The diagnostic performance of quantitative mapping in breast cancer patients: a preliminary study using synthetic MRI

Abstract

Background Previous studies have indicated that quantitative MRI (qMR) is beneficial for diagnosis of breast cancer. As a novel qMR technology, synthetic MRI (syMRI) may be advantageous by offering simultaneous generation of T1 and T2 mapping in one scan within a few minutes and without concern to the deposition of the gadolinium contrast agent in cell nucleus. In this study, the potential of quantitative mapping derived from Synthetic MRI (SyMRI) to diagnose breast cancer was investigated. Methods From April 2018 to May 2019, a total of 87 patients with suspicious breast lesions underwent both conventional and SyMRI before treatment. The quantitative metrics derived from SyMRI, including T1 and T2 values, were measured in breast lesions. The diagnostic performance of SyMRI was evaluated with unpaired Student’s t-tests, receiver operating characteristic curve analysis and multivariate logistic regression analysis. The AUCs of quantitative values were compared using Delong test. Results Among 77 patients who met the inclusion criteria, 48 were diagnosed with histopathological confirmed breast cancers, and the rest had benign lesions. The breast cancers showed significantly higher T1 (1611.61 ± 215.88 ms) values and lower T2 (80.93 ± 7.51 ms) values than benign lesions. The area under the ROC curve (AUC) values were 0.931 (95% CI: 0.874–0.989) and 0.883 (95% CI: 0.810–0.956) for T1 and T2 maps, respectively, in diagnostic discrimination between breast cancers and benign lesions. A slightly increased AUC of 0.978 (95% CI: 0.915–0.993) was achieved by combining those two relaxation-based quantitative metrics. Conclusion In conclusion, our preliminary study showed that the quantitative T1 and T2 values obtained by SyMRI could distinguish effectively between benign and malignant breast lesions, and T1 relaxation time showed the highest diagnostic efficiency. Furthermore, combining the two quantitative relaxation metrics further improved their diagnostic performance.

Quantitative Synthetic Magnetic Resonance Imaging for Brain Metastases: A Feasibility Study

“…Feasibility studies have previously reported that synthetic MRI-generated T1 and T2 maps were diagnostically acceptable in the normal brain [ 21 , 40 , 41 , 42 ]. The utility of this method has been further tested for extracranial organs, including the prostate, breast, and rectum [ 43 , 44 , 45 , 46 ]. The present study tested the robustness of the MAGiC method for the quantitative measurement of T1 and T2 values on a phantom and patients with BM.…”

Evaluation of the differentiation of benign and malignant breast lesions using synthetic relaxometry and the Kaiser score

“…We found that the mean T1 values of benign lesions were significantly higher than those of malignant ones. Some studies ( 22 , 23 ) presented conflicting synthetic T1 values in benign and malignant lesions. The possible reasons for this result were as follows: The ROI generating methods might have an effect on the location of the lesion area and the quantification values ( 32 , 33 ).…”

“…Synthetic magnetic resonance imaging (SyMRI) with magnetic resonance imaging compilation (MAGiC) is a recently proposed multi-dynamic multi-echo sequence, which can simultaneously generate quantitative T1 relaxation time (T1), T2 relaxation time (T2), and proton density (PD) maps in a clinically acceptable acquisition time ( 19 – 21 ). Several studies have demonstrated the usefulness of the quantitative values from SyMRI in the differential diagnosis of breast cancers ( 22 – 25 ). Meng et al.…”

“…Meng et al. ( 22 ) investigated T1 and T2 values from SyMRI and found their combination performed better in the diagnosis of breast cancer compared with T1 or T2. Matsuda et al.…”

Quantitative Traits Loci and candidate gene mapping for Quinones and Phenolic Biosynthesis in Nigella sativa L. using pseudo-chromosome Assembly Arusa Aftab, Zubaida Yousaf, Zill-e-Huma Aftab, Nadia Riaz, Bushra Shamsheer

“…QTL studies have a long and rich history and have played important roles in gene cloning and characterization; however, there is still a great deal of work to be done. Existing data on model organisms need to be expanded to the point at which meta-analysis is feasible in order to document robust trends regarding genetic architecture (Meng et al, 2020). Recently, the next generation sequencing methods have accelerated the research on QTL analysis.…”