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European Radiology

Erschienen in:

31.10.2020 | Magnetic Resonance

Diffusion and perfusion MRI radiomics obtained from deep learning segmentation provides reproducible and comparable diagnostic model to human in post-treatment glioblastoma

verfasst von: Ji Eun Park, Sungwon Ham, Ho Sung Kim, Seo Young Park, Jihye Yun, Hyunna Lee, Seung Hong Choi, Namkug Kim

Erschienen in: European Radiology | Ausgabe 5/2021

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Abstract

Objectives

Deep learning-based automatic segmentation (DLAS) helps the reproducibility of radiomics features, but its effect on radiomics modeling is unknown. We therefore evaluated whether DLAS can robustly extract anatomical and physiological MRI features, thereby assisting in the accurate assessment of treatment response in glioblastoma patients.

Methods

A DLAS model was trained on 238 glioblastomas and validated on an independent set of 98 pre- and 86 post-treatment glioblastomas from two tertiary hospitals. A total of 1618 radiomics features from contrast-enhanced T1-weighted images (CE-T1w) and histogram features from apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) mapping were extracted. The diagnostic performance of radiomics features and ADC and CBV parameters for identifying treatment response was tested using area under the curve (AUC) from receiver operating characteristics analysis. Feature reproducibility was tested using a 0.80 cutoff for concordance correlation coefficients.

Results

Reproducibility was excellent for ADC and CBV features (ICC, 0.82–0.99) and first-order features (pre- and post-treatment, 100% and 94.1% remained), but lower for texture (79.0% and 69.1% remained) and wavelet-transformed (81.8% and 74.9% remained) features of CE-T1w. DLAS-based radiomics showed similar performance to human-performed segmentations in internal validation (AUC, 0.81 [95% CI, 0.64–0.99] vs. AUC, 0.81 [0.60–1.00], p = 0.80), but slightly lower performance in external validation (AUC, 0.78 [0.61–0.95] vs. AUC, 0.65 [0.46–0.84], p = 0.23).

Conclusion

DLAS-based feature extraction showed high reproducibility for first-order features from anatomical and physiological MRI, and comparable diagnostic performance to human manual segmentations in the identification of pseudoprogression, supporting the utility of DLAS in quantitative MRI analysis.

Key Points

• Deep learning-based automatic segmentation (DLAS) enables fast and robust feature extraction from diffusion- and perfusion-weighted MRI.

• DLAS showed high reproducibility in first-order feature extraction from anatomical, diffusion, and perfusion MRI across two centers.

• DLAS-based radiomics features showed comparable diagnostic accuracy to manual segmentations in post-treatment glioblastoma.

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Titel: Diffusion and perfusion MRI radiomics obtained from deep learning segmentation provides reproducible and comparable diagnostic model to human in post-treatment glioblastoma
verfasst von: Ji Eun Park
Sungwon Ham
Ho Sung Kim
Seo Young Park
Jihye Yun
Hyunna Lee
Seung Hong Choi
Namkug Kim
Publikationsdatum: 31.10.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 5/2021
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-07414-3

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Diffusion and perfusion MRI radiomics obtained from deep learning segmentation provides reproducible and comparable diagnostic model to human in post-treatment glioblastoma