The Value of Quantitative Musculoskeletal Imaging

Abstract

Musculoskeletal imaging is mainly based on the subjective and qualitative analysis of imaging examinations. However, integration of quantitative assessment of imaging data could increase the value of imaging in both research and clinical practice. Some imaging modalities, such as perfusion magnetic resonance imaging (MRI), diffusion MRI, or T2 mapping, are intrinsically quantitative. But conventional morphological imaging can also be analyzed through the quantification of various parameters. The quantitative data retrieved from imaging examinations can serve as biomarkers and be used to support diagnosis, determine patient prognosis, or monitor therapy.

We focus on the value, or clinical utility, of quantitative imaging in the musculoskeletal field. There is currently a trend to move from volume- to value-based payments. This review contains definitions and examines the role that quantitative imaging may play in the implementation of value-based health care. The influence of artificial intelligence on the value of quantitative musculoskeletal imaging is also discussed.

Keywords

quantitative imaging - musculoskeletal imaging - imaging biomarkers - clinical utility - value-based health care

Publication History

Article published online:
29 September 2020

• References

• 1 Buckler AJ, Bresolin L, Dunnick NR, Sullivan DC. Group. A collaborative enterprise for multi-stakeholder participation in the advancement of quantitative imaging. Radiology 2011; 258 (03) 906-914
• 2 Porter ME. What is value in health care?. N Engl J Med 2010; 363 (26) 2477-2481
• 3 Sullivan DC, Obuchowski NA, Kessler LG. , et al; RSNA-QIBA Metrology Working Group. Metrology standards for quantitative imaging biomarkers. Radiology 2015; 277 (03) 813-825
• 4 Peterfy CG, Guermazi A, Zaim S. , et al. Whole-Organ Magnetic Resonance Imaging Score (WORMS) of the knee in osteoarthritis. Osteoarthritis Cartilage 2004; 12 (03) 177-190
• 5 Hunter DJ, Lo GH, Gale D, Grainger AJ, Guermazi A, Conaghan PG. The reliability of a new scoring system for knee osteoarthritis MRI and the validity of bone marrow lesion assessment: BLOKS (Boston Leeds Osteoarthritis Knee Score). Ann Rheum Dis 2008; 67 (02) 206-211
• 6 Lambin P, Leijenaar RTH, Deist TM. , et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017; 14 (12) 749-762
• 7 Litjens G, Kooi T, Bejnordi BE. , et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42: 60-88
• 8 Ahlawat S, Fritz J, Morris CD, Fayad LM. Magnetic resonance imaging biomarkers in musculoskeletal soft tissue tumors: review of conventional features and focus on nonmorphologic imaging. J Magn Reson Imaging 2019; 50 (01) 11-27
• 9 Fayad LM, Jacobs MA, Wang X, Carrino JA, Bluemke DA. Musculoskeletal tumors: how to use anatomic, functional, and metabolic MR techniques. Radiology 2012; 265 (02) 340-356
• 10 Pettersson H, Gillespy III T, Hamlin DJ. , et al. Primary musculoskeletal tumors: examination with MR imaging compared with conventional modalities. Radiology 1987; 164 (01) 237-241
• 11 Zajick Jr DC, Morrison WB, Schweitzer ME, Parellada JA, Carrino JA. Benign and malignant processes: normal values and differentiation with chemical shift MR imaging in vertebral marrow. Radiology 2005; 237 (02) 590-596
• 12 Kumar NM, Ahlawat S, Fayad LM. Chemical shift imaging with in-phase and opposed-phase sequences at 3 T: what is the optimal threshold, measurement method, and diagnostic accuracy for characterizing marrow signal abnormalities?. Skeletal Radiol 2018; 47 (12) 1661-1671
• 13 Ahlawat S, Khandheria P, Subhawong TK, Fayad LM. Differentiation of benign and malignant skeletal lesions with quantitative diffusion weighted MRI at 3T. Eur J Radiol 2015; 84 (06) 1091-1097
• 14 Oka K, Yakushiji T, Sato H, Hirai T, Yamashita Y, Mizuta H. The value of diffusion-weighted imaging for monitoring the chemotherapeutic response of osteosarcoma: a comparison between average apparent diffusion coefficient and minimum apparent diffusion coefficient. Skeletal Radiol 2010; 39 (02) 141-146
• 15 Soldatos T, Ahlawat S, Montgomery E, Chalian M, Jacobs MA, Fayad LM. Multiparametric assessment of treatment response in high-grade soft-tissue sarcomas with anatomic and functional MR imaging sequences. Radiology 2016; 278 (03) 831-840
• 16 Sujlana P, Skrok J, Fayad LM. Review of dynamic contrast-enhanced MRI: technical aspects and applications in the musculoskeletal system. J Magn Reson Imaging 2018; 47 (04) 875-890
• 17 Lang N, Yuan H, Yu HJ, Su MY. Diagnosis of spinal lesions using heuristic and pharmacokinetic parameters measured by dynamic contrast-enhanced MRI. Acad Radiol 2017; 24 (07) 867-875
• 18 Alic L, van Vliet M, Wielopolski PA. , et al. Regional heterogeneity changes in DCE-MRI as response to isolated limb perfusion in experimental soft-tissue sarcomas. Contrast Media Mol Imaging 2013; 8 (04) 340-349
• 19 Nagata S, Nishimura H, Uchida M. , et al. Diffusion-weighted imaging of soft tissue tumors: usefulness of the apparent diffusion coefficient for differential diagnosis. Radiat Med 2008; 26 (05) 287-295
• 20 Razek A, Nada N, Ghaniem M, Elkhamary S. Assessment of soft tissue tumours of the extremities with diffusion echoplanar MR imaging. Radiol Med (Torino) 2012; 117 (01) 96-101
• 21 Lee SY, Jee WH, Jung JY. , et al. Differentiation of malignant from benign soft tissue tumours: use of additive qualitative and quantitative diffusion-weighted MR imaging to standard MR imaging at 3.0 T. Eur Radiol 2016; 26 (03) 743-754
• 22 Del Grande F, Ahlawat S, Subhawong T, Fayad LM. Characterization of indeterminate soft tissue masses referred for biopsy: what is the added value of contrast imaging at 3.0 Tesla?. J Magn Reson Imaging 2017; 45 (02) 390-400
• 23 van Rijswijk CS, Geirnaerdt MJ, Hogendoorn PC. , et al. Soft-tissue tumors: value of static and dynamic gadopentetate dimeglumine-enhanced MR imaging in prediction of malignancy. Radiology 2004; 233 (02) 493-502
• 24 Del Grande F, Subhawong T, Weber K, Aro M, Mugera C, Fayad LM. Detection of soft-tissue sarcoma recurrence: added value of functional MR imaging techniques at 3.0 T. Radiology 2014; 271 (02) 499-511
• 25 Fayad LM, Barker PB, Bluemke DA. Molecular characterization of musculoskeletal tumors by proton MR spectroscopy. Semin Musculoskelet Radiol 2007; 11 (03) 240-245
• 26 Fayad LM, Wang X, Salibi N. , et al. A feasibility study of quantitative molecular characterization of musculoskeletal lesions by proton MR spectroscopy at 3 T. AJR Am J Roentgenol 2010; 195 (01) W69-75
• 27 Subhawong TK, Wang X, Durand DJ. , et al. Proton MR spectroscopy in metabolic assessment of musculoskeletal lesions. AJR Am J Roentgenol 2012; 198 (01) 162-172
• 28 Fayad LM, Wang X, Blakeley JO. , et al. Characterization of peripheral nerve sheath tumors with 3T proton MR spectroscopy. AJNR Am J Neuroradiol 2014; 35 (05) 1035-1041
• 29 Oei EH, van Tiel J, Robinson WH, Gold GE. Quantitative radiologic imaging techniques for articular cartilage composition: toward early diagnosis and development of disease-modifying therapeutics for osteoarthritis. Arthritis Care Res (Hoboken) 2014; 66 (08) 1129-1141
• 30 Tiulpin A, Klein S, Bierma-Zeinstra SMA. , et al. Multimodal machine learning-based knee osteoarthritis progression prediction from plain radiographs and clinical data. Sci Rep 2019; 9 (01) 20038
• 31 Chen P, Gao L, Shi X, Allen K, Yang L. Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss. Comput Med Imaging Graph 2019; 75: 84-92
• 32 Jang S, Graffy PM, Ziemlewicz TJ, Lee SJ, Summers RM, Pickhardt PJ. Opportunistic osteoporosis screening at routine abdominal and thoracic CT: normative L1 trabecular attenuation values in more than 20 000 adults. Radiology 2019; 291 (02) 360-367
• 33 Lee SJ, Pickhardt PJ. Opportunistic screening for osteoporosis using body CT scans obtained for other indications: the UW experience. Clin Rev Bone Miner Metab 2017; 15: 128-137
• 34 Pickhardt PJ, Pooler BD, Lauder T, del Rio AM, Bruce RJ, Binkley N. Opportunistic screening for osteoporosis using abdominal computed tomography scans obtained for other indications. Ann Intern Med 2013; 158 (08) 588-595
• 35 Pickhardt PJ, Lee LJ, del Rio AM. , et al. Simultaneous screening for osteoporosis at CT colonography: bone mineral density assessment using MDCT attenuation techniques compared with the DXA reference standard. J Bone Miner Res 2011; 26 (09) 2194-2203
• 36 Pickhardt PJ, Lee SJ, Liu J. , et al. Population-based opportunistic osteoporosis screening: Validation of a fully automated CT tool for assessing longitudinal BMD changes. Br J Radiol 2019; 92 (1094): 20180726
• 37 Graffy PM, Lee SJ, Ziemlewicz TJ, Pickhardt PJ. Prevalence of vertebral compression fractures on routine CT Scans according to L1 trabecular attenuation: determining relevant thresholds for opportunistic osteoporosis screening. AJR Am J Roentgenol 2017; 209 (03) 491-496
• 38 Lee SJ, Graffy PM, Zea RD, Ziemlewicz TJ, Pickhardt PJ. Future osteoporotic fracture risk related to lumbar vertebral trabecular attenuation measured at routine body CT. J Bone Miner Res 2018; 33 (05) 860-867
• 39 Murray TE, Williams D, Lee MJ. Osteoporosis, obesity, and sarcopenia on abdominal CT: a review of epidemiology, diagnostic criteria, and management strategies for the reporting radiologist. Abdom Radiol (NY) 2017; 42 (09) 2376-2386
• 40 Graffy PM, Liu J, Pickhardt PJ, Burns JE, Yao J, Summers RM. Deep learning-based muscle segmentation and quantification at abdominal CT: application to a longitudinal adult screening cohort for sarcopenia assessment. Br J Radiol 2019; 92 (1100): 20190327
• 41 Lee SJ, Liu J, Yao J, Kanarek A, Summers RM, Pickhardt PJ. Fully automated segmentation and quantification of visceral and subcutaneous fat at abdominal CT: application to a longitudinal adult screening cohort. Br J Radiol 2018; 91 (1089): 20170968
• 42 Han TS, Lean MEJ. A clinical perspective of obesity, metabolic syndrome and cardiovascular disease. JRSM Cardiovasc Dis 2016; 5: 2048004016633371
• 43 Graffy PM, Pickhardt PJ. Quantification of hepatic and visceral fat by CT and MR imaging: relevance to the obesity epidemic, metabolic syndrome and NAFLD. Br J Radiol 2016; 89 (1062): 20151024
• 44 Pickhardt PJ, Jee Y, O'Connor SD, del Rio AM. Visceral adiposity and hepatic steatosis at abdominal CT: association with the metabolic syndrome. AJR Am J Roentgenol 2012; 198 (05) 1100-1107
• 45 Abraham TM, Pedley A, Massaro JM, Hoffmann U, Fox CS. Association between visceral and subcutaneous adipose depots and incident cardiovascular disease risk factors. Circulation 2015; 132 (17) 1639-1647
• 46 Boutin RD, Yao L, Canter RJ, Lenchik L. Sarcopenia: current concepts and imaging implications. AJR Am J Roentgenol 2015; 205 (03) W255-W266
• 47 Lenchik L, Boutin RD. Sarcopenia: beyond muscle atrophy and into the new frontiers of opportunistic imaging, precision medicine, and machine learning. Semin Musculoskelet Radiol 2018; 22 (03) 307-322
• 48 Beaman FD, von Herrmann PF, Kransdorf MJ. , et al; Expert Panel on Musculoskeletal Imaging. ACR Appropriateness Criteria^® suspected osteomyelitis, septic arthritis, or soft tissue infection (excluding spine and diabetic foot). J Am Coll Radiol 2017; 14 (5S): S326-S337
• 49 Bhojwani N, Szpakowski P, Partovi S. , et al. Diffusion-weighted imaging in musculoskeletal radiology-clinical applications and future directions. Quant Imaging Med Surg 2015; 5 (05) 740-753
• 50 Nguyen A, Ledoux JB, Omoumi P, Becce F, Forget J, Federau C. Selective microvascular muscle perfusion imaging in the shoulder with intravoxel incoherent motion (IVIM). Magn Reson Imaging 2017; 35: 91-97
• 51 Liao D, Xie L, Han Y. , et al. Dynamic contrast-enhanced magnetic resonance imaging for differentiating osteomyelitis from acute neuropathic arthropathy in the complicated diabetic foot. Skeletal Radiol 2018; 47 (10) 1337-1347
• 52 Ozgen B, Oguz KK, Cila A. Diffusion MR imaging features of skull base osteomyelitis compared with skull base malignancy. AJNR Am J Neuroradiol 2011; 32 (01) 179-184
• 53 European Society of Radiology (ESR). ESR concept paper on value-based radiology. Insights Imaging 2017; 8 (05) 447-454
• 54 Tsevat J, Moriates C. Value-based health care meets cost-effectiveness analysis. Ann Intern Med 2018; 169 (05) 329-332
• 55 Roberts ET, Zaslavsky AM, McWilliams JM. The value-based payment modifier: program outcomes and implications for disparities. Ann Intern Med 2018; 168 (04) 255-265
• 56 Levin DC, Rao VM, Parker L, Frangos AJ, Sunshine JH. Bending the curve: the recent marked slowdown in growth of noninvasive diagnostic imaging. AJR Am J Roentgenol 2011; 196 (01) W25-W29
• 57 Papanicolas I, Woskie LR, Jha AK. Health care spending in the United States and other high-income countries. JAMA 2018; 319 (10) 1024-1039
• 58 Jones DN, Thomas MJ, Mandel CJ. , et al. Where failures occur in the imaging care cycle: lessons from the radiology events register. J Am Coll Radiol 2010; 7 (08) 593-602
• 59 Sarwar A, Boland G, Monks A, Kruskal JB. Metrics for radiologists in the era of value-based health care delivery. Radiographics 2015; 35 (03) 866-876
• 60 Fryback DG, Thornbury JR. The efficacy of diagnostic imaging. Med Decis Making 1991; 11 (02) 88-94
• 61 Gazelle GS, Kessler L, Lee DW. , et al; Working Group on Comparative Effectiveness Research for Imaging. A framework for assessing the value of diagnostic imaging in the era of comparative effectiveness research. Radiology 2011; 261 (03) 692-698
• 62 Hirschmann A, Cyriac J, Stieltjes B, Kober T, Richiardi J, Omoumi P. Artificial intelligence in musculoskeletal imaging: review of current literature, challenges, and trends. Semin Musculoskelet Radiol 2019; 23 (03) 304-311
• 63 Bach Cuadra M, Favre J, Omoumi P. Quantification in musculoskeletal imaging using computational analysis and machine learning: segmentation and radiomics. Semin Musculoskelet Radiol 2020; 24 (01) 50-64 (in press)
• 64 Omoumi PFM. Imaging Informatics: Artificial intelligence, structured reporting and beyond. In: Veira AL. , ed. Imaging of Motion & Performance: Stress & Strain. Vienna, Austria: European Society of Radiology; 2019: 197-201