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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

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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.
Author(s)
김남국김호성박지은윤지혜이현나함성원Seo Young ParkSeung Hong Choi
Issued Date
2021
Type
Article
Keyword
RadiomicsDeep learningNeuro-oncologySegmentationReproducibility(2020A02756)
DOI
10.1007/s00330-020-07414-3
URI
https://oak.ulsan.ac.kr/handle/2021.oak/8923
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_proquest_miscellaneous_2456415634&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Diffusion%20and%20perfusion%20MRI%20radiomics%20obtained%20from%20deep%20learning%20segmentation%20provides%20reproducible%20and%20comparable%20diagnostic%20model%20to%20human%20in%20post-treatment%20glioblastoma&offset=0&pcAvailability=true
Publisher
EUROPEAN RADIOLOGY
Location
독일
Language
영어
ISSN
0938-7994
Citation Volume
31
Citation Number
5
Citation Start Page
3127
Citation End Page
3137
Appears in Collections:
Engineering > Medical Engineering
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