Dataset Information

Contrast-Enhanced Computed Tomography Scoring System for Distinguishing Early Osteoarthritis Disease States: A Feasibility Study.

ABSTRACT: Early detection of osteoarthritis (OA) remains a diagnostic challenge owing to insensitive diagnostic techniques currently available. Herein a new semiquantitative scoring system, based upon contrast-enhanced computed tomographic (CECT) imaging, is described for further refinement of early OA disease staging. Trochlear ridge cartilage defects were surgically created in the femoropatellar joint of an adult horse (ACUC approved protocols). Seven weeks post-surgery, CECT imaging was performed on a clinical scanner after intra-articular injection of a cationic iodinated contrast agent, CA4+, into both injured and control femoropatellar joint compartments. The femoral cartilage surface was densely biopsied, and specimens were assessed for visual (Outerbridge score), functional (equilibrium compressive modulus), and biochemical (glycosaminoglycan content) measures of cartilage quality. Cartilage CECT attenuation was compared with cartilage quality measures using receiver operating characteristic curve analysis to establish attenuation thresholds for distinguishing among cartilage quality levels. CECT imaging identifies macroscopically damaged cartilage regions and in morphologically identical tissue provides moderately sensitive and specific semiquantitative segregation of cartilage quality based upon CECT attenuation, reflecting both glycosaminoglycan content and compressive stiffness of cartilage area under the curve (AUC?=?0.83 [95% confidence interval [CI]: 0.72-0.93] for distinguishing poor quality and AUC?=?0.76 [95% CI: 0.65-0.90] for distinguishing healthy quality cartilage). A semiquantitative 6-point scoring system-the Osteoarthritis Attenuation and Morphological Assessment (OAMA) score-is proposed as a tool for assessing cartilage quality from CECT images. The OAMA scoring system expands the current disease staging capability of early OA by inclusion of morphological, biochemical, and biomechanical assessments. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:2138-2148, 2019.

SUBMITTER: Stewart RC

PROVIDER: S-EPMC6739126 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Contrast-Enhanced Computed Tomography Scoring System for Distinguishing Early Osteoarthritis Disease States: A Feasibility Study.

Stewart Rachel C RC Nelson Brad B BB Kawcak Chris E CE Freedman Jonathan D JD Snyder Brian D BD Goodrich Laurie R LR Grinstaff Mark W MW

Journal of orthopaedic research : official publication of the Orthopaedic Research Society 20190621 10

Early detection of osteoarthritis (OA) remains a diagnostic challenge owing to insensitive diagnostic techniques currently available. Herein a new semiquantitative scoring system, based upon contrast-enhanced computed tomographic (CECT) imaging, is described for further refinement of early OA disease staging. Trochlear ridge cartilage defects were surgically created in the femoropatellar joint of an adult horse (ACUC approved protocols). Seven weeks post-surgery, CECT imaging was performed on a ...[more]

PMID: 31136003

Similar Datasets

Project description:PurposeModern non-invasive evaluation of Coronary Artery Disease (CAD) requires non-contrast low dose Computed Tomography (CT) imaging for determination of Calcium Scoring (CACS) and contrast-enhanced imaging for evaluation of vascular stenosis. Several methods for calculation of CACS from contrast-enhanced images have been proposed before. The main principle for that is generation of virtual non-contrast images by iodine subtraction from a contrast-enhanced spectral CT dataset. However, those techniques have some limitations: Dual-Source CT imaging can lead to increased radiation exposure, and switching of the tube voltage (rapid kVp switching) can be associated with slower rotation speed of the gantry and is thus prone to motion artefacts that are especially critical in cardiac imaging. Both techniques cannot simultaneously acquire spectral data. A novel technique to overcome these difficulties is spectral imaging with a dual-layer detector. After absorption of the lower energetic photons in the first layer, the second layer detects a hardened spectrum of the emitted radiation resulting in registration of two different energy spectra at the same time. The objective of the present investigation was to evaluate the accuracy of virtual non-contrast CACS computed from spectral data in comparison to standard non-contrast imaging.MethodsWe consecutively investigated 20 patients referred to Coronary Computed Tomography Angiography (CCTA) with suspicion of CAD using a Dual-Layer spectral CT system (IQon; Philips Healthcare, The Netherlands). CACS was calculated from both, real- and virtual non-contrast images by certified software for medical use. Correlation analyses for real- and virtual non-contrast images and agreement evaluation with Bland-Altman-Plots were performed.ResultsMean patient age was 57.7 ± 14 years (n = 20). 13 patients (65%) were male. Inter-quartile-range of clinical CACS was 0-448, the mean was 334. Correlation of CACS from real- and virtual non-contrast images was very high (0.94); p < 0.0001. The slope was 2.3 indicating that values from virtual non-contrast images are approximately half of the results obtained from real non-contrast data. Visual analysis of Bland-Altman-Plot shows good accordance of both methods when results from virtual non-contrast data are multiplied by the slope of the logistic regression model (2.3). The acquired power of this results is 0.99.ConclusionDetermination of Calcium Score from contrast enhanced CCTA using spectral imaging with a dual-layer detector is feasible and shows good agreement with the conventional technique when a proportionality factor is applied. The observed difference between both methods is due to an underestimation of plaque volume, and-to an even greater extend -an underestimation of plaque density with the virtual non-contrast approach. Our data suggest that radiation exposure can be reduced through omitting additional native scans for patients referred to CCTA when using a dual-layer spectral system without the usual limitations of dual energy analysis.

Project description:Background: Serous cystadenoma (SCA), mucinous cystadenoma (MCN), and intraductal papillary mucinous neoplasm (IPMN) are three subtypes of pancreatic cystic neoplasm (PCN). Due to the potential of malignant-transforming, patients with MCN and IPMN require radical surgery while patients with SCA need periodic surveillance. However, accurate pre-surgery diagnosis between SCA, MCN, and IPMN remains challenging in the clinic. Methods: This study enrolled 164 patients including 76 with SCA, 40 with MCN and 48 with IPMN. Patients were randomly split into a training cohort (n = 115) and validation cohort (n = 41). We performed statistical analysis and Boruta method to screen significantly distinct clinical factors and radiomics features extracted on pre-surgery contrast-enhanced computed tomography (CECT) images among three subtypes. Three reliable machine-learning algorithms, support vector machine (SVM), random forest (RF) and artificial neural network (ANN), were utilized to construct classifiers based on important radiomics features and clinical parameters. Precision, recall, and F1-score were calculated to assess the performance of the constructed classifiers. Results: Nine of 547 radiomics features and eight clinical factors showed a significant difference among SCA, MCN, and IPMN. Five radiomics features (Histogram_Entropy, Histogram_Skeweness, LLL_GLSZM_GLV, Histogram_Uniformity, HHL_Histogram_Kurtosis), and four clinical factors, including serum carbohydrate antigen 19-9, sex, age, and serum carcinoembryonic antigen, were identified important by Boruta method. The SVM classifier achieved an overall accuracy of 73.04% in training cohort and 71.43% in validation cohort, respectively. The RF classifier achieved overall accuracy of 84.35 and 79.59%, respectively. The constructed ANN model showed an overall accuracy of 77.39% in the training dataset and 71.43% in the validation dataset. All the three classifiers showed high F1 score for differentiation among the three subtypes. Conclusion: Our study proved the feasibility and translational value of CECT-based radiomics classifiers for differentiation among SCA, MCN, and IPMN.

Dataset Information

Contrast-Enhanced Computed Tomography Scoring System for Distinguishing Early Osteoarthritis Disease States: A Feasibility Study.

Publications

Contrast-Enhanced Computed Tomography Scoring System for Distinguishing Early Osteoarthritis Disease States: A Feasibility Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets