Edna Furman-Haran1, Dov Grobgeld2, Noam Nissan2, Myra Shapiro-Feinberg3, Hadassa Degani2. 1. Departmentof Biological Services, Weizmann Institute of Science, Rehovot, Israel. 2. Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel. 3. Department of Radiology, Meir Medical Center, Kfar Saba, Israel.
Abstract
PURPOSE: To evaluate whether the various anisotropy indices derived from breast diffusion tensor imaging (DTI) can characterize the healthy breast structure and differentiate cancer from normal breast tissue. MATERIALS AND METHODS: Six healthy volunteers and retrospectively selected 24 breast cancer patients were imaged at 3T. DTI included two b-values 0 and 700 sec/mm2 with 20-64 gradient directions and TE of 120 or 90 msec. The normalized anisotropy indices: fractional anisotropy (FA), relative anisotropy (RA), and 1-volume ratio (1-VR), as well as the absolute maximal anisotropy index (λ1 -λ3 ) were compared. RESULTS: The spatial distribution of the various anisotropy indices in healthy volunteers exhibited a high congruence (Pearson correlation coefficients range: 0.79-1.0). All indices showed a statistically significant reduction (P < 0.001) following shortening of the diffusion time. Significantly lower λ1 -λ3 values were found in cancers as compared to normal breast tissue (P < 6.0 × 10-7 ), while the values of the normalized indices in cancers were not significantly different from those in normal breast tissue (P < 0.65 for FA, P < 0.6 for RA, and P < 0.2 for 1-VR). The contrast-to-noise ratio of λ1 -λ3 was significantly higher (P < 0.001) than those of the normalized anisotropy indices, and the area under the curve in a receiver operating characteristic analysis exhibited the highest value for λ1 -λ3 (0.89 ± 0.04 vs. 0.51-0.54 for the other anisotropy indices). CONCLUSION: Water diffusion anisotropy in the healthy breast can be similarly mapped by the normalized indices and by λ1 -λ3 . However, the normalized anisotropy indices fail to differentiate cancer from normal breast tissue, whereas λ1 -λ3 can assist in differentiating cancer from normal breast tissue. J. Magn. Reson. Imaging 2016;44:1624-1632.
PURPOSE: To evaluate whether the various anisotropy indices derived from breast diffusion tensor imaging (DTI) can characterize the healthy breast structure and differentiate cancer from normal breast tissue. MATERIALS AND METHODS: Six healthy volunteers and retrospectively selected 24 breast cancerpatients were imaged at 3T. DTI included two b-values 0 and 700 sec/mm2 with 20-64 gradient directions and TE of 120 or 90 msec. The normalized anisotropy indices: fractional anisotropy (FA), relative anisotropy (RA), and 1-volume ratio (1-VR), as well as the absolute maximal anisotropy index (λ1 -λ3 ) were compared. RESULTS: The spatial distribution of the various anisotropy indices in healthy volunteers exhibited a high congruence (Pearson correlation coefficients range: 0.79-1.0). All indices showed a statistically significant reduction (P < 0.001) following shortening of the diffusion time. Significantly lower λ1 -λ3 values were found in cancers as compared to normal breast tissue (P < 6.0 × 10-7 ), while the values of the normalized indices in cancers were not significantly different from those in normal breast tissue (P < 0.65 for FA, P < 0.6 for RA, and P < 0.2 for 1-VR). The contrast-to-noise ratio of λ1 -λ3 was significantly higher (P < 0.001) than those of the normalized anisotropy indices, and the area under the curve in a receiver operating characteristic analysis exhibited the highest value for λ1 -λ3 (0.89 ± 0.04 vs. 0.51-0.54 for the other anisotropy indices). CONCLUSION:Water diffusion anisotropy in the healthy breast can be similarly mapped by the normalized indices and by λ1 -λ3 . However, the normalized anisotropy indices fail to differentiate cancer from normal breast tissue, whereas λ1 -λ3 can assist in differentiating cancer from normal breast tissue. J. Magn. Reson. Imaging 2016;44:1624-1632.
Authors: Colleen Bailey; Bernard Siow; Eleftheria Panagiotaki; John H Hipwell; Thomy Mertzanidou; Julie Owen; Patrycja Gazinska; Sarah E Pinder; Daniel C Alexander; David J Hawkes Journal: NMR Biomed Date: 2016-12-21 Impact factor: 4.044