Diffusion and perfusion MRI radiomics obtained from deep learning segmentation provides reproducible and comparable diag
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MAGNETIC RESONANCE
Diffusion and perfusion MRI radiomics obtained from deep learning segmentation provides reproducible and comparable diagnostic model to human in post-treatment glioblastoma Ji Eun Park 1 & Sungwon Ham 2 & Ho Sung Kim 1 Namkug Kim 1,2
&
Seo Young Park 3 & Jihye Yun 2 & Hyunna Lee 4 & Seung Hong Choi 5 &
Received: 7 July 2020 / Revised: 25 August 2020 / Accepted: 13 October 2020 # European Society of Radiology 2020
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 (CET1w) 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 posttreatment, 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 perfusionweighted MRI. • DLAS showed high reproducibility in first-order feature extraction from anatomical, diffusion, and perfusion MRI across two centers.
Ji Eun Park and Sungwon Ham contributed equally to this work. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-07414-3) contains supplementary material, which is available to authorized users. * Ho Sung Kim [email protected] 1
2
Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu,
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