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ADC texture-An imaging biomarker for high-grade glioma?
Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper.
Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper.
Vise andre og tillknytning
2014 (engelsk)Inngår i: Medical physics (Lancaster), ISSN 0094-2405, Vol. 41, nr 10, s. 101903-Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Purpose:

Survival for high-grade gliomas is poor, at least partly explained by intratumoral heterogeneity contributing to treatment resistance. Radiological evaluation of treatment response is in most cases limited to assessment of tumor size months after the initiation of therapy. Diffusion-weighted magnetic resonance imaging (MRI) and its estimate of the apparent diffusion coefficient (ADC) has been widely investigated, as it reflects tumor cellularity and proliferation. The aim of this study was to investigate texture analysis of ADC images in conjunction with multivariate image analysis as a means for identification of pretreatment imaging biomarkers.

Methods:

Twenty-three consecutive high-grade glioma patients were treated with radiotherapy (2 Gy/60 Gy) with concomitant and adjuvant temozolomide. ADC maps and T1-weighted anatomical images with and without contrast enhancement were collected prior to treatment, and (residual) tumor contrast enhancement was delineated. A gray-level co-occurrence matrix analysis was performed on the ADC maps in a cuboid encapsulating the tumor in coronal, sagittal, and transversal planes, giving a total of 60 textural descriptors for each tumor. In addition, similar examinations and analyses were performed at day 1, week 2, and week 6 into treatment. Principal component analysis (PCA) was applied to reduce dimensionality of the data, and the five largest components (scores) were used in subsequent analyses. MRI assessment three months after completion of radiochemotherapy was used for classifying tumor progression or regression.

Results:

The score scatter plots revealed that the first, third, and fifth components of the pretreatment examinations exhibited a pattern that strongly correlated to survival. Two groups could be identified: one with a median survival after diagnosis of 1099 days and one with 345 days, p = 0.0001.

Conclusions:

By combining PCA and texture analysis, ADC texture characteristics were identified, which seems to hold pretreatment prognostic information, independent of known prognostic factors such as age, stage, and surgical procedure. These findings encourage further studies with a larger patient cohort. (C) 2014 Author(s).

sted, utgiver, år, opplag, sider
2014. Vol. 41, nr 10, s. 101903-
Emneord [en]
texture analysis, glioma, multivariate image analysis, ADC
HSV kategori
Identifikatorer
URN: urn:nbn:se:umu:diva-96625DOI: 10.1118/1.4894812ISI: 000343032400019OAI: oai:DiVA.org:umu-96625DiVA, id: diva2:766452
Tilgjengelig fra: 2014-11-27 Laget: 2014-11-24 Sist oppdatert: 2018-06-07bibliografisk kontrollert
Inngår i avhandling
1. Applications of statistical methods in quantitative magnetic resonance imaging
Åpne denne publikasjonen i ny fane eller vindu >>Applications of statistical methods in quantitative magnetic resonance imaging
2017 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Magnetic resonance imaging, MRI, offers a vast range of imaging methods that can be employed in the characterization of tumors. MRI is generally used in a qualitative way, where radiologists interpret the images for e.g. diagnosis, follow ups, or assessment of treatment response. In the past decade, there has been an increasing interest for quantitative imaging, which give repeatable measurements of the anatomy. Quantitative imaging allows for objective analysis of the images, which are grounded in physical properties of the underlying tissues. The aim of this thesis was to improve quantitative measurements of Dynamic contrast enhanced MRI (DCE-MRI), and the texture analysis of diffusion weighted MRI (DW-MRI).

DCE-MRI measures perfusion, which is the delivery of blood, oxygen and nutrients to the tissues. The exam involves continuously imaging the region of interest, e.g. a tumor, while injecting a contrast agent (CA) in the blood stream. By analyzing how fast and how much CA leaks out into the tissues, the cell density and the permeability of the capillaries can be estimated. Tumors often have an irregular and broken vasculature, and DCE-MRI can aid in tumor grading or treatment assessment. One step is crucial when performing DCE-MRI analysis, the quantification of CA in the tissue. The CA concentration is difficult to measure accurately due to uncertainties in the imaging, properties of the CA, and physiology of the patient. Paper I, the possibility of using two aspects of the MRI data, phase and magnitude, for improved CA quantification, is explored. We found that the combination of phase and magnitude information improved the CA quantification in regions with high CA concentration, and was more advantageous for high field strength scanners.

DW-MRI measures the diffusion of water in and between cells, which reflects the cell density and structure of the tissue. The structure of a tumor can give insights into the prognosis of the disease. Tumors are heterogeneous, both genetically and in the distribution of cells, and tumors with high intratumoral heterogeneity have poorer prognosis. This heterogeneity can be measured using texture analysis. In 1973, Haralick et al. presented a texture analysis method using a gray level co-occurrence matrix, GLCM, to gauge the spatial distribution of gray levels in the image. This method of assessing texture in images has been successfully applied in many areas of research, from satellite images to medical applications. Texture analysis in treatment outcome assessment is studied in Paper II, where we showed that texture can distinguish between groups of patients with different survival times, in images acquired prior to treatment start.

However, this type of texture analysis is not inherently quantitative in the way it is calculated today. This was studied in Paper III, where we investigated how texture features were affected by five parameters related to image acquisition and pre-processing. We found that the texture feature values were dependent on the choice of these imaging and preprocessing parameters. In Paper IV, a novel method for calculating Haralick texture features was presented, which makes the texture features asymptotically invariant to the size of the GLCM. This method allows for comparison of textures between images that have been analyzed in different ways.

In conclusion, the work in this thesis has been aimed at improving quantitative analysis of tumors using MRI and texture analysis.

sted, utgiver, år, opplag, sider
Umeå: Umeå universitet, 2017. s. 65
Serie
Umeå University medical dissertations, ISSN 0346-6612 ; 1900
Emneord
Quantitative imaging, tumor imaging, dynamic contrast-enhanced MRI, diffusion weighted MRI, texture analysis
HSV kategori
Forskningsprogram
radiofysik
Identifikatorer
urn:nbn:se:umu:diva-134997 (URN)978-91-7601-729-6 (ISBN)
Disputas
2017-06-09, Bergasalen, byggnad 27, Norrlands universitetssjukhus, Umeå, 09:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2017-05-19 Laget: 2017-05-15 Sist oppdatert: 2018-06-09bibliografisk kontrollert

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