Related Experiment Video
Updated: Mar 19, 2026
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Quantitative and textural analysis of magnetization transfer and diffusion images in the early detection of brain
Nicola L Ainsworth1, Mary A McLean1, Dominick J O McIntyre1
1Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE.
Purpose:
The sensitivity of the magnetization transfer ratio (MTR) and apparent diffusion coefficient (ADC) for early detection of brain metastases was investigated in mice and humans.
Methods:
Mice underwent MRI twice weekly for up to 31 d following intracardiac injection of the brain-homing breast cancer cell line MDA-MB231-BR. Patients with small cell lung cancer underwent quarterly MRI for 1 year. MTR and ADC were measured in regions of metastasis and matched contralateral tissue at the final time point and in registered regions at earlier time points. Texture analysis and linear discriminant analysis were performed to detect metastasis-containing slices.
Results:
Compared with contralateral tissue, mouse metastases had significantly lower MTR and higher ADC at the final time point. Some lesions were visible at earlier time points on the MTR and ADC maps: 24% of these were not visible on corresponding T2 -weighted images. Texture analysis using the MTR maps showed 100% specificity and 98% sensitivity for metastasis at the final time point, with 77% sensitivity 2-4 d earlier and 46% 5-8 d earlier. Only 2 of 16 patients developed metastases, and their penultimate scans were normal.
Conclusions:
Some brain metastases may be detected earlier on MTR than conventional T2 ; however, the small gain is unlikely to justify "predictive" MRI. Magn Reson Med 77:1987-1995, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
