Rheumatoid Synovial Inflammation: Pixel-by-Pixel Dynamic Contrast-enhanced MR Imaging Time-Intensity Curve Shape Analysis—A Feasibility Study

Leij, Christiaan van der; Sande, Marleen G H van de; Lavini, Cristina; Tak, Paul P.; Maas, Mario

doi:10.1148/radiol.2531081722

Cited by 43 publications

(31 citation statements)

References 24 publications

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“…In a previous study, the early washout phase was explained by the back flux of contrast agent due to increased vascular permeability and/or the passive diffusion of contrast agent into effusion fluid. 18 In the knee and wrist, the washout phase is seen because contrast agent flows directly into the pannus from the vessels with high blood flow. On the other hand, as the partial synovial pannus in the finger joint is gradually supplied with blood via narrow peripheral arteries, the back flux of contrast agent due to increased vascular permeability may not necessarily have occurred.…”

Section: Discussionmentioning

confidence: 99%

“…It was reported that the relative number of Type 4 TIC shapes was significantly greater in patients with RA than in healthy control subjects; hence Type 4 TIC shapes were associated with RA. 18,19,23 Although these studies were performed in the knee, pannus formations were mainly classified as Type 4 in the RA hand as well due to the characteristics of inflamed synovial tissue related to increased tissue vascularization and permeability. However, as pointed out above, a part of pannus tissue located in the finger region was classified into other TIC types.…”

Section: Discussionmentioning

confidence: 99%

“…Among these, Type 4 is reported to be relevant to RA. 18,19 Type 6 shows rapid enhancement followed by a sharp signal drop. Type 7 shows all others.…”

Section: Mri Data Analysismentioning

confidence: 99%

“…The TIC shape distribution of each image section was visualized in colour-coded maps. 18 The total number of Type 4 TIC shapes was calculated because it has been reported that the Type 4 TIC shape is associated with RA. 19 In this study, we regarded the Type 4 TIC shape as the area of the pannus.…”

Section: Mri Data Analysismentioning

confidence: 99%

“…6,17 On the other hand, pixel-bypixel time-intensity curve (TIC) shape analysis is a new dynamic contrast-enhanced MRI technique to help visualize differently shaped TICs. 18,19 Previously, Type 4 TIC shape expression, characterized by fast initial enhancement followed by early washout, was reported to be increased in patients with RA compared with healthy subjects. 19 Although the feasibility of measuring the extent of pannus formation using this approach has not been determined in the hand, we hypothesize that this technique can accurately quantify the size of the synovial pannus.…”

Section: Introductionmentioning

confidence: 99%

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Accurate quantitative assessment of synovitis in rheumatoid arthritis using pixel-by-pixel, time–intensity curve shape analysis

Sakashita¹,

Kamishima²,

Kobayashi³

et al. 2016

BJR

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Objective: To improve on the reproducibility and sensitivity of the assessment of patients with rheumatoid arthritis (RA), two semi-automated measurement methods of the area of enhancing pannus (AEP), based on thresholding (AEP_THRES) and pixel-by-pixel timeintensity curve analysis (AEP_TIC), were evaluated as an alternative for the gold-standard manual contouring method (AEP_MANUAL). Methods: 8 patients (7 females and 1 male) with RA of the wrist or finger joints participated in the study. A threedimensional contrast-enhanced dynamic sequence was used at 3 T. After identifying the most relevant timeintensity curve (TIC) shape in terms of synovitis by comparing with the synovitis score using the RA-MRI scoring system, three different approaches for measuring the AEP were performed. Spearman's test of rank correlation was used to compare AEPs via two semi-automated methods (AEP_THRES and AEP_TIC) against manual segmentation (AEP_MANUAL) in the entire hand region as well as the wrist and the finger regions. Results: The TIC shape of "washout after fast initial enhancement" had excellent correlation with synovitis score (r 5 0.809). The correlation coefficient between AEP_TIC and AEP_MANUAL was evaluated to be better than that of AEP_THRES and AEP_MANUAL in the wrist region (AEP_THRES: r 5 0.716, AEP_TIC: r 5 0.815), whereas these were of comparable accuracy for the entire hand and the finger regions. Conclusion: This study suggests that TIC analysis may be an alternative to manual contouring for pannus quantification and provides important clinical information of the extent of the disease in patients with RA. Advances in knowledge: TIC shape analysis can be applied for new quantitative assessment for RA synovitis in the wrist.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…Among these, Type 4 is reported to be relevant to RA. 18,19 Type 6 shows rapid enhancement followed by a sharp signal drop. Type 7 shows all others.…”

Section: Mri Data Analysismentioning

confidence: 99%

Section: Mri Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Accurate quantitative assessment of synovitis in rheumatoid arthritis using pixel-by-pixel, time–intensity curve shape analysis

Sakashita¹,

Kamishima²,

Kobayashi³

et al. 2016

BJR

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Computer‐Assisted Image Analysis in Assessment of Peripheral Joint MRI in Inflammatory Arthritis: A Systematic Review and Meta‐analysis

Haj‐Mirzaian

Kubassova

Boesen

et al. 2022

ACR Open Rheumatology

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Objective To summarize the feasibility of computer‐assisted quantification of joint pathologies on magnetic resonance imaging (MRI) in patients with inflammatory arthritis by evaluating the published data on reliability, validity, and feasibility. Methods A systematic literature search was performed for original articles published from January 1, 1985, to January 1, 2021. We selected studies in which patients with inflammatory arthritis were enrolled, and arthritis‐related structural damage/synovitis in peripheral joints was assessed on non‐contrast‐enhanced, contrast‐enhanced (CE), or dynamic CE (DCE)‐MRI using (semi)automated methods. Data were pooled using random‐effects model. Results Twenty‐eight studies consisting of 1342 MRIs were included (mean age, 54.8 years; 66.7% female; duration of arthritis, 3.6 years). Among clinical/laboratory factors, synovial membrane volume (SV) was moderately correlated with erthrocyte sedimentation rate (ESR) level ( P < 0.01). Pooled analysis showed an overall excellent intra‐ and inter‐reader reliability for computer‐aided quantification of bone erosion volume (BEV; r = 0.97 [95% CI: 0.92‐0.99], 0.93 [0.87‐0.97]), SV (r = 0.98 [95% CI: 0.90‐0.99], 0.86 [0.78‐0.91]), and DCE‐MRI perfusion parameters (r = 0.96‐0.99). Meta‐regression showed that computer‐aided and manual methods provide comparable reliability ( P > 0.05). Computer‐aided measurement of BEV (r = 0.92), SV (r = 0.82), and DCE‐MRI biomarkers (r = 0.72 N‐total; r = 0.74 N‐plateau; r = 0.64 N‐washout) were significantly correlated with the Rheumatoid Arthritis Magnetic Resonance Imaging Score (RAMRIS; P < 0.01), allowing for earlier assessment of drug efficacy. On average, (semi)automated analysis of BEV/SV took 17 minutes (vs. 9 minutes for the RAMRIS) and DCE‐MRI took 4 minutes (vs. 33 minutes for manual assessment). Conclusion Computer‐aided image quantification technologies demonstrate excellent reliability and validity when used to quantify MRI pathologies of peripheral joints in patients with inflammatory arthritis. Computer‐aided evaluation of inflammatory arthritis is an emerging field and should be considered as a viable complement to conventional observer‐based scoring methods for clinical trials application.

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Model‐based, semiquantitative and time intensity curve shape analysis of dynamic contrast‐enhanced MRI: A comparison in patients undergoing antiangiogenic treatment for recurrent glioma

Lavini

Verhoeff

Majoie

et al. 2011

Magnetic Resonance Imaging

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Purpose: To compare time intensity curve (TIC)-shape analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data with model-based analysis and semiquantitative analysis in patients with highgrade glioma treated with the antiangiogenic drug bevacizumab. Materials and Methods:Fifteen patients had a pretreatment and at least one posttreatment DCE-MRI. We applied a pixel-by-pixel TIC shape analysis, where TICs are classified into five different types according to their shape, and calculated the occurrence of each TIC type in the region of interest (ROI). The results were compared to the pharmacokinetic model (PKM) parameters K trans , K ep , V e , and V i , and with the semiquantitative parameters maximum enhancement (ME) and initial slope of increase (ISI).Results: The relative amount of type 2 and 4 TIC shape significantly correlated with the parameter K ep but not with K trans or V e . The PKM parameter V e and the semiquantitative parameters ME and ISI showed significant changes after treatment. None of the TIC shapes individually showed significant changes. Conclusion:The semiquantitative parameters ME and ISI are more sensitive to the effect of the bevacizumab than K trans and V e . The pixel-by-pixel TIC shape analysis parameters are not sensitive to the effect of bevacizumab, although they can be seen as surrogates for the PKM parameter K ep . DYNAMIC CONTRAST-ENHANCED MAGNETIC RESO-NANCE IMAGING (DCE-MRI) using small molecular weight, gadolinium (Gd)-chelate-based contrast media is widely accepted as a valuable diagnostic aid in cancer imaging, as a mirror of the effect of antitumor drugs, and possibly also as a prognostic tool (1). Several analysis methods can be applied to DCE-MRI, varying from a simple by-eye observation of the timedependent variation in signal intensity after contrast delivery to more sophisticated methods that make use of theoretical pharmacokinetic (PK) models. The application of PK models to the analysis of DCE-MRI data allows the extraction of physiologically relevant quantities that reflect intrinsic properties of the tissue, such as vascular permeability, blood flow, extracellular-extravascular, and vascular volume. Because the quantities measured reflect properties of the tissue and are independent of the MRI settings and parameters that are used to generate them, this analysis method is meant to be a truly quantitative method. First proposed, independently, by Tofts, Brix and Larsson in early 1990 and later refined (2), this PK model is still widely used in cancer imaging: it generates the parameters K trans (the wash-in rate), which describes the forward leakage rate of the contrast medium, K ep (the wash-out constant unit), V e (the extracellular-extravascular space), and V i (the plasma volume). These PK parameters are useful in the differentiation between infective and neoplastic brain lesions (3) and glioma grading (4,5). A plethora of different other models have been proposed (6,7), but Tofts' model remains the most used because of its relative simplici...

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Rheumatoid Synovial Inflammation: Pixel-by-Pixel Dynamic Contrast-enhanced MR Imaging Time-Intensity Curve Shape Analysis—A Feasibility Study

Cited by 43 publications

References 24 publications

Accurate quantitative assessment of synovitis in rheumatoid arthritis using pixel-by-pixel, time–intensity curve shape analysis

Accurate quantitative assessment of synovitis in rheumatoid arthritis using pixel-by-pixel, time–intensity curve shape analysis

Computer‐Assisted Image Analysis in Assessment of Peripheral Joint MRI in Inflammatory Arthritis: A Systematic Review and Meta‐analysis

Model‐based, semiquantitative and time intensity curve shape analysis of dynamic contrast‐enhanced MRI: A comparison in patients undergoing antiangiogenic treatment for recurrent glioma

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