Investigative Magnetic Resonance Imaging

Korean Society of Magnetic Resonance in Medicine (대한자기공명의과학회)
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Benign versus Malignant Soft-Tissue Tumors: Differentiation with 3T Magnetic Resonance Image Textural Analysis Including Diffusion-Weighted Imaging

  • Lee, Youngjun (Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Jee, Won-Hee (Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Whang, Yoon Sub (Department of Radiology, Myongji St. Mary's Hospital) ;
  • Jung, Chan Kwon (Department of Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Chung, Yang-Guk (Department of Orthopedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Lee, So-Yeon (Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea)
  • Received : 2021.03.09
  • Accepted : 2021.04.26
  • Published : 2021.06.30
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Abstract

Purpose: To investigate the value of MR textural analysis, including use of diffusion-weighted imaging (DWI) to differentiate malignant from benign soft-tissue tumors on 3T MRI. Materials and Methods: We enrolled 69 patients (25 men, 44 women, ages 18 to 84 years) with pathologically confirmed soft-tissue tumors (29 benign, 40 malignant) who underwent pre-treatment 3T-MRI. We calculated MR texture, including mean, standard deviation (SD), skewness, kurtosis, mean of positive pixels (MPP), and entropy, according to different spatial-scale factors (SSF, 0, 2, 4, 6) on axial T1- and T2-weighted images (T1WI, T2WI), contrast-enhanced T1WI (CE-T1WI), high b-value DWI (800 sec/mm2), and apparent diffusion coefficient (ADC) map. We used the Mann-Whitney U test, logistic regression, and area under the receiver operating characteristic curve (AUC) for statistical analysis. Results: Malignant soft-tissue tumors had significantly lower mean values of DWI, ADC, T2WI and CE-T1WI, MPP of ADC, and CE-T1WI, but significantly higher kurtosis of DWI, T1WI, and CE-T1WI, and entropy of DWI, ADC, and T2WI than did benign tumors (P < 0.050). In multivariate logistic regression, the mean ADC value (SSF, 6) and kurtosis of CE-T1WI (SSF, 4) were independently associated with malignancy (P ≤ 0.009). A multivariate model of MR features worked well for diagnosis of malignant soft-tissue tumors (AUC, 0.909). Conclusion: Accurate diagnosis could be obtained using MR textural analysis with DWI and CE-T1WI in differentiating benign from malignant soft-tissue tumors.

Keywords

References

  1. World Health Organization. Classification of tumours. In Fletcher CDM, Unni KK, Mertens F, eds. Pathology and genetics of tumors of soft tissue. Lyon: IARC Press; 2002:12-224
  2. Surov A, Nagata S, Razek AA, Tirumani SH, Wienke A, Kahn T. Comparison of ADC values in different malignancies of the skeletal musculature: a multicentric analysis. Skeletal Radiol 2015;44:995-1000 https://doi.org/10.1007/s00256-015-2141-5
  3. Razek A, Nada N, Ghaniem M, Elkhamary S. Assessment of soft tissue tumours of the extremities with diffusion echoplanar MR imaging. Radiol Med 2012;117:96-101 https://doi.org/10.1007/s11547-011-0709-2
  4. 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;50:11-27 https://doi.org/10.1002/jmri.26659
  5. Kransdorf MJ, Murphey MD. Imaging of soft-tissue tumors. 3rd ed. Philadelphia: Lippincott Williams & Wilkins (LWW), 2013
  6. Chung WJ, Chung HW, Shin MJ, et al. MRI to differentiate benign from malignant soft-tissue tumours of the extremities: a simplified systematic imaging approach using depth, size and heterogeneity of signal intensity. Br J Radiol 2012;85:e831-836 https://doi.org/10.1259/bjr/27487871
  7. Gruber L, Loizides A, Luger AK, et al. Soft-tissue tumor contrast enhancement patterns: diagnostic value and comparison between ultrasound and MRI. AJR Am J Roentgenol 2017;208:393-401 https://doi.org/10.2214/AJR.16.16859
  8. 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;26:743-754 https://doi.org/10.1007/s00330-015-3878-x
  9. Choi YJ, Lee IS, Song YS, Kim JI, Choi KU, Song JW. Diagnostic performance of diffusion-weighted (DWI) and dynamic contrast-enhanced (DCE) MRI for the differentiation of benign from malignant soft-tissue tumors. J Magn Reson Imaging 2019;50:798-809 https://doi.org/10.1002/jmri.26607
  10. Lee KR, Ko SY, Choi GM. Quantitative T2 mapping of articular cartilage of the glenohumeral joint at 3.0T in rotator cuff disease patients: the evaluation of degenerative change of cartilage. Investig Magn Reson Imaging 2019;23:228-240 https://doi.org/10.13104/imri.2019.23.3.228
  11. Kim HS, Kim JH, Yoon YC, Choe BK. Tumor spatial heterogeneity in myxoid-containing soft tissue using texture analysis of diffusion-weighted MRI. PLoS One 2017;12:e0181339 https://doi.org/10.1371/journal.pone.0181339
  12. Makanyanga J, Ganeshan B, Rodriguez-Justo M, et al. MRI texture analysis (MRTA) of T2-weighted images in Crohn's disease may provide information on histological and MRI disease activity in patients undergoing ileal resection. Eur Radiol 2017;27:589-597 https://doi.org/10.1007/s00330-016-4324-4
  13. Patel N, Henry A, Scarsbrook A. The value of MR textural analysis in prostate cancer. Clin Radiol 2019;74:876-885 https://doi.org/10.1016/j.crad.2018.11.007
  14. Qiu Q, Duan J, Duan Z, et al. Reproducibility and non-redundancy of radiomic features extracted from arterial phase CT scans in hepatocellular carcinoma patients: impact of tumor segmentation variability. Quant Imaging Med Surg 2019;9:453-464 https://doi.org/10.21037/qims.2019.03.02
  15. Corino VDA, Montin E, Messina A, et al. Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high-grade lesions. J Magn Reson Imaging 2018;47:829-840 https://doi.org/10.1002/jmri.25791
  16. Parikh J, Selmi M, Charles-Edwards G, et al. Changes in primary breast cancer heterogeneity may augment midtreatment MR imaging assessment of response to neoadjuvant chemotherapy. Radiology 2014;272:100-112 https://doi.org/10.1148/radiol.14130569
  17. Wang H, Zhang J, Bao S, et al. Preoperative MRI-based radiomic machine-learning nomogram may accurately distinguish between benign and malignant soft-tissue lesions: a two-center study. J Magn Reson Imaging 2020;52:873-882 https://doi.org/10.1002/jmri.27111
  18. Wang H, Nie P, Wang Y, et al. Radiomics nomogram for differentiating between benign and malignant soft-tissue masses of the extremities. J Magn Reson Imaging 2020;51:155-163 https://doi.org/10.1002/jmri.26818
  19. Lim HK, Jee WH, Jung JY, et al. Intravoxel incoherent motion diffusion-weighted MR imaging for differentiation of benign and malignant musculoskeletal tumours at 3 T. Br J Radiol 2018;91:20170636
  20. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT Texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 2017;37:1483-1503 https://doi.org/10.1148/rg.2017170056
  21. De Cecco CN, Ganeshan B, Ciolina M, et al. Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance. Invest Radiol 2015;50:239-245 https://doi.org/10.1097/rli.0000000000000116
  22. Peng H, Long F, Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 2005;27:1226-1238 https://doi.org/10.1109/TPAMI.2005.159
  23. Youden WJ. Index for rating diagnostic tests. Cancer 1950;3:32-35 https://doi.org/10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3
  24. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837-845 https://doi.org/10.2307/2531595
  25. Berenguer R, Pastor-Juan MDR, Canales-Vazquez J, et al. Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology 2018;288:407-415 https://doi.org/10.1148/radiol.2018172361
  26. Hiwatashi A, Kinoshita T, Moritani T, et al. Hypointensity on diffusion-weighted MRI of the brain related to T2 shortening and susceptibility effects. AJR Am J Roentgenol 2003;181:1705-1709 https://doi.org/10.2214/ajr.181.6.1811705
  27. Maldjian JA, Listerud J, Moonis G, Siddiqi F. Computing diffusion rates in T2-dark hematomas and areas of low T2 signal. AJNR Am J Neuroradiol 2001;22:112-118