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Predictive value of texture analysis on lumbar MRI in patients with chronic low back pain

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Abstract

Purpose

The aim of this study was to determine whether MRI texture analysis could predict the prognosis of patients with non-specific chronic low back pain.

Methods

A prospective observational study was conducted on 100 patients with non-specific chronic low back pain, who underwent a conventional MRI, followed by rehabilitation treatment, and revisited after 6 months. Sociodemographic variables, numeric pain scale (NPS) value, and the degree of disability as measured by the Roland–Morris disability questionnaire (RMDQ), were collected. The MRI analysis included segmentation of regions of interest (vertebral endplates and intervertebral disks from L3–L4 to L5–S1, paravertebral musculature at the L4–L5 space) to extract texture variables (PyRadiomics software). The classification random forest algorithm was applied to identify individuals who would improve less than 30% in the NPS or would score more than 4 in the RMDQ at the end of the follow-up. Sensitivity, specificity, and the area under the ROC curve were calculated.

Results

The final series included 94 patients. The predictive model for classifying patients whose pain did not improve by 30% or more offered a sensitivity of 0.86, specificity 0.57, and area under the ROC curve 0.71. The predictive model for classifying patients with a RMDQ score 4 or more offered a sensitivity of 0.83, specificity of 0.20, and area under the ROC curve of 0.52.

Conclusion

The texture analysis of lumbar MRI could help identify patients who are more likely to improve their non-specific chronic low back pain through rehabilitation programs, allowing a personalized therapeutic plan to be established.

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References

  1. Vos T, Barber RM, Bell B et al (2015) Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the global burden of disease study 2013. Lancet 86(9995):743–800. https://doi.org/10.1016/S0140-6736(15)60692-4

    Article  Google Scholar 

  2. Rodriguez Reiro C (2013) Utilidad de la resonancia magnética en pacientes con dolor lumbar inespecifico. Ministerio de Sanidad, Servicios Sociales e Igualdad. Unidad de Evaluación de Tecnologías Sanitarias de la Comunidad de Madrid. Informes de evaluación de tecnologías sanitarias Tecnologías Sanitarias: 1–54. https://www.seram.es/images/site/utilidad_de_la_rnm_lumbar.pdf.

  3. Weishaupt D, Zanetti M, Hodler J, Boos N (1998) MR imaging of the lumbar spine: prevalence of intervertebral disk extrusion and sequestration, nerve root compression, end plate abnormalities, and osteoarthritis of the facet joints in asymptomatic volunteers. Radiology 209(3):661–666. https://doi.org/10.1148/radiology.209.3.9844656

    Article  CAS  PubMed  Google Scholar 

  4. Tonosu J, Oka H, Higashikawa A, Okazaki H, Tanaka S, Matsudaira K (2017) The associations between magnetic resonance imaging findings and low back pain: a 10-year longitudinal analysis. Plos One 12(11):e0188057. https://doi.org/10.1371/journal.pone.0188057

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Rahyussalim AJ, Zufar MLL, Kurniawati T (2020) Significance of the association between disc degeneration changes on imaging and low back pain: a review article. Asian Spine J 14(2):245–57. https://doi.org/10.31616/asj.2019.0046

    Article  PubMed  Google Scholar 

  6. Steffens D, Hancock MJ, Maher CG, Williams C, Jensen TS, Latimer J (2014) Does magnetic resonance imaging predict future low back pain? A systematic review. Eur J Pain 18(6):755–765. https://doi.org/10.1002/j.1532-2149.2013.00427.x

    Article  CAS  PubMed  Google Scholar 

  7. Parekh V, Jacobs MA (2016) Radiomics: a new application from established techniques. Expert Rev Precis Med drug Dev 1(2):207–226. https://doi.org/10.1080/23808993.2016.1164013

    Article  PubMed  PubMed Central  Google Scholar 

  8. Marti-Bonmati L (2019) Radiomics and imaging biomarkers in observational clinical studies with retrospective data. An RANM 136:34–42. https://doi.org/10.32440/ar.2019.136.01.rev07

    Article  Google Scholar 

  9. Mayerhoefer ME, Stelzeneder D, Bachbauer W, Welsch GH, Mamisch TC, Szczypinski P et al (2012) Quantitative analysis of lumbar intervertebral disc abnormalities at 3.0 Tesla: value of T2 texture features and geometric parameters. NMR Biomed 25(6):866–872. https://doi.org/10.1002/nbm.1803

    Article  PubMed  Google Scholar 

  10. Abdollah V, Parent EC, Dolatabadi S, Marr E, Croutze R, Wachowicz K, Kawchuk G (2021) Texture analysis in the classification of T2-weighted magnetic resonance images in persons with and without low back pain. J Orthop Res 39(10):2187–2196. https://doi.org/10.1002/jor.24930

    Article  CAS  PubMed  Google Scholar 

  11. Ketola JHJ, Inkinen SI, Karppinen J, Niinimäki J, Tervonen O, Nieminen MT (2021) T2-weighted magnetic resonance imaging texture as predictor of low back pain: a texture analysis-based classification pipeline to symptomatic and asymptomatic cases. J Orthop Res 39(11):2428–2438

    Article  PubMed  Google Scholar 

  12. Oliveira CB, Maher CG, Pinto RZ, Traeger AC, Lin CC, Chenot JF, van Tulder M, Koes BW (2018) Clinical practice guidelines for the management of non-specific low back pain in primary care: an updated overview. Eur Spine J. 27(11):2791–2803. https://doi.org/10.1007/s00586-018-5673-2

    Article  PubMed  Google Scholar 

  13. Pedregosa F, Michel V, Grisel O, Blondel M, Prettenhofer P, Weiss R et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830. https://doi.org/10.48550/arXiv.1201.0490

    Article  Google Scholar 

  14. Mckinney W (2010) Data structures for statistical computing in Python. Proc Python Sci Conf 445(1):56–61. https://doi.org/10.25080/Majora-92bf1922-00a

    Article  Google Scholar 

  15. World Medical Association (2013) World medical association declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310(20):2191–2194. https://doi.org/10.1001/jama.2013.281053

    Article  CAS  Google Scholar 

  16. Pedersen CF, Andersen MØ, Carreon LY, Eiskjaer S (2022) Applied machine learning for spine surgeons: predicting outcome for patients undergoing treatment for lumbar disc herniation using PRO data. Global Spine J 12(5):866–876. https://doi.org/10.1177/2192568220967643

    Article  PubMed  Google Scholar 

  17. Ren GR, Yu K, Xie ZY, Wang PY, Zhang W, Huang Y et al (2022) Current applications of machine learning in spine: from clinical view. Glob Spine J 12(8):1827–1840. https://doi.org/10.1177/21925682211035363

    Article  Google Scholar 

  18. Vagaska E, Litavcova A, Srotova I, Vlckova E, Kerkovsky M, Jarkovsky J et al (2019) Do lumbar magnetic resonance imaging changes predict neuropathic pain in patients with chronic non-specific low back pain? Med (Baltim) 98(17):e1537. https://doi.org/10.1097/md.0000000000015377

    Article  Google Scholar 

  19. Dunsmuir RA, Nisar S, Cruickshank JA, Loughenbury PR (2022) No correlation identified between the proportional size of a prolapsed intravertebral disc with disability or leg pain. Bone Joint J 104-B(6):715–720. https://doi.org/10.1302/0301-620X.104B6.BJJ-2021-1725.R2

    Article  PubMed  Google Scholar 

  20. Mertimo T, Karppinen J, Niinimäki J, Blanco R, Määttä J, Kankaanpää M et al (2022) Association of lumbar disc degeneration with low back pain in middle age in the northern Finland birth cohort 1966. BMC Musculoskelet Disord 23(1):359. https://doi.org/10.1186/s12891-022-05302-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wang H, Liu C, Meng Z, Zhou W, Chen T, Zhang K et al (2022) Real-world study for identifying the predictive factors of surgical intervention and the value of magnetic resonance imaging in patients with low back pain. Quant Imaging Med Surg 12(3):1830–1843. https://doi.org/10.21037/qims-21-584

    Article  PubMed  PubMed Central  Google Scholar 

  22. Jin L, Jiang C, Gu L, Jiang M, Shi Y, Qu Q et al (2023) Predictive classification system for low back pain based on unsupervised clustering. Glob Spine J 13(3):630–635. https://doi.org/10.1177/21925682211001813

    Article  Google Scholar 

  23. Shim JG, Ryu KH, Cho EA, Ahn JH, Kim HK, Lee YJ et al (2021) Machine learning approaches to predict chronic lower back pain in people aged over 50 years. Medicina (Kaunas) 57(11):1230. https://doi.org/10.3390/medicina57111230

    Article  PubMed  Google Scholar 

  24. Liew BXW, Kovacs FM, Rügamer D, Royuela A (2022) Machine learning versus logistic regression for prognostic modelling in individuals with non-specific neck pain. Eur Spine J 31(8):2082–2091. https://doi.org/10.1007/s00586-022-07188-w

    Article  PubMed  Google Scholar 

  25. Ketola JHJ, Inkinen SI, Karppinen J, Niinimäki J, Tervonen O, Nieminen MT (2021) T2-weighted magnetic resonance imaging texture as predictor of low back pain: a texture analysis-based classification pipeline to symptomatic and asymptomatic cases. J Orthop Res 39(11):2428–2438. https://doi.org/10.1002/jor.24973

    Article  PubMed  Google Scholar 

  26. Eriksson S, Waldenberg C, Torén L, Grimby-Ekman A, Brisby H, Hebelka H et al (2022) Texture analysis of magnetic resonance images enables phenotyping of potentially painful annular fissures. Spine (Phila Pa 1976) 47(5):430–437. https://doi.org/10.1097/brs.0000000000004160

    Article  PubMed  Google Scholar 

  27. Lagerstrand K, Hebelka H, Brisby H (2022) Identification of potentially painful disc fissures in magnetic resonance images using machine-learning modelling. Eur Spine J 31:1992–1999. https://doi.org/10.1007/s00586-021-07066-x

    Article  PubMed  Google Scholar 

  28. Gebrewold Y, Tesfaye B (2022) Does lumbar MRI predict degree of disability in patients with degenerative disc disease? A prospective cross-sectional study at University of Gondar comprehensive specialized hospital, north west Ethiopia, 2020. BMC Med Imaging 22(1):138. https://doi.org/10.1186/s12880-022-00866-7

    Article  PubMed  PubMed Central  Google Scholar 

  29. Banitalebi H, Aaen J, Storheim K, Negård A, Myklebust TÅ, Grotle M et al (2022) A novel MRI index for paraspinal muscle fatty infiltration: reliability and relation to pain and disability in lumbar spinal stenosis: results from a multicentre study. Eur Radiol Exp 6(1):38. https://doi.org/10.1186/s41747-022-00284-y

    Article  PubMed  PubMed Central  Google Scholar 

  30. Karran EL, McAuley JH, Traeger AC, Hillier SL, Grabherr L, Russek LN et al (2017) Can screening instruments accurately determine poor outcome risk in adults with recent onset low back pain? A systematic review and meta-analysis. BMC Med 15(1):673–686. https://doi.org/10.1186/s12916-016-0774-4

    Article  Google Scholar 

  31. Karran EL, Traeger AC, McAuley JH, Hillier SL, Yau YH, Moseley GL (2017) The value of prognostic screening for patients with low back pain in secondary care. J Pain 18(6):673–686. https://doi.org/10.1016/j.jpain.2016.12.020

    Article  PubMed  Google Scholar 

  32. de Schepper EIT, Koes BW, Oei EHG, Bierma-Zeinstra SMA, Luijsterburg PAJ (2016) The added prognostic value of MRI findings for recovery in patients with low back pain in primary care: a 1-year follow-up cohort study. Eur Spine J 25(4):1234–1241. https://doi.org/10.1007/s00586-016-4423-6

    Article  PubMed  Google Scholar 

  33. Tagliaferri SD, Angelova M, Zhao X, Owen PJ, Miller CT, Wilkin T et al (2020) Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews. NPJ Digit Med 3:93. https://doi.org/10.1038/s41746-020-0303-x

    Article  PubMed  PubMed Central  Google Scholar 

  34. Otero-Ketterer E, Peñacoba-Puente C, Ferreira Pinheiro-Araujo C, Valera-Calero JA, Ortega-Santiago R (2022) Biopsychosocial factors for chronicity in individuals with non-specific low back pain: an umbrella review. Int J Environ Res Public Health 19(16):10145. https://doi.org/10.3390/ijerph191610145

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Orthopaedic Surgery Department, Hospital Mare de Déu Dels Lliris, Alcoi (Alicante), Spain

    Vicente-Jose Climent-Peris

  2. Medical Imaging Department, Hospital Universitario y Politécnico La Fe, Valencia, Spain

    Luís Martí-Bonmatí

  3. Graphic Engineering Department, Universidad Politécnica de Valencia, Valencia, Spain

    Alejandro Rodríguez-Ortega

  4. Orthopaedic Surgery Department, Clínica Universidad de Navarra, Pamplona, Spain

    Julio Doménech-Fernández

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  1. Vicente-Jose Climent-Peris
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Correspondence to Vicente-Jose Climent-Peris.

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Climent-Peris, VJ., Martí-Bonmatí, L., Rodríguez-Ortega, A. et al. Predictive value of texture analysis on lumbar MRI in patients with chronic low back pain. Eur Spine J 32, 4428–4436 (2023). https://doi.org/10.1007/s00586-023-07936-6

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