Validation of CBCT for the computation of textural biomarkers

Abstract

Osteoarthritis (OA) is associated with significant pain and 42.6% of patients with TMJ disorders present with evidence of TMJ OA. However, OA diagnosis and treatment remain controversial, since there are no clear symptoms of the disease. The subchondral bone in the TMJ is believed to play a major role in the progression of OA. We hypothesize that the textural imaging biomarkers computed in high resolution Conebeam CT (hr-CBCT) and μCT scans are comparable. The purpose of this study is to test the feasibility of computing textural imaging biomarkers in-vivo using hr-CBCT, compared to those computed in μCT scans as our Gold Standard. Specimens of condylar bones obtained from condylectomies were scanned using μCT and hr-CBCT. Nine different textural imaging biomarkers (four co-occurrence features and five run-length features) from each pair of μCT and hr-CBCT were computed and compared. Pearson correlation coefficients were computed to compare textural biomarkers values of μCT and hr-CBCT. Four of the nine computed textural biomarkers showed a strong positive correlation between biomarkers computed in μCT and hr-CBCT. Higher correlations in Energy and Contrast, and in GLN (grey-level non-uniformity) and RLN (run length non-uniformity) indicate quantitative texture features can be computed reliably in hr-CBCT, when compared with μCT. The textural imaging biomarkers computed in-vivo hr-CBCT have captured the structure, patterns, contrast between neighboring regions and uniformity of healthy and/or pathologic subchondral bone. The ability to quantify bone texture non-invasively now makes it possible to evaluate the progression of subchondral bone alterations, in TMJ OA.

Development and Validation of a Magnetic Resonance Imaging-Based Machine Learning Model for TMJ Pathologies

“…Based on their results, they concluded that CBCT Texture Analysis can be used for differentiating mandibular condyle changes. Recent studies also showed that GLCM and GLRLM textural features are potential diagnostic markers of TMJ osteoartrosis [25,26].…”

Modeling and evaluation of a high‐resolution CMOS detector for cone‐beam CT of the extremities

“…It is anticipated that improved system performance in the discrimination task will translate also to improved performance in the threshold‐based quantitative measurements. A potentially more challenging extension of this work might involve predicting the performance of a texture classification algorithm applied to trabecular regions . The investigation of textural biomarkers of bone quality is still in early stages and there is no consensus yet as to which of the textural features are best suited for diagnostic applications.…”

“…A potentially more challenging extension of this work might involve predicting the performance of a texture classification algorithm applied to trabecular regions. [77][78][79][80] The investigation of textural biomarkers of bone quality is still in early stages and there is no consensus yet as to which of the textural features are best suited for diagnostic applications. For some features, such as those based on the gray-level co-occurrence matrix, 81 improved conspicuity of fine trabecular detail may improve the sensitivity of classification.…”

Quantitative Assessment of Variation in CT Parameters on Texture Features: Pilot Study Using a Nonanatomic Phantom

“…The use of texture analysis has been increasing in prevalence throughout the radiology literature, particularly as an adjunct aiding in diagnosis, lesion characterization, and even in the evaluation for treatment-related response. [10][11][12][13][14]19,24 Preliminary works investigating the use of a texture analysis to detect and characterize stages of hepatic fibrosis have been reported in the literature. 7,25,26 Similarly, texture analysis has also been used for examining potential differences in tissue architecture on CT in oropharyngeal squamous cell carcinomas and for help in evaluating changes in cartilage.…”

“…[8][9][10][11][12][13][14][15][16][17][18] Despite a growing number of publications on the use of texture analysis for tumor imaging, direct correlations between texture analysis features and histopathologic analysis are still being investigated. 18,19 Recently, the upsurge in genomic discoveries and advanced imaging technologies on publicly available data bases, such as The Cancer Imaging Archive (http://www.cancerimagingarchive. net/) and The Cancer Genomic Atlas (https://cancergenome.nih.…”