Validation of a Knowledge Transfer Tool According to the OMERACT Filter: Does Web-based Real-time Iterative Calibration Enhance the Evaluation of Bone Marrow Lesions in Hip Osteoarthritis?

Jaremko, Jacob L.; Azmat, Omar; Lambert, Robert G.; Bird, Paul; Haugen, Ida K.; Jans, Lennart; Weber, Ulrich; Winn, Naomi; Zubler, Veronika; Maksymowych, Walter P.

doi:10.3899/jrheum.161101

Cited by 8 publications

(8 citation statements)

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“…Reader training. We trained readers using a previously described RETIC tool validated and reported for OMERACT 5 (www.carearthritis.com). New readers reviewed the HIMRISS instructional PowerPoint module, then scored up to 16 hips from 8 patients with hip OA scanned at 2 timepoints, for which consensus BML scores had been previously agreed by expert HIMRISS developers.…”

Section: Methodsmentioning

confidence: 99%

“…(OMERACT) Filter 2.0 1,2,3 , scoring systems should be carefully evaluated for reliability, feasibility, and discrimination. The Hip Inflammation MRI Scoring System (HIMRISS) evaluates markers of active hip inflammation including bone marrow lesion (BML) 4,5 , recognizing that BML is increased T2/short-tau inversion recovery (STIR) signal intensity from inflammatory or noninflammatory processes 6 . In OMERACT 2016, a Web-based training module incorporating real-time iterative feedback calibration (RETIC) improved feasibility of HIMRISS scoring without sacrificing reliability 5 .…”

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confidence: 99%

“…The Hip Inflammation MRI Scoring System (HIMRISS) evaluates markers of active hip inflammation including bone marrow lesion (BML) 4,5 , recognizing that BML is increased T2/short-tau inversion recovery (STIR) signal intensity from inflammatory or noninflammatory processes 6 . In OMERACT 2016, a Web-based training module incorporating real-time iterative feedback calibration (RETIC) improved feasibility of HIMRISS scoring without sacrificing reliability 5 . Subsequent innovations in HIMRISS include Web-based interface and touch-sensitive electronic overlays to facilitate scoring.…”

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confidence: 99%

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OMERACT Hip Inflammation Magnetic Resonance Imaging Scoring System (HIMRISS) Assessment in Longitudinal Study

et al. 2019

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Objective.To assess reliability, feasibility, and responsiveness of Hip Inflammation Magnetic resonance imaging Scoring System (HIMRISS) for bone marrow lesions (BML) in hip osteoarthritis (OA).Methods.HIMRISS was scored by 8 readers in 360 hips of 90 patients imaged pre/post-hip steroid injection. Pre-scoring, new readers trained online to achieve intraclass correlation coefficient (ICC) > 0.80 versus experts.Results.HIMRISS reliability was excellent for BML status (ICC 0.83–0.92). Despite small changes post-injection, reliability of BML change scores was high in femur (0.76–0.81) and moderate in acetabulum (0.42–0.56).Conclusion.HIMRISS should be a priority for further assessment of hip BML in OA, and evaluated for use in other arthropathies.

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OMERACT Hip Inflammation Magnetic Resonance Imaging Scoring System (HIMRISS) Assessment in Longitudinal Study

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“…Imaging of osteoarthritis is significant for diagnosis process [ 145 ]. Internet-based knowledge transfer methods employing the well-established recording protocols showed harmony between imaging and medical specialty in explaining T2-W outcome [ 146 ]. Identical recording has been used in multiple sclerosis [ 147 ] and rectal wall diagnosis [ 148 ].…”

Section: Types Of Mri Biomarkers Based On Quantitative Abilitymentioning

confidence: 99%

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

et al. 2021

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The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.

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“…In cervical spondylosis, grading of high T2-weighted (T2W) signal within the spinal cord has been related variably to disease severity and outcome [11, 12]. In common diseases such as osteoarthritis, where follow-up scans to assess progression are vital in treatment decision-making, such scoring approaches also are useful [13]; web-based knowledge transfer tools using the developed scoring systems indicate good agreement between readers with both radiological and clinical background specialisms in interpreting the T2W MRI data [14]. Similar analyses have been extensively applied in diseases such as multiple sclerosis [15] and even to delineate the rectal wall from adjacent fibrosis [16].…”

Section: Validated Imaging Biomarkers Currently Used To Support Clinimentioning

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Validated imaging biomarkers as decision-making tools in clinical trials and routine practice: current status and recommendations from the EIBALL* subcommittee of the European Society of Radiology (ESR)

et al. 2019

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Observer-driven pattern recognition is the standard for interpretation of medical images. To achieve global parity in interpretation, semi-quantitative scoring systems have been developed based on observer assessments; these are widely used in scoring coronary artery disease, the arthritides and neurological conditions and for indicating the likelihood of malignancy. However, in an era of machine learning and artificial intelligence, it is increasingly desirable that we extract quantitative biomarkers from medical images that inform on disease detection, characterisation, monitoring and assessment of response to treatment. Quantitation has the potential to provide objective decision-support tools in the management pathway of patients. Despite this, the quantitative potential of imaging remains under-exploited because of variability of the measurement, lack of harmonised systems for data acquisition and analysis, and crucially, a paucity of evidence on how such quantitation potentially affects clinical decision-making and patient outcome. This article reviews the current evidence for the use of semi-quantitative and quantitative biomarkers in clinical settings at various stages of the disease pathway including diagnosis, staging and prognosis, as well as predicting and detecting treatment response. It critically appraises current practice and sets out recommendations for using imaging objectively to drive patient management decisions.

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Validation of a Knowledge Transfer Tool According to the OMERACT Filter: Does Web-based Real-time Iterative Calibration Enhance the Evaluation of Bone Marrow Lesions in Hip Osteoarthritis?

Cited by 8 publications

References 10 publications

OMERACT Hip Inflammation Magnetic Resonance Imaging Scoring System (HIMRISS) Assessment in Longitudinal Study

OMERACT Hip Inflammation Magnetic Resonance Imaging Scoring System (HIMRISS) Assessment in Longitudinal Study

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

Validated imaging biomarkers as decision-making tools in clinical trials and routine practice: current status and recommendations from the EIBALL* subcommittee of the European Society of Radiology (ESR)

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