Abstract
Contrast-enhanced magnetic resonance imaging (MRI) has shown variation in the stroma with distance from the tumor and this correlates with histological microvessel density. To date, however, conventional diffusion MRI has demonstrated limited sensitivity to these changes. This study modelled the diffusion signal by intravoxel incoherent motion (IVIM) to obtain parameters related to the vasculature and tissue diffusion. This revealed a small vascular contribution to the signal in tumor and peri-tumoral stroma within 8 mm. Monoexponential fitting performed worse than the IVIM model in tumor and stroma within 8 mm, but was sufficient in more distal stromal regions where lower microvessel density is expected. Modelling diffusion MRI by IVIM provided a measure of vascularity that may complement information from DCE-MRI and yielded additional information about diffusion in the extravascular tissue.
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Kim, M.Y., Cho, N., Koo, H.R., Yun, B.L., Bae, M.S., Chie, E.K., Moon, W.K.: Predicting local recurrence following breast-conserving treatment: Parenchymal signal enhancement ratio (SER) around the tumor on preoperative MRI. Acta Radiol. 54, 731–738 (2013)
Hattangadi, J., Park, C., Rembert, J., Klifa, C., Hwang, J., Gibbs, J., Hylton, N.M.: Breast stromal enhancement on MRI is associated with response to neoadjuvant chemotherapy. Am. J. Roentgenol. 190, 1630–1636 (2008)
Nabavizadeh, N., Klifa, C., Newitt, D., Lu, Y., Chen, Y.-Y., Hsu, H., Fisher, C., Tokayasu, T., Olshen, A.B., Spellman, P., Gray, J.W., Hylton, N., Park, C.C.: Topographic enhancement mapping of the cancer-associated breast stroma using breast MRI. Integr. Biol. 3, 490–496 (2011)
Bokacheva, L., Kaplan, J.B., Giri, D.D., Patil, S., Gnanasigamani, M., Nyman, C.G., Deasy, J.O., Morris, E.A., Thakur, S.B.: Intravoxel incoherent motion diffusion-weighted MRI at 3.0 T differentiates malignant breast lesions from benign lesions and breast parenchyma. J. Magn. Reson. Imaging. Early View (2013)
Sigmund, E.E., Cho, G.Y., Kim, S., Finn, M., Moccaldi, M., Jensen, J.H., Sodickson, D.K., Goldberg, J.D., Formenti, S., Moy, L.: Intravoxel incoherent motion imaging of tumor microenvironment in locally advanced breast cancer. Magn. Reson. Med. 65, 1437–1447 (2011)
Mclaughlin, R.L., Newitt, D.C., Wilmes, L.J., Jones, E.F., Wisner, D.J., Kornak, J., Proctor, E., Joe, B.N., Hylton, N.M.: High Resolution In Vivo Characterization of Apparent Difusion Coefficient at the Tumor-Stromal Boundary of Breast Carcinomas: A Pilot Study to Assess Treatment Response Using Proximity-Dependent Diffusion-Weighted Imaging. J. Magn. Reson. Imaging. Early View (2013)
Modat, M., Ridgway, G.R., Taylor, Z.A., Lehmann, M., Barnes, J., Fox, N.C., Hawkes, D.J., Ourselin, S.: Fast free-form deformation using graphics processing units. Comput. Methods Programs Biomed. 98, 278–284 (2010)
Henkelman, R.M.: Measurement of signal intensities in the presence of noise in MR images. Med. Phys. 12, 232–233 (1985)
Panagiotaki, E., Schneider, T., Siow, B., Hall, M.G., Lythgoe, M.F., Alexander, D.C.: Comparment models of the diffusion MR signal in brain white matter: A taxonomy and comparison. NeuroImage 59, 2241–2254 (2012)
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Bailey, C. et al. (2014). Modelling Vascularity in Breast Cancer and Surrounding Stroma Using Diffusion MRI and Intravoxel Incoherent Motion. In: Fujita, H., Hara, T., Muramatsu, C. (eds) Breast Imaging. IWDM 2014. Lecture Notes in Computer Science, vol 8539. Springer, Cham. https://doi.org/10.1007/978-3-319-07887-8_53
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DOI: https://doi.org/10.1007/978-3-319-07887-8_53
Publisher Name: Springer, Cham
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