Intravoxel Incoherent Motion and Quantitative Non-Gaussian Diffusion MR Imaging: Evaluation of the Diagnostic and Prognostic Value of Several Markers of Malignant and Benign Breast Lesions

Intravoxel incoherent motion and non-Gaussian diffusion parameters through their combination as integrated markers can allow for differentiation of malignant and benign breast lesions with a diagnostic accuracy almost comparable to Breast Imaging and Reporting Data System categories without the need of contrast agents, and these parameters may provide prognostic guidance.

Purpose

To investigate the performance of integrated approaches that combined intravoxel incoherent motion (IVIM) and non-Gaussian diffusion parameters compared with the Breast Imaging and Reporting Data System (BI-RADS) to establish multiparameter thresholds scores or probabilities by using Bayesian analysis to distinguish malignant from benign breast lesions and their correlation with molecular prognostic factors.

Materials and Methods

Between May 2013 and March 2015, 411 patients were prospectively enrolled and 199 patients (allocated to training [n = 99] and validation [n = 100] sets) were included in this study. IVIM parameters (flowing blood volume fraction [fIVIM] and pseudodiffusion coefficient [D*]) and non-Gaussian diffusion parameters (theoretical apparent diffusion coefficient [ADC] at b value of 0 sec/mm2 [ADC0] and kurtosis [K]) by using IVIM and kurtosis models were estimated from diffusion-weighted image series (16 b values up to 2500 sec/mm2), as well as a synthetic ADC (sADC) calculated by using b values of 200 and 1500 (sADC200–1500) and a standard ADC calculated by using b values of 0 and 800 sec/mm2 (ADC0–800). The performance of two diagnostic approaches (combined parameter thresholds and Bayesian analysis) combining IVIM and diffusion parameters was evaluated and compared with BI-RADS performance. The Mann-Whitney U test and a nonparametric multiple comparison test were used to compare their performance to determine benignity or malignancy and as molecular prognostic biomarkers and subtypes of breast cancer.

Results

Significant differences were found between malignant and benign breast lesions for IVIM and non-Gaussian diffusion parameters (ADC0, K, fIVIM, fIVIM · D*, sADC200–1500, and ADC0–800; P < .05). Sensitivity and specificity for the validation set by radiologists A and B were as follows: sensitivity, 94.7% and 89.5%, and specificity, 75.0% and 79.2% for sADC200–1500, respectively; sensitivity, 94.7% and 96.1%, and specificity, 75.0% and 66.7%, for the combined thresholds approach, respectively; sensitivity, 92.1% and 92.1%, and specificity, 83.3% and 66.7%, for Bayesian analysis, respectively; and sensitivity and specificity, 100% and 79.2%, for BI-RADS, respectively. The significant difference in values of sADC200–1500 in progesterone receptor status (P = .002) was noted. sADC200–1500 was significantly different between histologic subtypes (P = .006).

Conclusion

Approaches that combined various IVIM and non-Gaussian diffusion MR imaging parameters may provide BI-RADS–equivalent scores almost comparable to BI-RADS categories without the use of contrast agents. Non-Gaussian diffusion parameters also differed by biologic prognostic factors.

© RSNA, 2017

Online supplemental material is available for this article.

References

  • 1. Torre LA, Siegel RL, Ward EM, et al. Global cancer incidence and mortality rates and trends–an update. Cancer Epidemiol Biomarkers Prev 2016;25:16–27. Crossref, MedlineGoogle Scholar
  • 2. Goldhirsch A, Wood WC, Coates AS, et al. Strategies for subtypes–dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol 2011;22(8):1736–1747. Crossref, MedlineGoogle Scholar
  • 3. Sardanelli F, Giuseppetti GM, Panizza P, et al. Sensitivity of MRI versus mammography for detecting foci of multifocal, multicentric breast cancer in Fatty and dense breasts using the whole-breast pathologic examination as a gold standard. AJR Am J Roentgenol 2004;183(4):1149–1157. Crossref, MedlineGoogle Scholar
  • 4. Evans DG, Kesavan N, Lim Y, et al. MRI breast screening in high-risk women: cancer detection and survival analysis. Breast Cancer Res Treat 2014;145(3):663–672. Crossref, MedlineGoogle Scholar
  • 5. Woodhams R, Matsunaga K, Iwabuchi K, et al. Diffusion-weighted imaging of malignant breast tumors: the usefulness of apparent diffusion coefficient (ADC) value and ADC map for the detection of malignant breast tumors and evaluation of cancer extension. J Comput Assist Tomogr 2005;29(5):644–649. Crossref, MedlineGoogle Scholar
  • 6. Partridge SC, Demartini WB, Kurland BF, Eby PR, White SW, Lehman CD. Differential diagnosis of mammographically and clinically occult breast lesions on diffusion-weighted MRI. J Magn Reson Imaging 2010;31(3):562–570. Crossref, MedlineGoogle Scholar
  • 7. Iima M, Le Bihan D, Okumura R, et al. Apparent diffusion coefficient as an MR imaging biomarker of low-risk ductal carcinoma in situ: a pilot study. Radiology 2011;260(2):364–372. LinkGoogle Scholar
  • 8. Park SH, Moon WK, Cho N, et al. Diffusion-weighted MR imaging: pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer. Radiology 2010;257(1):56–63. LinkGoogle Scholar
  • 9. Cho GY, Moy L, Kim SG, et al. Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors. Eur Radiol 2016;26(8):2547–2558. Crossref, MedlineGoogle Scholar
  • 10. Martincich L, Deantoni V, Bertotto I, et al. Correlations between diffusion-weighted imaging and breast cancer biomarkers. Eur Radiol 2012;22(7):1519–1528. Crossref, MedlineGoogle Scholar
  • 11. Le Bihan D. Apparent diffusion coefficient and beyond: what diffusion MR imaging can tell us about tissue structure. Radiology 2013;268(2):318–322. LinkGoogle Scholar
  • 12. Iima M, Le Bihan D. Clinical Intravoxel Incoherent Motion and Diffusion MR Imaging: Past, Present, and Future. Radiology 2016;278(1):13–32. LinkGoogle Scholar
  • 13. Iima M, Yano K, Kataoka M, et al. Quantitative non-Gaussian diffusion and intravoxel incoherent motion magnetic resonance imaging: differentiation of malignant and benign breast lesions. Invest Radiol 2015;50(4):205–211. Crossref, MedlineGoogle Scholar
  • 14. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 1988;168(2):497–505. LinkGoogle Scholar
  • 15. Iima M, Reynaud O, Tsurugizawa T, et al. Characterization of glioma microcirculation and tissue features using intravoxel incoherent motion magnetic resonance imaging in a rat brain model. Invest Radiol 2014;49(7):485–490. Crossref, MedlineGoogle Scholar
  • 16. Partridge SC, Nissan N, Rahbar H, Kitsch AE, Sigmund EE. Diffusion-weighted breast MRI: Clinical applications and emerging techniques. J Magn Reson Imaging 2017;45(2):337–355. Crossref, MedlineGoogle Scholar
  • 17. Choi SY, Chang YW, Park HJ, Kim HJ, Hong SS, Seo DY. Correlation of the apparent diffusion coefficiency values on diffusion-weighted imaging with prognostic factors for breast cancer. Br J Radiol 2012;85(1016):e474–e479. Crossref, MedlineGoogle Scholar
  • 18. Jeh SK, Kim SH, Kim HS, et al. Correlation of the apparent diffusion coefficient value and dynamic magnetic resonance imaging findings with prognostic factors in invasive ductal carcinoma. J Magn Reson Imaging 2011;33(1):102–109. Crossref, MedlineGoogle Scholar
  • 19. Kato F, Kudo K, Yamashita H, et al. Differences in morphological features and minimum apparent diffusion coefficient values among breast cancer subtypes using 3-tesla MRI. Eur J Radiol 2016;85(1):96–102. Crossref, MedlineGoogle Scholar
  • 20. Lee YJ, Kim SH, Kang BJ, et al. Intravoxel incoherent motion (IVIM)-derived parameters in diffusion-weighted MRI: Associations with prognostic factors in invasive ductal carcinoma. J Magn Reson Imaging 2017;45(5):1394–1406. Crossref, MedlineGoogle Scholar
  • 21. Kamitani T, Matsuo Y, Yabuuchi H, et al. Correlations between apparent diffusion coefficient values and prognostic factors of breast cancer. Magn Reson Med Sci 2013;12(3):193–199. Crossref, MedlineGoogle Scholar
  • 22. Suo S, Cheng F, Cao M, et al. Multiparametric diffusion-weighted imaging in breast lesions: Association with pathologic diagnosis and prognostic factors. J Magn Reson Imaging 2017;46(3):740–750. Crossref, MedlineGoogle Scholar
  • 23. Eby PR, DeMartini WB, Gutierrez RL, Saini MH, Peacock S, Lehman CD. Characteristics of probably benign breast MRI lesions. AJR Am J Roentgenol 2009;193(3):861–867. Crossref, MedlineGoogle Scholar
  • 24. Fattaneh A, Tavassoli A, Devilee P. Tumours of the Breast and Female Genital Organs. In: WHO/IARC Classification of Tumours. Geneva, Switzerland: World Health Organization, 2003. Google Scholar
  • 25. Sun K, Chen X, Chai W, et al. Breast Cancer: Diffusion Kurtosis MR Imaging-Diagnostic Accuracy and Correlation with Clinical-Pathologic Factors. Radiology 2015;277(1):46–55. LinkGoogle Scholar
  • 26. Wu D, Li G, Zhang J, Chang S, Hu J, Dai Y. Characterization of breast tumors using diffusion kurtosis imaging (DKI). PLoS One 2014;9(11):e113240. Crossref, MedlineGoogle Scholar
  • 27. Lee HJ, Rha SY, Chung YE, et al. Tumor perfusion-related parameter of diffusion-weighted magnetic resonance imaging: correlation with histological microvessel density. Magn Reson Med 2014;71(4):1554–1558. Crossref, MedlineGoogle Scholar
  • 28. Sigmund EE, Cho GY, Kim S, et al. Intravoxel incoherent motion imaging of tumor microenvironment in locally advanced breast cancer. Magn Reson Med 2011;65(5):1437–1447. Crossref, MedlineGoogle Scholar
  • 29. Liu C, Liang C, Liu Z, Zhang S, Huang B. Intravoxel incoherent motion (IVIM) in evaluation of breast lesions: comparison with conventional DWI. Eur J Radiol 2013;82(12):e782–e789. Crossref, MedlineGoogle Scholar
  • 30. Grimm LJ. Breast MRI radiogenomics: Current status and research implications. J Magn Reson Imaging 2016;43(6):1269–1278. Crossref, MedlineGoogle Scholar
  • 31. Dorrius MD, Dijkstra H, Oudkerk M, Sijens PE. Effect of b value and pre-admission of contrast on diagnostic accuracy of 1.5-T breast DWI: a systematic review and meta-analysis. Eur Radiol 2014;24(11):2835–2847. Crossref, MedlineGoogle Scholar
  • 32. Kanda T, Nakai Y, Oba H, Toyoda K, Kitajima K, Furui S. Gadolinium deposition in the brain. Magn Reson Imaging 2016;34(10):1346–1350. Crossref, MedlineGoogle Scholar
  • 33. Kim EJ, Kim SH, Park GE, et al. Histogram analysis of apparent diffusion coefficient at 3.0t: Correlation with prognostic factors and subtypes of invasive ductal carcinoma. J Magn Reson Imaging 2015;42(6):1666–1678. Crossref, MedlineGoogle Scholar

Article History

Received December 20, 2016; revision requested February 27, 2017; revision received June 6; accepted June 30; final version accepted September 7.
Published online: Nov 02 2017
Published in print: May 2018