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Q-space imaging yields a higher effect gradient to assess cellularity than conventional diffusion-weighted imaging methods at 3.0 t: A pilot study with freshly excised whole-breast tumors

Lookup NU author(s): Dr Jiabao He



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2019, Radiological Society of North America Inc.. All rights reserved.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 (r = −0.468; P = .038) and median (r = −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.

Publication metadata

Author(s): Senn N, Masannat Y, Husain E, Siow B, Heys SD, He J

Publication type: Article

Publication status: Published

Journal: Radiology: Imaging Cancer

Year: 2019

Volume: 1

Issue: 1

Print publication date: 01/09/2019

Online publication date: 27/09/2019

Acceptance date: 25/07/2019

Date deposited: 01/11/2022

ISSN (electronic): 2638-616X

Publisher: Radiological Society of North America Inc.


DOI: 10.1148/rycan.2019190008

PubMed id: 33778671


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Funder referenceFunder name
1654748, BB/M010996/1
N.S. supported by Biotechnology and Biological Sciences Research Council (1654748, BB/M010996/1).
Study supported by the National Health Service Grampian Endowment Fund (15/1/052).