Using multidimensional topological data analysis to identify traits of hip osteoarthritis

Rossi-Devries, J.; Pedoia, Valentina; Samaan, Michael A.; Ferguson, Adam R.; Souza, Richard B.; Majumdar, Sharmila

doi:10.1002/jmri.26029

Cited by 12 publications

(10 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This indicates that the thickness of the subtrochanter region may be continuously remodeling to adjust bone architecture to meet the needs of the changing joint in osteoarthritic patients, which could manifest as pain and discomfort. A previous study has used multidimensional data analysis in traits of hip OA and shown that pain changes during different stages of the disease . This further corroborates the need for a longitudinal study, to evaluate pain at different stages of the disease.…”

Section: Discussionsupporting

confidence: 64%

[¹⁸F]‐Sodium Fluoride PET/MR Imaging for Bone–Cartilage Interactions in Hip Osteoarthritis: A Feasibility Study

Tibrewala

Bahroos

Mehrabian

et al. 2019

Journal Orthopaedic Research

Self Cite

View full text Add to dashboard Cite

This study characterized the distribution of [ 18 F]-sodium fluoride (NaF) uptake and blood flow in the femur and acetabulum in hip osteoarthritis (OA) patients to find associations between bone remodeling and cartilage composition in the presence of morphological abnormalities using simultaneous positron emission tomography and magnetic resonance imaging (PET/MR), quantitative magnetic resonance imaging (MRI) and femur shape modeling. Ten patients underwent a [ 18 F]-NaF PET/MR dynamic scan of the hip simultaneously with: (i) fast spin-echo CUBE for morphology grading and (ii) T 1ρ /T 2 magnetization-prepared angle-modulated partitioned k-space spoiled gradient echo snapshots for cartilage, bone segmentation, bone shape modeling, and T 1ρ /T 2 quantification. The standardized uptake values (SUVs) and Patlak kinetic parameter (K pat ) were calculated for each patient as PET outcomes, using an automated post-processing pipeline. Shape modeling was performed to extract the variations in bone shapes in the patients. Pearson's correlation coefficients were used to study the associations between bone shapes, PET outcomes, and patient reported pain. Direct associations between quantitative MR and PET evidence of bone remodeling were established in the acetabulum and femur. Associations of shaft thickness with SUV in the femur (p = 0.07) and K pat in the acetabulum (p = 0.02), cam deformity with acetabular score (p = 0.09), osteophytic growth on the femur head with K pat (p = 0.01) were observed. Pain had increased correlations with SUV in the acetabulum (p = 0.14) and femur (p = 0.09) when shaft thickness was accounted for. This study demonstrated the ability of [ 18 F]-NaF PET-MRI, 3D shape modeling, and quantitative MRI to investigate cartilage-bone interactions and bone shape features in hip OA, providing potential investigative tools to diagnose OA.

show abstract

Section: Discussionsupporting

confidence: 64%

[¹⁸F]‐Sodium Fluoride PET/MR Imaging for Bone–Cartilage Interactions in Hip Osteoarthritis: A Feasibility Study

Tibrewala

Bahroos

Mehrabian

et al. 2019

Journal Orthopaedic Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…After reading complete articles, we excluded 43 articles related to theoretical radiology, 9 to surgery outcomes and robotics, 10 to reeducation outcomes, 45 to molecular biology and 10 to other diseases. We finally selected and analysed 46 articles9 27–72 (online supplemental table S1).…”

Section: Resultsmentioning

confidence: 99%

Use of machine learning in osteoarthritis research: a systematic literature review

et al. 2022

Self Cite

View full text Add to dashboard Cite

ObjectiveThe aim of this systematic literature review was to provide a comprehensive and exhaustive overview of the use of machine learning (ML) in the clinical care of osteoarthritis (OA).MethodsA systematic literature review was performed in July 2021 using MEDLINE PubMed with key words and MeSH terms. For each selected article, the number of patients, ML algorithms used, type of data analysed, validation methods and data availability were collected.ResultsFrom 1148 screened articles, 46 were selected and analysed; most were published after 2017. Twelve articles were related to diagnosis, 7 to prediction, 4 to phenotyping, 12 to severity and 11 to progression. The number of patients included ranged from 18 to 5749. Overall, 35% of the articles described the use of deep learning And 74% imaging analyses. A total of 85% of the articles involved knee OA and 15% hip OA. No study investigated hand OA. Most of the studies involved the same cohort, with data from the OA initiative described in 46% of the articles and the MOST and Cohort Hip and Cohort Knee cohorts in 11% and 7%. Data and source codes were described as publicly available respectively in 54% and 22% of the articles. External validation was provided in only 7% of the articles.ConclusionThis review proposes an up-to-date overview of ML approaches used in clinical OA research and will help to enhance its application in this field.

show abstract

“…In this study, we considered only the relaxation times in the tibia-femoral cartilage for the characterization of OA and included only the age, gender, BMI, and KOOS reported pain scores in the model. OA is whole organ disease that includes other tissues in the joint, specifically their morphological, biochemical, and biomechanical aspect, along with the subjects physical activity levels; the separate analysis of each contributing factor is unable to fully capture the complex nature of this multifactorial disease 35,36 . Thus, further evaluation including the features in the meniscus and bone marrow, along with activity levels, may provide greater insights.…”

Section: Discussionmentioning

confidence: 99%

Diagnosing osteoarthritis from T2 maps using deep learning: an analysis of the entire Osteoarthritis Initiative baseline cohort

Pedoia

Lee

Norman

et al. 2019

Osteoarthritis and Cartilage

View full text Add to dashboard Cite

Objective: We aim to study to what extent conventional and deep-learning-based T 2 relaxometry patterns are able to distinguish between knees with and without radiographic osteoarthritis (OA). Methods: T 2 relaxation time maps were analyzed for 4,384 subjects from the baseline Osteoarthritis Initiative (OAI) Dataset. Voxel Based Relaxometry (VBR) was used for automatic quantification and voxelbased analysis of the differences in T 2 between subjects with and without radiographic OA. A Densely Connected Convolutional Neural Network (DenseNet) was trained to diagnose OA from T 2 data. For comparison, more classical feature extraction techniques and shallow classifiers were used to benchmark the performance of our algorithm's results. Deep and shallow models were evaluated with and without the inclusion of risk factors. Sensitivity and Specificity values and McNemar test were used to compare the performance of the different classifiers. Results: The best shallow model was obtained when the first ten Principal Components, demographics and pain score were included as features (AUC ¼ 77.77%, Sensitivity ¼ 67.01%, Specificity ¼ 71.79%). In comparison, DenseNet trained on raw T 2 data obtained AUC ¼ 83.44%, Sensitivity ¼ 76.99%, Specificity ¼ 77.94%. McNemar test on two misclassified proportions form the shallow and deep model showed that the boost in performance was statistically significant (McNemar's chi-squared ¼ 10.33, degree of freedom (DF) ¼ 1, P-value ¼ 0.0013). Conclusion: In this study, we presented a Magnetic Resonance Imaging (MRI)-based data-driven platform using T 2 measurements to characterize radiographic OA. Our results showed that feature learning from T 2 maps has potential in uncovering information that can potentially better diagnose OA than simple averages or linear patterns decomposition.

show abstract

Using multidimensional topological data analysis to identify traits of hip osteoarthritis

Abstract: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1046-1058.

Cited by 12 publications

References 40 publications

[¹⁸F]‐Sodium Fluoride PET/MR Imaging for Bone–Cartilage Interactions in Hip Osteoarthritis: A Feasibility Study

[¹⁸F]‐Sodium Fluoride PET/MR Imaging for Bone–Cartilage Interactions in Hip Osteoarthritis: A Feasibility Study

Use of machine learning in osteoarthritis research: a systematic literature review

Diagnosing osteoarthritis from T2 maps using deep learning: an analysis of the entire Osteoarthritis Initiative baseline cohort

Contact Info

Product

Resources

About

Using multidimensional topological data analysis to identify traits of hip osteoarthritis

Abstract: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1046-1058.

Cited by 12 publications

References 40 publications

[18F]‐Sodium Fluoride PET/MR Imaging for Bone–Cartilage Interactions in Hip Osteoarthritis: A Feasibility Study

[18F]‐Sodium Fluoride PET/MR Imaging for Bone–Cartilage Interactions in Hip Osteoarthritis: A Feasibility Study

Use of machine learning in osteoarthritis research: a systematic literature review

Diagnosing osteoarthritis from T2 maps using deep learning: an analysis of the entire Osteoarthritis Initiative baseline cohort

Contact Info

Product

Resources

About

[¹⁸F]‐Sodium Fluoride PET/MR Imaging for Bone–Cartilage Interactions in Hip Osteoarthritis: A Feasibility Study

[¹⁸F]‐Sodium Fluoride PET/MR Imaging for Bone–Cartilage Interactions in Hip Osteoarthritis: A Feasibility Study