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Deep learning-based detection and identification of brain tumor biomarkers in quantitative MR-images
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7582-1706
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-0555-8877
Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine.ORCID iD: 0000-0002-8857-5698
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-7061-7995
2023 (English)In: Machine Learning: Science and Technology, E-ISSN 2632-2153, Vol. 4, no 3, article id 035038Article in journal (Refereed) Published
Abstract [en]

The infiltrative nature of malignant gliomas results in active tumor spreading into the peritumoral edema, which is not visible in conventional magnetic resonance imaging (cMRI) even after contrast injection. MR relaxometry (qMRI) measures relaxation rates dependent on tissue properties, and can offer additional contrast mechanisms to highlight the non-enhancing infiltrative tumor. To investigate if qMRI data provides additional information compared to cMRI sequences when considering deep learning-based brain tumor detection and segmentation, preoperative conventional (T1-w per- and post-contrast, T2-w and FLAIR) and quantitative (pre- and post-contrast R1, R2 and proton density) MR data was obtained from 23 patients with typical radiological findings suggestive of a high-grade malignant glioma. 2D deep learning models were trained on transversal slices (n=528) for tumor detection and segmentation using either conventional or quantitative data. Moreover, trends in quantitative R1 and R2 rates of regions identified as relevant for tumor detection by model explainability methods were qualitatively analyzed. Tumor detection and segmentation performance for models trained with a combination of qMRI pre- and post-contrast was the highest (detection MCC=0.72, segmentation DSC=0.90), however, the difference compared to cMRI was not statistically significant. Overall analysis of the relevant regions identified using model explainability showed no differences between models trained on cMRI or qMRI. When looking at the individual cases, relaxation rates of brain regions outside the annotation and identified as relevant for tumor detection exhibited changes after contrast injection similar to region inside the annotation in the majority of cases. In conclusion, models trained on qMRI data obtained similar detection and segmentation performance to those trained on cMRI data, with the advantage of quantitatively measuring brain tissue properties within similar scan time. When considering individual patients, the analysis of relaxation rates of regions identified by model explainability suggests the presence of infiltrative tumor outside the tumor cMRI-based annotation.

Place, publisher, year, edition, pages
IOP Publishing Ltd , 2023. Vol. 4, no 3, article id 035038
Keywords [en]
quantitative MRI, brain tumor, deep learning, model explainability, cancer
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-196603DOI: 10.1088/2632-2153/acf095ISI: 001058164800001OAI: oai:DiVA.org:liu-196603DiVA, id: diva2:1788219
Funder
Swedish Research Council, 2018-05250Vinnova, ASSISTVinnova, IMPACTÅke Wiberg Foundation, M22-0088Medical Research Council of Southeast Sweden (FORSS), FORSS-234551Linköpings universitet, LiU Cancer Strength Area 2021
Note

Funding: CENIIT at Linkoeping University, ITEA3 / VINNOVA funded project Intelligence based iMprovement of Personalized treatment And Clinical workflow supporT (IMPACT); ITEA4 / VINNOVA funded project Automation, Surgery Support and Intuitive 3D visualization to optimize workflow in IGT SysTems (ASSIST) [2021-01954]; Cancer Strength Area at Linkoeping University, VINOVA project via the Analytic Imaging Diagnostics Arena (AIDA) [2017-02447]; Medical Research Council of Southeast Sweden [FORSS-234551]; Swedish Research Council [2018-05250]

Available from: 2023-08-16 Created: 2023-08-16 Last updated: 2023-09-21

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Tampu, Iulian EmilHaj-Hosseini, NedaBlystad, Ida

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Tampu, Iulian EmilHaj-Hosseini, NedaBlystad, IdaEklund, Anders
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Division of Biomedical EngineeringCenter for Medical Image Science and Visualization (CMIV)Faculty of Science & EngineeringFaculty of Medicine and Health SciencesDepartment of Radiology in LinköpingDivision of Diagnostics and Specialist MedicineThe Division of Statistics and Machine Learning
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Machine Learning: Science and Technology
Medical Image Processing

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