T2 texture index of cartilage can predict early symptomatic OA progression: data from the osteoarthritis initiative

Urish, Kenneth L.; Keffalas, M.G.; Durkin, John; Miller, David J.; Chu, Constance R.; Mosher, Timothy J.

doi:10.1016/j.joca.2013.06.007

Cited by 47 publications

(34 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, many machine learning and deep learning methods have been applied to OA EMRs data mining, those methods are shown to be superior to the conventional methods in the specific tasks, classification and prediction, such as logistic regression method was used to predict the risk of knee OA [13]; deep convolutional neural networks (CNN) was used to quantify the severity of knee osteoarthritis, and showed a sizable improvement on the current state-of-the-art methods [14]; support vector machine(SVM) was used to predict symptomatic progression of OA using the texture metric [15], etc. However, there are few studies on the side effects of OA medication, especially for the risk prediction model of side effects of analgesics.…”

Section: Introductionmentioning

confidence: 99%

An interpretable boosting model to predict side effects of analgesics for osteoarthritis

Liu

Fei

et al. 2018

BMC Syst Biol

View full text Add to dashboard Cite

BackgroundOsteoarthritis (OA) is the most common disease of arthritis. Analgesics are widely used in the treat of arthritis, which may increase the risk of cardiovascular diseases by 20% to 50% overall.There are few studies on the side effects of OA medication, especially the risk prediction models on side effects of analgesics. In addition, most prediction models do not provide clinically useful interpretable rules to explain the reasoning process behind their predictions. In order to assist OA patients, we use the eXtreme Gradient Boosting (XGBoost) method to balance the accuracy and interpretability of the prediction model.ResultsIn this study we used the XGBoost model as a classifier, which is a supervised machine learning method and can predict side effects of analgesics for OA patients and identify high-risk features (RFs) of cardiovascular diseases caused by analgesics. The Electronic Medical Records (EMRs), which were derived from public knee OA studies, were used to train the model. The performance of the XGBoost model is superior to four well-known machine learning algorithms and identifies the risk features from the biomedical literature. In addition the model can provide decision support for using analgesics in OA patients.ConclusionCompared with other machine learning methods, we used XGBoost method to predict side effects of analgesics for OA patients from EMRs, and selected the individual informative RFs. The model has good predictability and interpretability, this is valuable for both medical researchers and patients.

show abstract

Section: Introductionmentioning

confidence: 99%

An interpretable boosting model to predict side effects of analgesics for osteoarthritis

Liu

Fei

et al. 2018

BMC Syst Biol

View full text Add to dashboard Cite

show abstract

“…These changes result in less signal variation between voxels in the T2 map leading to increased homogeneity. This is as compared to the normal articular cartilage where regional variations in collagen fiber anisotropy and water content provide a well-recognized pattern of signal variation [3, 9, 10]. …”

Section: Introductionmentioning

confidence: 99%

T2 map signal variation predicts symptomatic osteoarthritis progression: data from the Osteoarthritis Initiative

2016

View full text Add to dashboard Cite

Objective The aim of this work is to use quantitative Magnetic Resonance Imaging (MRI) to identify patients at risk for symptomatic osteoarthritis (OA) progression. We hypothesized that classification of signal variation on T2 maps could predict symptomatic OA progression. Methods Patients were selected from the Osteoarthritis Initiative (OAI), a prospective cohort. Two groups were identified; a symptomatic OA progression group and a control group. At baseline, both groups were asymptomatic (WOMAC total <10) with no radiographic evidence of OA (KL≤1). The OA progression group (n=103) had a change in total WOMAC score greater than 10 by 3 year follow up. The control group (n=79) remained asymptomatic with a change in total WOMAC score less than 10 at the 3 year follow up. A classifier was designed to predict OA progression in an independent population based on T2 map cartilage signal variation. The classifier was designed using a nearest neighbor classification based on a Gaussian Mixture Model log-likelihood fit of T2 map cartilage voxel intensities. Results The use of T2 map signal variation to predict symptomatic OA progression in asymptomatic individuals achieved a specificity of 89.3%, a sensitivity of 77.2%, and an overall accuracy rate of 84.2 %. Conclusion T2 map signal variation can predict symptomatic knee OA progression in asymptomatic individuals, serving as a possible early OA imaging biomarker.

show abstract

“…These methods discriminate between groups based on inherent patterns and textures within images; the application of such techniques to identifying early OA in human subjects has recently been explored 10–12 . Urish et al .…”

Section: Introductionmentioning

confidence: 99%

“…Urish et al . calculated four categories of MRI features, including histogram, gray level co-occurrence matrix, gray level run length matrix, and z-score from T 2 parameter maps obtained as part of the Osteoarthritis Initiative (OAI) 12, 13 . Using the support-vector machine approach, they achieved 71% accuracy in predicting the symptomatic progression of OA as defined by the Western Ontario and McMaster Universities Arthritis (WOMAC) questionnaire 14 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning classification of OARSI-scored human articular cartilage using magnetic resonance imaging

Ashinsky

Coletta

Bouhrara

et al. 2015

Osteoarthritis and Cartilage

View full text Add to dashboard Cite

Objective The purpose of this study is to evaluate the ability of machine learning to discriminate between magnetic resonance images (MRI) of normal and pathological human articular cartilage obtained under standard clinical conditions. Method An approach to MRI classification of cartilage degradation is proposed using pattern recognition and multivariable regression in which image features from MRIs of histologically scored human articular cartilage plugs were computed using weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHRM). The WND-CHRM method was first applied to several clinically available MRI scan types to perform binary classification of normal and osteoarthritic osteochondral plugs based on the OARSI histological system. In addition, the image features computed from WND-CHRM were used to develop a multiple linear least-squares regression model for classification and prediction of an OARSI score for each cartilage plug. Results The binary classification of normal and osteoarthritic plugs yielded results of limited quality with accuracies between 36% and 70%. However, multiple linear least-squares regression successfully predicted OARSI scores and classified plugs with accuracies as high as 86%. The present results improve upon the previously-reported accuracy of classification using average MRI signal intensities and parameter values. Conclusion MRI features detected by WND-CHRM reflect cartilage degradation status as assessed by OARSI histologic grading. WND-CHRM is therefore of potential use in the clinical detection and grading of osteoarthritis.

show abstract

T2 texture index of cartilage can predict early symptomatic OA progression: data from the osteoarthritis initiative

Cited by 47 publications

References 34 publications

An interpretable boosting model to predict side effects of analgesics for osteoarthritis

An interpretable boosting model to predict side effects of analgesics for osteoarthritis

T2 map signal variation predicts symptomatic osteoarthritis progression: data from the Osteoarthritis Initiative

Machine learning classification of OARSI-scored human articular cartilage using magnetic resonance imaging

Contact Info

Product

Resources

About