Logistic Regression and Elastic Net Regularization for the Diagnosis of Fibromyalgia (LEDF)
12 de setembro de 2019 atualizado por: Dinesh Kumbhare, Toronto Rehabilitation Institute
Logistic Regression and Elastic Net Regularization for the Diagnosis of Fibromyalgia: A Quantitative Approach Using B-Mode Ultrasound
This study will utilize ultrasound image texture variables to construct an elastic net regularized, logistic regression model to differentiate between healthy and Fibromyalgia patients.
The collected ultrasound data will be from participants who are healthy, and from participants who have Fibromyalgia.
The predicted performance accuracy of the diagnostic model will be validated and this will confirm or deny the hypothesis that differentiation between the two cohorts is possible.
Visão geral do estudo
Status
Concluído
Condições
Intervenção / Tratamento
Descrição detalhada
Fibromyalgia (FM) diagnosis remains a challenge for clinicians due to a lack of objective diagnostic tools.
One proposed solution is the use of quantitative ultrasound (US) techniques, such as image texture analysis, which has demonstrated discriminatory capabilities with other chronic pain conditions.
The investigators propose the use of US image texture variables to construct an elastic net regularized, logistic regression model, for differentiating between the trapezius muscle in the healthy and FM patients.
162 Ultrasound videos of the right and left trapezius muscle were acquired from healthy participants and participants with FM.
The videos will then be put through a mutli-step processing pipe including converting them into skeletal muscle regions of interest (ROI).
The ROI's will be then filtered by an algorithm utilizing the complex wavelet structural similarity index (CW-SSIM), which removes ROI's that are too similar to one another.
Eighty-eight texture variables will be extracted from the ROI's, which will be used in nested cross-validation to construct a logistic regression model with and without elastic net regularization.
The generalized performance accuracy of both models will be estimated and confirmed with a final validation on a holdout test set.
Depending on the predicted, generalized performance accuracy it will be validated or not by the final, holdout test set (confirming the model construction is accurate).
These models should then confirm or deny the hypothesis that a regularized logistic regression model built on ultrasound texture features can accurately differentiate between healthy trapezius muscle and that of patients with FM.
Tipo de estudo
Observacional
Inscrição (Real)
81
Contactos e Locais
Esta seção fornece os detalhes de contato para aqueles que conduzem o estudo e informações sobre onde este estudo está sendo realizado.
Locais de estudo
-
-
Ontario
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Toronto, Ontario, Canadá, M5G2A2
- Toronto Rehabilitation Institute
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Critérios de participação
Os pesquisadores procuram pessoas que se encaixem em uma determinada descrição, chamada de critérios de elegibilidade. Alguns exemplos desses critérios são a condição geral de saúde de uma pessoa ou tratamentos anteriores.
Critérios de elegibilidade
Idades elegíveis para estudo
20 anos a 65 anos (Adulto, Adulto mais velho)
Aceita Voluntários Saudáveis
Sim
Gêneros Elegíveis para o Estudo
Tudo
Método de amostragem
Amostra Não Probabilística
População do estudo
Patients diagnosed with Fibromyalgia and healthy age-matched controls.
Descrição
Inclusion Criteria:
- gender independent; chronic widespread pain, fitting the 2016 FM criteria, absence of myofascial pain syndrome trigger points and between the ages of 20 and 65 years (44.3 ± 13.9 years).
- Healthy asymptomatic volunteers who were age matched (n = 17) with no physical complaints or abnormality on physical examination also participated.
Exclusion Criteria:
- Participants were excluded if they demonstrated clinical evidence of another cause for widespread pain, such as polymyositis, dermatomyositis, endocrine disorders, etc. None of the participants had performed any physical exercise during the two to three days prior to entry into the study.
Plano de estudo
Esta seção fornece detalhes do plano de estudo, incluindo como o estudo é projetado e o que o estudo está medindo.
Como o estudo é projetado?
Detalhes do projeto
Coortes e Intervenções
Grupo / Coorte |
Intervenção / Tratamento |
|---|---|
|
Fibromyalgia
Patients who display symptoms and have a history of Fibromyalgia, between 20-65 years of age.
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B-mode ultrasound pictures of the upper Trapezius were collected from both left and right sides.
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|
Healthy Controls
Age-matched, healthy controls, between 20-65 years of age who present no signs of chronic pain.
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B-mode ultrasound pictures of the upper Trapezius were collected from both left and right sides.
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O que o estudo está medindo?
Medidas de resultados primários
Medida de resultado |
Descrição da medida |
Prazo |
|---|---|---|
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Ultrasound Image Texture Variables
Prazo: 1 hour
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91 statistical image texture variables are extracted from the B mode ultrasound images from both cohorts in order to construct a diagnostic model.
The texture variables will be extracted using MATLAB.
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1 hour
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Fibromyalgia Diagnostic Criteria
Prazo: 10 minutes
|
This evaluates symptoms related to Fibromyalgia and determines a score to assess the severity.
This score is comprised of the Widespread Pain Index(WPI), which quantifies the regions of pain, and the Symptom Severity Scale(SSS), which measures qualitative aspects of pain such as fatigue and cognitive symptoms.
The WPI scale ranges from 0-19 (0- no areas of body pain, 19- all body regions have pain), whereas the SSS ranges from 0-12 (0-no qualitative aspects of pain, 12-many qualitative aspects of pain).
This criteria was evaluated on each patient to determine which cohort they belong to.
According to the Fibromyalgia Diagnostic Criteria, one is diagnosed with Fibromyalgia if they have a WPI score of 7 or higher, and a SSS score of 5 or higher.
Fibromyalgia is also diagnosed with a score of 3-6 on the WPI score, and a score of 9 or higher on the SSS score.
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10 minutes
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Central Sensitization Inventory
Prazo: 10 minutes
|
This is a self reported outcome measure designed to identify patients that experience central sensitization.
It involves 25 questions which include symptomatic experiences.
The subject must answer on a scale of 0(never) to 5(always) corresponding to how often they experience these.
The maximum score is 100 and a score of more than 40 indicates the presence of Central Sensitization.
This criteria was evaluated on each patient to determine which cohort they belong to.
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10 minutes
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Colaboradores e Investigadores
É aqui que você encontrará pessoas e organizações envolvidas com este estudo.
Patrocinador
Investigadores
- Investigador principal: Dinesh Kumbhare, MD,PhD, Toronto Rehabilitation Institute
Publicações e links úteis
A pessoa responsável por inserir informações sobre o estudo fornece voluntariamente essas publicações. Estes podem ser sobre qualquer coisa relacionada ao estudo.
Publicações Gerais
- Wolfe F, Clauw DJ, Fitzcharles MA, Goldenberg DL, Hauser W, Katz RL, Mease PJ, Russell AS, Russell IJ, Walitt B. 2016 Revisions to the 2010/2011 fibromyalgia diagnostic criteria. Semin Arthritis Rheum. 2016 Dec;46(3):319-329. doi: 10.1016/j.semarthrit.2016.08.012. Epub 2016 Aug 30.
- Wolfe F, Ross K, Anderson J, Russell IJ, Hebert L. The prevalence and characteristics of fibromyalgia in the general population. Arthritis Rheum. 1995 Jan;38(1):19-28. doi: 10.1002/art.1780380104.
- Kumbhare DA, Ahmed S, Behr MG, Noseworthy MD. Quantitative Ultrasound Using Texture Analysis of Myofascial Pain Syndrome in the Trapezius. Crit Rev Biomed Eng. 2018;46(1):1-31. doi: 10.1615/CritRevBiomedEng.2017024947.
- Gittins R, Howard M, Ghodke A, Ives TJ, Chelminski P. The Accuracy of a Fibromyalgia Diagnosis in General Practice. Pain Med. 2018 Mar 1;19(3):491-498. doi: 10.1093/pm/pnx155.
- Schaefer C, Mann R, Masters ET, Cappelleri JC, Daniel SR, Zlateva G, McElroy HJ, Chandran AB, Adams EH, Assaf AR, McNett M, Mease P, Silverman S, Staud R. The Comparative Burden of Chronic Widespread Pain and Fibromyalgia in the United States. Pain Pract. 2016 Jun;16(5):565-79. doi: 10.1111/papr.12302. Epub 2015 May 16.
- Ablin JN, Wolfe F. A Comparative Evaluation of the 2011 and 2016 Criteria for Fibromyalgia. J Rheumatol. 2017 Aug;44(8):1271-1276. doi: 10.3899/jrheum.170095. Epub 2017 Jun 1.
- U.S. Department of Health and Human Services Food and Drug Administration/Centre for Drug Evaluation and Research. Guidance for Industry and FDA Staff Qualification Process for Drug Development Tools. Silver Spring, MD: Author; 2014
- Kravis MM, Munk PL, McCain GA, Vellet AD, Levin MF. MR imaging of muscle and tender points in fibromyalgia. J Magn Reson Imaging. 1993 Jul-Aug;3(4):669-70. doi: 10.1002/jmri.1880030418.
- Meenagh G, Sakellariou G, Iagnocco A, Delle Sedie A, Riente L, Filippucci E, Di Geso L, Grassi W, Bombardieri S, Valesini G, Montecucco C. Ultrasound imaging for the rheumatologist XXXIX. Sonographic assessment of the hip in fibromyalgia patients. Clin Exp Rheumatol. 2012 May-Jun;30(3):319-21. Epub 2012 Jun 26.
- Bendtsen L, Norregaard J, Jensen R, Olesen J. Evidence of qualitatively altered nociception in patients with fibromyalgia. Arthritis Rheum. 1997 Jan;40(1):98-102. doi: 10.1002/art.1780400114.
- MathWorks. Image Processing Toolbox., Release 2018a, The MathWorks Inc.,Natick, Massachusetts, United States
- Sampat MP, Wang Z, Gupta S, Bovik AC, Markey MK. Complex wavelet structural similarity: a new image similarity index. IEEE Trans Image Process. 2009 Nov;18(11):2385-401. doi: 10.1109/TIP.2009.2025923. Epub 2009 Jun 23.
- Behr M, Noseworthy M, Kumbhare D. Feasibility of a Support Vector Machine Classifier for Myofascial Pain Syndrome: Diagnostic Case-Control Study. J Ultrasound Med. 2019 Aug;38(8):2119-2132. doi: 10.1002/jum.14909. Epub 2019 Jan 7.
- Haralick, R. M., & Shanmugam, K. Textural features for image classification. IEEE Transactions on systems, man, and cybernetics. 1973;SMC-3(6):610-621.
- Galloway, M. M. Texture classification using gray level run length. Computer graphics and image processing. 1975;4(2):172-179.
- Zou, H., & Hastie, T. Regularization and variable selection via the elastic net. Journal of the royal statistical society: series B (statistical methodology) 2005;67(2):301-320.
- MathWorks. Statistics and Machine Learning Toolbox., Release 2018a, The MathWorks Inc.,Natick, Massachusetts, United States
- Jalalian A, Mashohor SB, Mahmud HR, Saripan MI, Ramli AR, Karasfi B. Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clin Imaging. 2013 May-Jun;37(3):420-6. doi: 10.1016/j.clinimag.2012.09.024. Epub 2012 Nov 13.
- Virmani, J., Kumar, V., Kalra, N., & Khandelwal, N. Prediction of liver cirrhosis based on multiresolution texture descriptors from B-mode ultrasound. International Journal of Convergence Computing 2013;1(1):19-37.
- Xian, G. M. An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM. Expert Systems with Applications 2010;37(10):6737-6741.
- Bishop, C. M. Pattern recognition and machine learning. New York, NY: Springer-Verlag: 2006. p. 205-207.
- Sarle, W. S. Stopped training and other remedies for overfitting. Computing science and statistics, 1996:352-360.
Datas de registro do estudo
Essas datas acompanham o progresso do registro do estudo e os envios de resumo dos resultados para ClinicalTrials.gov. Os registros do estudo e os resultados relatados são revisados pela National Library of Medicine (NLM) para garantir que atendam aos padrões específicos de controle de qualidade antes de serem publicados no site público.
Datas Principais do Estudo
Início do estudo (Real)
1 de setembro de 2018
Conclusão Primária (Real)
6 de setembro de 2019
Conclusão do estudo (Real)
6 de setembro de 2019
Datas de inscrição no estudo
Enviado pela primeira vez
11 de setembro de 2019
Enviado pela primeira vez que atendeu aos critérios de CQ
12 de setembro de 2019
Primeira postagem (Real)
13 de setembro de 2019
Atualizações de registro de estudo
Última Atualização Postada (Real)
17 de setembro de 2019
Última atualização enviada que atendeu aos critérios de controle de qualidade
12 de setembro de 2019
Última verificação
1 de setembro de 2019
Mais Informações
Termos relacionados a este estudo
Palavras-chave
Termos MeSH relevantes adicionais
Outros números de identificação do estudo
- FibromyalgiaDiagnosis
Plano para dados de participantes individuais (IPD)
Planeja compartilhar dados de participantes individuais (IPD)?
NÃO
Informações sobre medicamentos e dispositivos, documentos de estudo
Estuda um medicamento regulamentado pela FDA dos EUA
Não
Estuda um produto de dispositivo regulamentado pela FDA dos EUA
Não
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