ADC texture—An imaging biomarker for high‐grade glioma?. Issue 10 (15th September 2014)

• Record Type: Journal Article
• Authors: Brynolfsson, Patrik; Nilsson, David; Henriksson, Roger; Hauksson, Jón; Karlsson, Mikael; Garpebring, Anders; Birgander, Richard; Trygg, Johan; Nyholm, Tufve; Asklund, Thomas
• Is Part Of: Medical physics. Volume 41:Issue 10(2014)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2014-09-15
• Subjects: biodiffusion -- biomedical MRI -- cancer -- image enhancement -- image texture -- matrix algebra -- medical image processing -- principal component analysis -- radiation therapy -- tumours; Magnetic resonance imaging -- Cancer -- Matrix theory -- Probability theory, stochastic processes, and statistics; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Radiation therapy -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image -- Analysis of texture; texture analysis -- glioma -- multivariate image analysis -- ADC; Cancer -- Medical imaging -- Magnetic resonance imaging -- Image analysis -- Data analysis -- Spatial analysis -- Diffusion -- Image registration -- Multivariate analysis -- Cell growth; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4894812
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : 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.