Principal Component Analysis of Simultaneous PET‐MRI Reveals Patterns of Bone–Cartilage Interactions in Osteoarthritis

Tibrewala, Radhika; Pedoia, Valentina; Bucknor, Matthew; Majumdar, Sharmila

doi:10.1002/jmri.27387

Cited by 8 publications

(11 citation statements)

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“…Differences in SUV and kinetic parameters were observed between trabecular and cortical bone, in addition to regional differences in bone perfusion and tracer extraction fraction in subchondral bone 30 . Additionally, in subjects with early OA, observed increases in [ 18 F] NaF uptake measures of SUV and K i were associated with quantitative MRI parameters of overlying cartilage 18 and also with semi-quantitative scores for cartilage and BML 31 . However, the mechanisms behind the elevated tracer uptake in OA bone compared to healthy bone remain unclear.…”

Section: Introductionmentioning

confidence: 82%

Assessment of quantitative [18F]Sodium fluoride PET measures of knee subchondral bone perfusion and mineralization in osteoarthritic and healthy subjects

Watkins

Mackay

Haddock

et al. 2021

Osteoarthritis and Cartilage

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Section: Introductionmentioning

confidence: 82%

Assessment of quantitative [18F]Sodium fluoride PET measures of knee subchondral bone perfusion and mineralization in osteoarthritic and healthy subjects

Watkins

Mackay

Haddock

et al. 2021

Osteoarthritis and Cartilage

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“…It would be costly if a patient was subjected to multiple imaging techniques for a thorough diagnosis. Hence, researchers worked intensively on exploring hybrid imaging techniques that could combine the pros of different imaging systems [31][32][33].…”

Section: Potentials and Limitationsmentioning

confidence: 99%

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Teoh

Lai

Usman

et al. 2022

Journal of Healthcare Engineering

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Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren–Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future.

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“…A quick sample size calculation for a trial with 24.6% incidence in the control group and 18.6% in the intervention group would require 2 groups of 735 individuals followed-up for 3 years (a 5 95%, b 5 80%). 27 The aforementioned examples illustrate the importance of the selection of target populations based on the interaction of multiple risk factors for future preventive interventions. Risk factors such as female sex/gender or genetic predisposition might not be relevant on their own for the selection of a target population for OA prevention, as these factors are nonmodifiable.…”

Section: Identification Of Individuals With Osteoarthritis Risk Factorsmentioning

confidence: 99%

The Challenges in the Primary Prevention of Osteoarthritis

Runhaar

Bierma-Zeinstra

2022

Clinics in Geriatric Medicine

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Principal Component Analysis of Simultaneous PET‐MRI Reveals Patterns of Bone–Cartilage Interactions in Osteoarthritis

Cited by 8 publications

References 0 publications

Assessment of quantitative [18F]Sodium fluoride PET measures of knee subchondral bone perfusion and mineralization in osteoarthritic and healthy subjects

Assessment of quantitative [18F]Sodium fluoride PET measures of knee subchondral bone perfusion and mineralization in osteoarthritic and healthy subjects

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

The Challenges in the Primary Prevention of Osteoarthritis

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