract

Purpose

To explore the feasibility and diagnostic performance of radiomics based on anatomical, diffusion and perfusion MRI in differentiating among glioma subtypes and predicting tumour proliferation.

Methods

220 pathology-confirmed gliomas and ten contrasts were included in the retrospective analysis. After being registered to T2FLAIR images and resampling to 1 mm³ isotropically, 431 radiomics features were extracted from each contrast map within a semi-automatic defined tumour volume. For single-contrast and the combination of all contrasts, correlations between the radiomics features and pathological biomarkers were revealed by partial correlation analysis, and multivariate models were built to identify the best predictive models with adjusted 0.632+ bootstrap AUC.

Results

In univariate analysis, both non-wavelet and wavelet radiomics features were correlated significantly with tumour grade and the Ki-67 labelling index. The max R was 0.557 (p = 2.04E-14) in T₁C for tumour grade and 0.395 (p = 2.33E-07) in ADC for Ki-67. In the multivariate analysis, the combination of all-contrast radiomics features had the highest AUCs in both differentiating among glioma subtypes and predicting proliferation compared with those in single-contrast images. For low-/high-grade gliomas, the best AUC was 0.911. In differentiating among glioma subtypes, the best AUC was 0.896 for grades II–III, 0.997 for grades II–IV, and 0.881 for grades III–IV. In predicting proliferation levels, multicontrast features led to an AUC of 0.936.

Conclusion

Multicontrast radiomics supplies complementary information on both geometric characters and molecular biological traits, which correlated significantly with tumour grade and proliferation. Combining all-contrast radiomics models might precisely predict glioma biological behaviour, which may be attributed to presurgical personal diagnosis.

Key Points

• Multicontrast MRI radiomics features are significantly correlated with tumour grade and Ki-67 LI.

• Multimodality MRI provides independent but supplemental information in assessing glioma pathological behaviour.

• Combined multicontrast MRI radiomics can precisely predict glioma subtypes and proliferation levels.

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Titel: Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour
verfasst von: Changliang Su
Jingjing Jiang
Shun Zhang
Jingjing Shi
Kaibin Xu
Nanxi Shen
Jiaxuan Zhang
Li Li
Lingyun Zhao
Ju Zhang
Yuanyuan Qin
Yong Liu
Wenzhen Zhu
Publikationsdatum: 12.10.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 4/2019
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-018-5704-8

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Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour