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Prediction of knee pain improvement over two years for knee osteoarthritis using a dynamic nomogram based on MRI-derived radiomics: a proof-of-concept study

Published:November 02, 2022DOI:https://doi.org/10.1016/j.joca.2022.10.014

      Summary

      Objectives

      To develop and validate a nomogram to detect improved knee pain in osteoarthritis (OA) by integrating magnetic resonance imaging (MRI) radiomics signature of subchondral bone and clinical characteristics.

      Methods

      Participants were selected from the Vitamin D Effects on Osteoarthritis (VIDEO) study. The primary outcome was 20% improvement of knee pain score over 2 years in participants administrated either vitamin D or placebo. Radiomics features of subchondral bone and clinical characteristics from 216 participants were extracted and analyzed. The participants were randomly split into the training and validation cohorts at a ratio of 8:2. Least absolute shrinkage and selection operator (LASSO) regression was used to select features and generate radiomics signatures. The optimal radiomics signature and clinical indicators were fitted into a nomogram using multivariable logistic regression model.

      Results

      The nomogram showed favorable discrimination performance [AUCtraining, 0.79 (95% CI: 0.72–0.79), AUCvalidation, 0.83 (95% CI: 0.70–0.96)] as well as a good calibration. Additional contributing value of fusion radiomics signature to the nomogram was statistically significant (NRI, 0.23; IDI, 0.14, P < 0.001 in training cohort and NRI, 0.29; IDI, 0.18, P < 0.05 in validating cohort). Decision curve analysis confirmed the clinical usefulness of nomogram.

      Conclusion

      The radiomics-based nomogram comprising the MR radiomics signature and clinical variables achieves a favorable predictive efficacy and accuracy in differentiating improvement in knee pain among OA patients. This proof-of-concept study provides a promising way to predict clinically meaningful outcomes.

      Keywords

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      References

        • Hunter D.J.
        • March L.
        • Chew M.
        Osteoarthritis in 2020 and beyond: a Lancet commission.
        Lancet. 2020; 396: 1711-1712https://doi.org/10.1016/S0140-6736(20)32230-3
        • Kolasinski S.L.
        • Neogi T.
        • Hochberg M.C.
        • Oatis C.
        • Guyatt G.
        • Block J.
        • et al.
        American College of Rheumatology/Arthritis Foundation guideline for the management of osteoarthritis of the hand, hip, and knee.
        Arthritis Rheumatol. 2019; 72 (2020): 220-233https://doi.org/10.1002/art.41142
        • Zhu Z.
        • Li J.
        • Ruan G.
        • Wang G.
        • Huang C.
        • Ding C.
        Investigational drugs for the treatment of osteoarthritis, an update on recent developments.
        Expet Opin Invest Drugs. 2018; 27: 881-900https://doi.org/10.1080/13543784.2018.1539075
        • Bowes M.A.
        • Kacena K.
        • Alabas O.A.
        • Brett A.D.
        • Dube B.
        • Bodick N.
        • et al.
        Machine-learning, MRI bone shape and important clinical outcomes in osteoarthritis: data from the Osteoarthritis Initiative.
        Ann Rheum Dis. 2020; 80: 502-508https://doi.org/10.1136/annrheumdis-2020-217160
        • Jin X.
        • Jones G.
        • Cicuttini F.
        • Wluka A.
        • Zhu Z.
        • Han W.
        • et al.
        Effect of vitamin D supplementation on tibial cartilage volume and knee pain among patients with symptomatic knee osteoarthritis: a randomized clinical trial.
        JAMA. 2016; 315: 1005-1013https://doi.org/10.1001/jama.2016.1961
        • Jamshidi A.
        • Pelletier J.P.
        • Martel-Pelletier J.
        Machine-learning-based patient-specific prediction models for knee osteoarthritis.
        Nat Rev Rheumatol. 2019; 15: 49-60https://doi.org/10.1038/s41584-018-0130-5
        • Wang X.
        • Blizzard L.
        • Halliday A.
        • Han W.
        • Jin X.
        • Cicuttini F.
        • et al.
        Association between MRI-detected knee joint regional effusion-synovitis and structural changes in older adults: a cohort study.
        Ann Rheum Dis. 2016; 75: 519-525https://doi.org/10.1136/annrheumdis-2014-206676
        • Ding C.
        • Garnero P.
        • Cicuttini F.
        • Scott F.
        • Cooley H.
        • Jones G.
        Knee cartilage defects: association with early radiographic osteoarthritis, decreased cartilage volume, increased joint surface area and type II collagen breakdown.
        Osteoarthr Cartil. 2005; 13: 198-205https://doi.org/10.1016/j.joca.2004.11.007
        • Peterfy C.G.
        • Guermazi A.
        • Zaim S.
        • Tirman P.F.
        • Miaux Y.
        • White D.
        • et al.
        Whole-organ magnetic resonance imaging score (WORMS) of the knee in osteoarthritis.
        Osteoarthr Cartil. 2004; 12: 177-190https://doi.org/10.1016/j.joca.2003.11.003
        • Zhu Z.
        • Laslett L.L.
        • Han W.
        • Antony B.
        • Pan F.
        • Cicuttini F.
        • et al.
        Associations between MRI-detected early osteophytes and knee structure in older adults: a population-based cohort study.
        Osteoarthr Cartil. 2017; 25: 1084-1092https://doi.org/10.1016/j.joca.2017.01.007
        • Wu R.J.
        • Ma Yc
        • Yang Yh
        • Li M.Y.
        • Zheng Q.J.
        • Fu G.
        A clinical model for predicting knee replacement in early-stage knee osteoarthritis: data from osteoarthritis initiative.
        Clin Rheumatol. 2022; 41: 1199-1210https://doi.org/10.1007/s10067-021-05986-z
        • Mayerhoefer M.E.
        • Materka A.
        • Langs G.
        • Haggstrom I.
        • Szczypinski P.
        • Gibbs P.
        • et al.
        Introduction to radiomics.
        J Nucl Med. 2020; 61: 488-495https://doi.org/10.2967/jnumed.118.222893
        • Xu X.
        • Zhang H.L.
        • Liu Q.P.
        • Sun S.W.
        • Zhang J.
        • Zhu F.P.
        • et al.
        Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma.
        J Hepatol. 2019; 70: 1133-1144https://doi.org/10.1016/j.jhep.2019.02.023
        • Liu Z.
        • Wang S.
        • Dong D.
        • Wei J.
        • Fang C.
        • Zhou X.
        • et al.
        The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges.
        Theranostics. 2019; 9: 1303-1322https://doi.org/10.7150/thno.30309
        • Li H.
        • Zhu Y.
        • Burnside E.S.
        • Huang E.
        • Drukker K.
        • Hoadley K.A.
        • et al.
        Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set.
        NPJ Breast Cancer. 2016; 216012https://doi.org/10.1038/npjbcancer.2016.12
        • Pickles M.D.
        • Lowry M.
        • Gibbs P.
        Pretreatment prognostic value of dynamic contrast-enhanced magnetic resonance imaging vascular, texture, shape, and size parameters compared with traditional survival indicators obtained from locally advanced breast cancer patients.
        Invest Radiol. 2016; 51: 177-185https://doi.org/10.1097/RLI.0000000000000222
        • Kim J.-H.
        • Ko E.S.
        • Lim Y.
        • Lee K.S.
        • Han B.-K.
        • Ko E.Y.
        • et al.
        Breast cancer heterogeneity: MR imaging texture analysis and survival outcomes.
        Radiology. 2017; 282: 665-675https://doi.org/10.1148/radiol.2016160261
        • Hirvasniemi J.
        • Klein S.
        • Bierma-Zeinstra S.
        • Vernooij M.W.
        • Schiphof D.
        • Oei E.H.G.
        A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone.
        Eur Radiol. 2021; 31: 8513-8521https://doi.org/10.1007/s00330-021-07951-5
        • Teoh Y.X.
        • Lai K.W.
        • Usman J.
        • Goh S.L.
        • Mohafez H.
        • Hasikin K.
        • et al.
        Discovering knee osteoarthritis imaging features for diagnosis and prognosis: review of manual imaging grading and machine learning approaches.
        J Healthc Eng. 2022; 20224138666https://doi.org/10.1155/2022/4138666
        • Xie Y.
        • Dan Y.
        • Tao H.
        • Wang C.
        • Zhang C.
        • Wang Y.
        • et al.
        Radiomics feature analysis of cartilage and subchondral bone in differentiating knees predisposed to posttraumatic osteoarthritis after anterior cruciate ligament reconstruction from healthy knees.
        BioMed Res Int. 2021; 20214351499https://doi.org/10.1155/2021/4351499
        • Li j
        • Fu S.
        • Gong Z.
        • Zhu Z.H.
        • Zeng D.
        • Cao P.H.
        • et al.
        MRI-Based texture analysis of infrapatellar fat pad to predict knee osteoarthritis incidence.
        Radiology. 2022; 212009https://doi.org/10.1148/radiol.212009
        • Kersten P.
        • White P.J.
        • Tennant A.
        The visual analogue WOMAC 3.0 scale--internal validity and responsiveness of the VAS version.
        BMC Muscoskel Disord. 2010; 11: 80https://doi.org/10.1186/1471-2474-11-80
        • Evaluation Bellamy
        Of WOMAC 20, 50, 70 response criteria in patients treated with hylan G-F 20 for knee osteoarthritis.
        Ann Rheum Dis. 2005; 64: 881-885https://doi.org/10.1136/ard.2004.026443
        • Jin X.Z.
        • Jones G.
        • Cicuttini F.
        • Wluka A.
        • Zhu Z.H.
        • Han W.Y.
        • et al.
        Effect of vitamin D supplementation on tibial cartilage volume and knee pain among patients with symptomatic knee osteoarthritis.
        JAMA. 2016; 315: 1005-1013https://doi.org/10.1001/jama.2016.1961
        • Dore D.
        • Martens A.
        • Quinn S.
        • Ding C.
        • Winzenberg T.
        • Zhai G.
        • et al.
        Bone marrow lesions predict site-specific cartilage defect development and volume loss: a prospective study in older adults.
        Arthritis Res Ther. 2010; 12: R222https://doi.org/10.1186/ar3209
        • Altman R.D.
        • Gold G.E.
        Atlas of individual radiographic features in osteoarthritis, revised.
        Osteoarthr Cartil. 2007; 15: A1-A56https://doi.org/10.1016/j.joca.2006.11.009
        • Zhai G.
        • Blizzard L.
        • Srikanth V.
        • Ding C.
        • Cooley H.
        • Cicuttini F.
        • et al.
        Correlates of knee pain in older adults: tasmanian older adult cohort study.
        Arthritis Rheum. 2006; 55: 264-271https://doi.org/10.1002/art.21835
        • Zhu Z.
        • Jin X.
        • Wang B.
        • Wluka A.
        • Antony B.
        • Laslett L.L.
        • et al.
        Cross-sectional and longitudinal associations between serum levels of high-sensitivity C-reactive protein, knee bone marrow lesions, and knee pain in patients with knee osteoarthritis.
        Arthritis Care Res. 2016; 68 (1471–1417 https://doi.org/10.1002/acr.22834)
        • Ashrafinia S.
        Quantitative Nuclear Medicine Imaging Using Advanced Image Reconstruction and Radiomics.
        Johns Hopkins University, 2019
        • Zwanenburg A.
        • Vallieres M.
        • Abdalah M.A.
        • Aerts H.
        • Andrearczyk V.
        • Apte A.
        • et al.
        The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping.
        Radiology. 2020; 295: 328-338https://doi.org/10.1148/radiol.2020191145
        • Kim J.H.
        • Kwon Y.S.
        • Baek M.S.
        Machine learning models to predict 30-day mortality in mechanically ventilated patients.
        J Clin Med. 2021; 10: 2172https://doi.org/10.3390/jcm10102172
        • Jang Y.
        • Son J.
        • Park K.H.
        • Park S.J.
        • Jung K.H.
        Laterality classification of fundus images using interpretable deep neural network.
        J Digit Imag. 2018; 31: 923-928https://doi.org/10.1007/s10278-018-0099-2
        • Sauerbrei W.
        • Royston P.
        • Binder H.
        Selection of important variables and determination of functional form for continuous predictors in multivariable model building.
        Stat Med. 2010; 26: 5512-5528https://doi.org/10.1002/sim.3148
        • Hosmer D.W.
        • StanleyLemesbow
        Goodness of fit tests for the multiple logistic regression model.
        Commun Stat Theor Methods. 1980; 9: 1043-1069https://doi.org/10.1080/03610928008827941
        • Pencina M.J.
        • Sr R.B.D.A.
        • Steyerberg E.W.
        Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.
        Stat Med. 2011; 30: 11-21https://doi.org/10.1002/sim.4085
        • Schelbert E.B.
        • Cao J.J.
        • Sigurdsson S.
        • Aspelund T.
        • Kellman P.
        • Aletras A.H.
        • et al.
        Prevalence and prognosis of unrecognized myocardial infarction determined by cardiac magnetic resonance in older adults.
        JAMA. 2012; 308: 890-896https://doi.org/10.1001/2012.jama.11089
        • Bartley E.J.
        • Palit S.
        • Kuhn B.L.
        • Kerr K.L.
        • Rhudy J.L.
        Natural variation in testosterone is associated with hypoalgesia in healthy women.
        Clin J Pain. 2015; 31: 730-739https://doi.org/10.1097/AJP.0000000000000153
        • De Kruijf M.
        • Stolk L.
        • Zillikens M.C.
        • De Rijke Y.B.
        • Bierma-Zeinstra S.M.A.
        • Hofman A.
        • et al.
        Lower sex hormone levels are associated with more chronic musculoskeletal pain in community-dwelling elderly women.
        Pain. 2016; 157: 1425-1431https://doi.org/10.1097/j.pain.0000000000000535
        • Jin X.
        • Wang B.
        • Wang X.
        • Antony B.
        • Ding C.
        Associations between endogenous sex hormones and MRI structural changes in patients with symptomatic knee osteoarthritis.
        Osteoarthritis Cartilage. 2017; 25: 1100-1106https://doi.org/10.1016/j.joca.2017.01.015
        • Chang G.H.
        • Felson D.T.
        • Qiu S.
        • Guermazi A.
        • Capellini T.D.
        • Kolachalama V.B.
        Assessment of knee pain from MR imaging using a convolutional Siamese network.
        Eur Radiol. 2020; 30: 3538-3548https://doi.org/10.1007/s00330-020-06658-3
        • Lambin P.
        • Leijenaar R.T.H.
        • Deist T.M.
        • Peerlings J.
        • De Jong E.E.C.
        • Van Timmeren J.
        • et al.
        Radiomics: the bridge between medical imaging and personalized medicine.
        Nat Rev Clin Oncol. 2017; 14: 749-762https://doi.org/10.1038/nrclinonc.2017.141
        • Fiz F.
        • Vigano L.
        • Gennaro N.
        • Costa G.
        • Torzilli G.
        Radiomics of liver metastases: a systematic review.
        Cancers. 2020; 12: 2881https://doi.org/10.3390/cancers12102881
        • Mayerhoefer M.E.
        • Materka A.
        • Langs G.
        • Hggstrm I.
        • Szczypiński P.
        • Gibbs P.
        • et al.
        Introduction to radiomics.
        J Nucl Med. 2020; 61: 488-495https://doi.org/10.2967/jnumed.118.222893
        • Jiang Y.
        • Chen C.
        • Xie J.
        • Wang W.
        • Zha X.
        • Lv W.
        • et al.
        Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer.
        EBioMedicine. 2018; 36: 171-182https://doi.org/10.1016/j.ebiom.2018.09.007
        • Huang Y.
        • Liu Z.
        • He L.
        • Chen X.
        • Pan D.
        • Ma Z.
        • et al.
        Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non–small cell lung cancer.
        Radiology. 2016; 281: 947-957https://doi.org/10.1148/radiol.2016152234
        • Halilaj E.
        • Le Y.
        • Hicks J.L.
        • Hastie T.J.
        • Delp S.L.
        Modeling and predicting osteoarthritis progression: data from the osteoarthritis initiative.
        Osteoarthr Cartil. 2018; 26: 1643-1650https://doi.org/10.1016/j.joca.2018.08.003
        • Bonakdari H.
        • Jamshidi A.
        • Pelletier J.P.
        • Abram F.
        • Tardif G.
        • Martel-Pelletier J.
        A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening.
        Ther Adv Musculoskelet Dis. 2021; 13 (1759720X21993254)https://doi.org/10.1177/1759720X21993254
        • Brahim A.
        • Jennane R.
        • Riad R.
        • Janvier T.
        • Khedher L.
        • Toumi H.
        • et al.
        A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: data from the OsteoArthritis Initiative.
        Comput Med Imag Graph. 2019; 73: 11-18https://doi.org/10.1016/j.compmedimag.2019.01.007
        • Jin C.
        • Cao J.
        • Cai Y.
        • Wang L.
        • Liu K.
        • Shen W.
        • et al.
        A nomogram for predicting the risk of invasive pulmonary adenocarcinoma for patients with solitary peripheral subsolid nodules.
        J Thorac Cardiovasc Surg. 2017; 153 (e1): 462-469https://doi.org/10.1016/j.jtcvs.2016.10.019
        • Zhang J.
        • Fan J.
        • Yin R.
        • Geng L.
        • Shen H.
        A nomogram to predict overall survival of patients with early stage non-small cell lung cancer.
        J Thorac Dis. 2019; 11: 5407-5416https://doi.org/10.21037/jtd.2019.11.53
        • Sun W.
        • Li G.
        • Song Y.
        • Zhu Z.
        • Yang Z.
        • Chen Y.
        • et al.
        A web based dynamic MANA Nomogram for predicting the malignant cerebral edema in patients with large hemispheric infarction.
        BMC Neurol. 2020; 20: 360https://doi.org/10.1186/s12883-020-01935-6
        • Zheng S.
        • Jin X.
        • Cicuttini F.
        • Wang X.
        • Zhu Z.
        • Wluka A.
        • et al.
        Maintaining vitamin D sufficiency is associated with improved structural and symptomatic outcomes in knee osteoarthritis.
        Am J Med. 2017; 130: 1211-1218https://doi.org/10.1016/j.amjmed.2017.04.038
        • Zhou Z.R.
        • Wang W.W.
        • Li Y.
        • Jin K.R.
        • Wang X.Y.
        • Wang Z.W.
        • et al.
        In-depth mining of clinical data: the construction of clinical prediction model with R.
        Ann Transl Med. 2019; 7: 796https://doi.org/10.21037/atm.2019.08.63
        • Hazra A.
        • Gogtay N.
        Biostatistics series module 6: correlation and linear regression.
        Indian J Dermatol. 2016; 61: 593-601https://doi.org/10.4103/0019-5154.193662