11 September 2015 Pixel-based approach to assess contrast-enhanced ultrasound kinetics parameters for differential diagnosis of rheumatoid arthritis
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Abstract
Inflammatory rheumatic diseases are the leading causes of disability and constitute a frequent medical disorder, leading to inability to work, high comorbidity, and increased mortality. The standard for diagnosing and differentiating arthritis is based on clinical examination, laboratory exams, and imaging findings, such as synovitis, bone edema, or joint erosions. Contrast-enhanced ultrasound (CEUS) examination of the small joints is emerging as a sensitive tool for assessing vascularization and disease activity. Quantitative assessment is mostly performed at the region of interest level, where the mean intensity curve is fitted with an exponential function. We showed that using a more physiologically motivated perfusion curve, and by estimating the kinetic parameters separately pixel by pixel, the quantitative information gathered is able to more effectively characterize the different perfusion patterns. In particular, we demonstrated that a random forest classifier based on pixelwise quantification of the kinetic contrast agent perfusion features can discriminate rheumatoid arthritis from different arthritis forms (psoriatic arthritis, spondyloarthritis, and arthritis in connective tissue disease) with an average accuracy of 97%. On the contrary, clinical evaluation (DAS28), semiquantitative CEUS assessment, serological markers, or region-based parameters do not allow such a high diagnostic accuracy.
© 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
Gaia Rizzo, Gaia Rizzo, Bernd Raffeiner, Bernd Raffeiner, Alessandro Coran, Alessandro Coran, Luca Ciprian, Luca Ciprian, Ugo Fiocco, Ugo Fiocco, Costantino Botsios, Costantino Botsios, Roberto Stramare, Roberto Stramare, Enrico Grisan, Enrico Grisan, } "Pixel-based approach to assess contrast-enhanced ultrasound kinetics parameters for differential diagnosis of rheumatoid arthritis," Journal of Medical Imaging 2(3), 034503 (11 September 2015). https://doi.org/10.1117/1.JMI.2.3.034503 . Submission:
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