The Role of Perfusion Magnetic Resonance Imaging in the Discrimination of Benign vs Malign Character of Musculoskeletal Tumors

Year 2024, Volume: 8 Issue: 3, 603 - 610, 30.09.2024

Serap Sarı , Ekrem Anıl Sarı , Ali Balcı , Hasan Havıtçıoğlu , Burçin Tuna

https://doi.org/10.30621/jbachs.1450436

Abstract

Purpose: There may be difficulties in diagnosing musculoskeletal system tumors with Magnetic Resonance Imaging (MRI). Advanced MRI imaging techniques may contribute to noninvasive diagnosis.The aim of our study was to evaluate the performance of perfusion magnetic resonance imaging quantitative data in the differentiation of benign and malignant musculoskeletal tumors.
Material and Methods: Thirty-six histologically proven patients were included in the study retrospectively. All patients underwent 1.5-T perfusion MRI (magnetic resonance imaging) with T1 mapping and diffusion MRI. Four quantitative and one semiquantitative parameter were obtained for each tumor using the Toft pharmacokinetic model. ADCmean and ADCmin values were calculated from apparent diffusion coefficient (ADC) maps.
Results: Eighteen of 36 patients (50%) had malignant tumors, and 18 had benign tumors. There were 26 soft tissue tumors and 10 bone tissue tumors. Among malignant and benign tumors, the best performance was found in the Ktrans, Kep, Ve values. (p=0.011, p=0.013, p=0,035).
Conclusion: Ktrans and Kep values, which are indicators of increased permeability in the tumor, are noninvasive parameters for determining the malignant character of the tumor. The 'Ve' value is an indicator of the extracellular volume of the tumor. Ktrans, Kep and Ve maps can also guide the biopsy.

Keywords

MRI, Kep, Ktrans, Ve, ADC, ROI

References

• Wu JS, Hochman MG. Soft-Tissue Tumors and Tumorlike Lesions: A Systematic Imaging Approach. Radiology 2009;253:297–316.
• Choi YJ, Lee IS, Song YS, Kim J Il, Choi K-U, Song JW. Diagnostic performance of diffusion-weighted (DWI) and dynamic contrast-enhanced (DCE) MRI for the differentiation of benign from malignant soft-tissue tumors. Journal of Magnetic Resonance Imaging 2019;50:798–809.
• Buckley DL, Drew PJ, Mussurakis S, Monson JRT, Horsman A. Microvessel density in invasive breast cancer assessed by dynamic gd-dtpa enhanced MRI. Journal of Magnetic Resonance Imaging 1997;7:461–4.
• Gandhi D, Chepeha DB, Miller T, Carlos RC, Bradford CR, Karamchandani R, et al. Correlation between initial and early follow-up CT perfusion parameters with endoscopic tumor response in patients with advanced squamous cell carcinomas of the oropharynx treated with organ-preservation therapy. AJNR Am J Neuroradiol 2006;27:101–6.
• Gondim Teixeira PA, Leplat C, Chen B, De Verbizier J, Beaumont M, Badr S, et al. Contrast-enhanced 3T MR Perfusion of Musculoskeletal Tumours: T1 Value Heterogeneity Assessment and Evaluation of the Influence of T1 Estimation Methods on Quantitative Parameters. Eur Radiol 2017;27:4903–12.
• Leplat C, Hossu G, Chen B, De Verbizier J, Beaumont M, Blum A, et al. Contrast-enhanced 3-T perfusion MRI with quantitative analysis for the characterization of musculoskeletal tumors: Is it worth the trouble? American Journal of Roentgenology 2018;211:1092–8.
• Mangham DC. World Health Organisation classification of tumours: pathology and genetics of tumours of soft tissue and bone. J Bone Joint Surg Br 2004;86-B:466–466.
• Tofts PS, Brix G, Buckley DL, Evelhoch JL, Henderson E, Knopp M V., et al. Estimating kinetic parameters from dynamic contrast-enhanced t1-weighted MRI of a diffusable tracer: Standardized quantities and symbols. Journal of Magnetic Resonance Imaging 1999;10:223–32.
• King AD, Chow SKK, Yu K-H, Mo FKF, Yeung DKW, Yuan J, et al. DCE-MRI for Pre-Treatment Prediction and Post-Treatment Assessment of Treatment Response in Sites of Squamous Cell Carcinoma in the Head and Neck. PLoS One 2015;10:e0144770.
• Tofts PS. Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. Journal of Magnetic Resonance Imaging 1997;7:91–101.
• Fukuda T, Wengler K, de Carvalho R, Boonsri P, Schweitzer ME. MRI biomarkers in osseous tumors. Journal of Magnetic Resonance Imaging 2019;50:702–18.
• Cuenod CA, Balvay D. Perfusion and vascular permeability: Basic concepts and measurement in DCE-CT and DCE-MRI. Diagn Interv Imaging 2013;94:1187–204.
• Zhang Y, Yue B, Zhao X, Chen H, Sun L, Zhang X, et al. Benign or Malignant Characterization of Soft-Tissue Tumors by Using Semiquantitative and Quantitative Parameters of Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Canadian Association of Radiologists Journal 2020;71:92–9.
• Lee SK, Jee W-H, Jung CK, Chung Y-G. Multiparametric quantitative analysis of tumor perfusion and diffusion with 3T MRI: differentiation between benign and malignant soft tissue tumors. Br J Radiol 2020;93:20191035.
• Patterson DM, Padhani AR, Collins DJ. Technology Insight: water diffusion MRI—a potential new biomarker of response to cancer therapy. Nat Clin Pract Oncol 2008;5:220–33.
• Yabuuchi H, Soeda H, Matsuo Y, Okafuji T, Eguchi T, Sakai S, et al. Phyllodes Tumor of the Breast: Correlation between MR Findings and Histologic Grade. Radiology 2006;241:702–9.
• Hayashida Y, Hirai T, Yakushiji T, Katahira K, Shimomura O, Imuta M, et al. Evaluation of diffusion-weighted imaging for the differential diagnosis of poorly contrast-enhanced and T2-prolonged bone masses: Initial experience. Journal of Magnetic Resonance Imaging 2006;23:377–82.