Noncontrast‐Enhanced MR‐Based Conductivity Imaging for Breast Cancer Detection and Lesion Differentiation

Abstract

Background: There is increasing interest in noncontrast-enhanced MRI due to safety concerns for gadolinium contrast agents. Purpose: To investigate the clinical feasibility of MR-based conductivity imaging for breast cancer detection and lesion differentiation. Study Type: Prospective. Subjects: One hundred and ten women, with 112 known cancers and 17 benign lesions (biopsy-proven), scheduled for preoperative MRI. Field Strength/Sequence: Non-fat-suppressed T2-weighted turbo spin-echo sequence (T2WI), dynamic contrast-enhanced MRI and diffusion-weighted imaging (DWI) at 3T. Assessment: Cancer detectability on each imaging modality was qualitatively evaluated on a per-breast basis: the conductivity maps derived from T2WI were independently reviewed by three radiologists (R1-R3). T2WI, DWI, and preoperative digital mammography were independently reviewed by three other radiologists (R4-R6). Conductivity and apparent diffusion coefficient (ADC) measurements (mean, minimum, and maximum) were performed for 112 cancers and 17 benign lesions independently by two radiologists (R1 and R2). Tumor size was measured from surgical specimens. Statistical Tests: Cancer detection rates were compared using generalized estimating equations. Multivariable logistic regression analysis was performed to identify factors associated with cancer detectability. Discriminating ability of conductivity and ADC was evaluated by using the areas under the receiver operating characteristic curve (AUC). Results: Conductivity imaging showed lower cancer detection rates (20%-32%) compared to T2WI (62%-71%), DWI (85%-90%), and mammography (79%-88%) (all P < 0.05). Fatty breast on MRI (odds ratio = 11.8, P < 0.05) and invasive tumor size (odds ratio = 1.7, P < 0.05) were associated with cancer detectability of conductivity imaging. The maximum conductivity showed comparable ability to the mean ADC in discriminating between cancers and benign lesions (AUC = 0.

Predictive Value of Preoperative Dynamic Contrast-Enhanced MRI Imaging Features in Breast Cancer Patients with Postoperative Recurrence Time

“…This is also the main reason for the significant difference in median survival time between the two groups in this study. DCE-MRI can predict the recurrence time of patients by showing the blood supply and tumor shape of the tumor, providing important reference value for clinical practice [ 14 ].…”

In vivo electrical conductivity measurement of muscle, cartilage, and peripheral nerve around knee joint using MR-electrical properties tomography

“…As the Laplacian operator inevitably leads to noise amplification, a bilateral median filter was applied to the conductivity resulting from those equations, again applying the locally shaped kernel as done for the differentiation step. This approach has successfully been applied in previous studies for brain tumor conductivity 20 , 24 and similarly also for breast tumor conductivity 19 , 25 .…”

Physics Informed Neural Networks (PINN) for Low Snr Magnetic Resonance Electrical Properties Tomography (MREPT)

“…The noise sensitiveness is a major obstacle to the practical use of MREPT in clinical settings [ 2 , 3 , 4 , 52 , 53 ], where noise contamination is unavoidable. As shown in Figure 3 , Figure 5 and Figure 6 , the performance of the two analytic models deteriorate fast as the SNR decreases.…”

“…Electrical properties (EPs) of biological tissues (permittivity, , and conductivity, ) are quantitative properties that could facilitate the identification of non-calcified cancerous tissues [ 1 , 2 , 3 , 4 ]. Moreover, the knowledge of the EP distribution of human tissues is a base to calculate the specific absorption rate (SAR) for RF safety when humans are exposed to RF fields, e.g., wireless power transfer [ 5 ], ultra-high field MRI [ 6 ].…”