Prediction of recurrence and remission using superb microvascular imaging in rheumatoid arthritis

Matsuo, Hidemasa; Imamura, Akari; Shimizu, Mika; Inagaki, Maiko; Tsuji, Y; Nakabo, Shuichiro; Hashimoto, Motomu; Ito, Hiromu; Tanaka, Shiro; Mimori, Tsuneyo; Fujii, Yasutomo

doi:10.1007/s10396-019-00978-8

Cited by 7 publications

(10 citation statements)

References 39 publications

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“…Typical cases are not difficult to diagnose, but early patients and atypical cases are easy to be missed or misdiagnosed (16,17). These patients who are easily misdiagnosed or missed should be combined with laboratory and imaging results (18)(19)(20), our result that combined diagnosis of RF, A-CCP and radiological outcome has the highest diagnostic ability for RA conforms that combined blood and imaging is an effective mode for the diagnosis of RA. However, laboratory results have not been reported to predict the imaging increased risk of RA in people with different imaging stages.…”

Section: Discussionsupporting

confidence: 59%

Clinical analysis of blood markers and imaging for diagnosis and prognosis of rheumatoid arthritis

Yang

Shengxia

et al. 2020

Preprint

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Background The purpose of this study is to explore the combined diagnostic value of anti-cyclic citrullinated peptide antibody (A-CCP), rheumatoid factor (RF) and imaging result in early rheumatoid arthritis, and the role of A-CCP and RF in prediction for radiological outcomes. Methods 101 patients with rheumatoid arthritis and 97 control individuals were detect their A-CCP, RF and radiological joint changes, the diagnostic efficacy of the respective indicators and the combined indicators was analyzed by logistic regression model. Results Our results showed that values of A-CCP and RF were significantly higher in the RA group than in the normal group. Receiver operating characteristic curve results show that the sensitivity of RF is higher than A-CCP and radiological outcome, and specificity of CCP is higher than RA and radiological outcome, and the diagnostic ability of combination of RF, A-CCP and radiological outcome is the strongest. Furthermore, RF and A-CCP had high significant OR for radiological joint damage and progression. Conclusions Our study indicated that the combined diagnosis of RF and A-CCP with radiological outcome can improve the diagnostic efficacy of RA, and RF and A-CCP are independent predictors of radiological damage and progression.

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Section: Discussionsupporting

confidence: 59%

Clinical analysis of blood markers and imaging for diagnosis and prognosis of rheumatoid arthritis

Yang

Shengxia

et al. 2020

Preprint

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“…Therefore, XGBoost was considered more accurate in feature selection because it could select a smaller number and more efficient features. In our previous study analyzing almost the same cohort without using ML, the highest AUC was 0.67 for predicting relapse 12 , suggesting that ML using US examination and blood test data improved prediction results. This study’s sample size (n = 210) is typical among previous studies on ML applications to autoimmune diseases 17 .…”

Section: Discussionmentioning

confidence: 87%

“…Three features (wrist and MTP SMI scores and ESR) were also included in the researcher-selected features. Wrist and MTP SMI scores were reported as prognostic factors 12 , 13 , and ESR is one of RA’s most fundamental inflammation markers 1 , 4 . In the remaining seven features, ALT and height were significantly lower in patients with relapse.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies show that detecting synovitis with the US is associated with the disease progression of RA 6 – 11 . Recently, our group demonstrated the utility of superb microvascular imaging (SMI), a recent innovative type of Doppler US technology used for visualizing minute vessels with low blood flow velocity, in predicting relapse in RA patients 12 . Among joints generally assessed with the US, we showed that wrist and metatarsophalangeal (MTP) SMI scores might be vital for predicting RA relapse, depending on patients’ baseline disease activity 12 , 13 .…”

Section: Introductionmentioning

confidence: 99%

“…Recently, our group demonstrated the utility of superb microvascular imaging (SMI), a recent innovative type of Doppler US technology used for visualizing minute vessels with low blood flow velocity, in predicting relapse in RA patients 12 . Among joints generally assessed with the US, we showed that wrist and metatarsophalangeal (MTP) SMI scores might be vital for predicting RA relapse, depending on patients’ baseline disease activity 12 , 13 . However, it is unclear whether combining US data and other features, including clinical and biological markers, improves relapse prediction.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test

Matsuo

Kamada

Imamura

et al. 2022

Sci Rep

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Recent effective therapies enable most rheumatoid arthritis (RA) patients to achieve remission; however, some patients experience relapse. We aimed to predict relapse in RA patients through machine learning (ML) using data on ultrasound (US) examination and blood test. Overall, 210 patients with RA in remission at baseline were dichotomized into remission (n = 150) and relapse (n = 60) based on the disease activity at 2-year follow-up. Three ML classifiers [Logistic Regression, Random Forest, and extreme gradient boosting (XGBoost)] and data on 73 features (14 US examination data, 54 blood test data, and five data on patient information) at baseline were used for predicting relapse. The best performance was obtained using the XGBoost classifier (area under the receiver operator characteristic curve (AUC) = 0.747), compared with Random Forest and Logistic Regression (AUC = 0.719 and 0.701, respectively). In the XGBoost classifier prediction, ten important features, including wrist/metatarsophalangeal superb microvascular imaging scores, were selected using the recursive feature elimination method. The performance was superior to that predicted by researcher-selected features, which are conventional prognostic markers. These results suggest that ML can provide an accurate prediction of relapse in RA patients, and the use of predictive algorithms may facilitate personalized treatment options.

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Positive rate and prognostic significance of the superb microvascular imaging signal in joints of rheumatoid arthritis patients in remission with normal C-reactive protein levels and erythrocyte sedimentation rates

et al. 2021

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Prediction of recurrence and remission using superb microvascular imaging in rheumatoid arthritis

Cited by 7 publications

References 39 publications

Clinical analysis of blood markers and imaging for diagnosis and prognosis of rheumatoid arthritis

Clinical analysis of blood markers and imaging for diagnosis and prognosis of rheumatoid arthritis

Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test

Positive rate and prognostic significance of the superb microvascular imaging signal in joints of rheumatoid arthritis patients in remission with normal C-reactive protein levels and erythrocyte sedimentation rates

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