Nicholas Senn1, Yazan Masannat1, Ehab Husain1, Bernard Siow1, Steven D Heys1, Jiabao He1. 1. Institute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen AB25 2ZD, Scotland (N.S., S.D.H., J.H.); Breast Unit (Y.M., S.D.H.) and Department of Pathology (E.H.), Aberdeen Royal Infirmary, Aberdeen, Scotland; and MRI Unit, The Francis Crick Institute, London, England (B.S.).
Abstract
Purpose: To determine whether q-space imaging (QSI), an advanced diffusion-weighted MRI method, provides a higher effect gradient to assess tumor cellularity than existing diffusion imaging methods, and fidelity to cellularity obtained from histologic analysis. Materials and Methods: In this prospective study, diffusion-weighted images were acquired from 20 whole-breast tumors freshly excised from participants (age range, 35-78 years) by using a clinical 3.0-T MRI unit. Median and skewness values were extracted from the histogram distributions obtained from QSI, monoexponential model, diffusion kurtosis imaging (DKI), and stretched exponential model (SEM). The skewness from QSI and other diffusion models was compared by using paired t tests and relative effect gradient obtained from correlating skewness values. Results: The skewness obtained from QSI (mean, 1.34 ± 0.77 [standard deviation]) was significantly higher than the skewness from monoexponential fitting approach (mean, 1.09 ± 0.67; P = .015), SEM (mean, 1.07 ± 0.70; P = .014), and DKI (mean, 0.97 ± 0.63; P = .004). QSI yielded a higher effect gradient in skewness (percentage increase) compared with monoexponential fitting approach (0.26 of 0.74; 35.1%), SEM (0.26 of 0.74; 35.1%), and DKI (0.37 of 0.63; 58.7%). The skewness and median from QSI were significantly correlated with the skewness (ρ = -0.468; P = .038) and median (ρ = -0.513; P = .021) of cellularity from histologic analysis. Conclusion: QSI yields a higher effect gradient in assessing breast tumor cellularity than existing diffusion methods, and fidelity to underlying histologic structure.Keywords: Breast, MR-Diffusion Weighted Imaging, MR-Imaging, Pathology, Tissue Characterization, Tumor ResponseOnline supplemental material is available for this article.Published under a CC BY 4.0 license. 2019 by the Radiological Society of North America, Inc.
Purpose: To determine whether q-space imaging (QSI), an advanced diffusion-weighted MRI method, provides a higher effect gradient to assess tumor cellularity than existing diffusion imaging methods, and fidelity to cellularity obtained from histologic analysis. Materials and Methods: In this prospective study, diffusion-weighted images were acquired from 20 whole-breast tumors freshly excised from participants (age range, 35-78 years) by using a clinical 3.0-T MRI unit. Median and skewness values were extracted from the histogram distributions obtained from QSI, monoexponential model, diffusion kurtosis imaging (DKI), and stretched exponential model (SEM). The skewness from QSI and other diffusion models was compared by using paired t tests and relative effect gradient obtained from correlating skewness values. Results: The skewness obtained from QSI (mean, 1.34 ± 0.77 [standard deviation]) was significantly higher than the skewness from monoexponential fitting approach (mean, 1.09 ± 0.67; P = .015), SEM (mean, 1.07 ± 0.70; P = .014), and DKI (mean, 0.97 ± 0.63; P = .004). QSI yielded a higher effect gradient in skewness (percentage increase) compared with monoexponential fitting approach (0.26 of 0.74; 35.1%), SEM (0.26 of 0.74; 35.1%), and DKI (0.37 of 0.63; 58.7%). The skewness and median from QSI were significantly correlated with the skewness (ρ = -0.468; P = .038) and median (ρ = -0.513; P = .021) of cellularity from histologic analysis. Conclusion: QSI yields a higher effect gradient in assessing breast tumor cellularity than existing diffusion methods, and fidelity to underlying histologic structure.Keywords: Breast, MR-Diffusion Weighted Imaging, MR-Imaging, Pathology, Tissue Characterization, TumorResponseOnline supplemental material is available for this article.Published under a CC BY 4.0 license. 2019 by the Radiological Society of North America, Inc.
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