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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 Imaging
Identifiers
URN: urn:nbn:se:liu:diva-196603DOI: 10.1088/2632-2153/acf095ISI: 001058164800001Scopus ID: 2-s2.0-85170823259OAI: 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: 2025-02-19
In thesis
1. Deep learning for medical image analysis in cancer diagnosis
Open this publication in new window or tab >>Deep learning for medical image analysis in cancer diagnosis
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Medical imaging is one of the cornerstones of clinical diagnosis, providing insights into the anatomy and physiology of organs and tissues for screening, initial diagnosis, treatment planning, and follow-up. Utilizing both invasive and non-invasive techniques, medical imaging employs various contrast mechanisms to capture details of the tissue structure and the functionality of biological systems at different spatial and temporal resolutions, and dimensionalities. The ever-growing volume of medical image data driven by screening programs, digitalization, and the push towards precision medicine has highlighted the need for automatic image analysis methods to reduce the workload of healthcare personnel in reviewing these images.

Deep learning (DL), a subset of artificial intelligence (AI), comprises of methods that learn representations from data to perform various predictive tasks. Although DL was introduced in the mid-1960s, it has only been successfully applied for computer vision tasks in the past two decades, becoming the standard method for natural image processing. Additionally, the versatility of DL in processing data from diverse sources (such as speech, text, and climate) has encouraged its application in the medical domain as well.

This thesis explores the application of DL-based methods for medical image analysis, focusing on cancer diagnosis at various treatment planning stages, including preoperative, intraoperative, and postoperative procedures. Methods were developed and applied to three medical imaging modalities: optical coherence tomography (OCT) for intraoperative diagnosis, magnetic resonance imaging (MRI) for pre-operative diagnosis and radiotherapy treatment planning, and histopathology whole-slide images (WSI) for postoperative final diagnosis, addressing tasks such as detection, semantic segmentation, and classification for thyroid diseases and pediatric and adult brain tumors.

In summary, the outcomes of this thesis highlight the potential of deep learning-based methods for medical image analysis in the context of cancer diagnosis. These works demonstrate the versatility of deep learning in processing medical images from various sources and at different spatial resolutions and dimensionalities. Appropriate dataset curation, method validation and interpretation, and translational research are needed to promote the integration of deep learning-powered tools in the clinic.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2024. p. 78
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2408
Keywords
Medical imaging, Cancer diagnosis, Deep learning
National Category
Medical Imaging
Identifiers
urn:nbn:se:liu:diva-208602 (URN)10.3384/9789180757805 (DOI)9789180757799 (ISBN)9789180757805 (ISBN)
Public defence
2024-11-27, Belladonna, Building 511, Campus US, Linköping, 09:00 (English)
Opponent
Supervisors
Available from: 2024-10-17 Created: 2024-10-17 Last updated: 2025-02-09Bibliographically approved

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Tampu, Iulian EmilHaj-Hosseini, NedaBlystad, Ida

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