External validation of multiparametric magnetic resonance imaging-based decision rules for characterizing breast lesions and comparison to Kaiser score and breast imaging reporting and data system (BI-RADS) category.

Background: Breast imaging reporting and data system (BI-RADS) provides standard descriptors but not detailed decision rules for characterizing breast lesions. Diffusion-weighted imaging (DWI) and T2-weighted imaging (T2WI) are also not incorporated in the BI-RADS. Several multiparametric magnetic resonance imaging (mpMRI)-based decision rules have been developed to differentiate breast lesions, but lack external validation.

Magnetic resonance imaging-based radiomics was used to evaluate the level of prognosis-related immune cell infiltration in breast cancer tumor microenvironment.

Purpose: The tumor immune microenvironment is a valuable source of information for predicting prognosis in breast cancer (BRCA) patients. To identify immune cells associated with BRCA patient prognosis from the Cancer Genetic Atlas (TCGA), we established an MRI-based radiomics model for evaluating the degree of immune cell infiltration in breast cancer patients.

Methods: CIBERSORT was utilized to evaluate the degree of infiltration of 22 immune cell types in breast cancer patients from the TCGA database, and both univariate and multivariate Cox regressions were employed to determine the prognostic significance of immune cell infiltration levels in BRCA patients.

Characterization of breast lesions using multi-parametric diffusion MRI and machine learning.

. To investigate quantitative imaging markers based on parameters from two diffusion-weighted imaging (DWI) models, continuous-time random-walk (CTRW) and intravoxel incoherent motion (IVIM) models, for characterizing malignant and benign breast lesions by using a machine learning algorithm..

The value of noncontrast MRI in evaluating breast imaging reporting and data system category 0 lesions on digital mammograms.

Background: Benign and malignant diagnosis of nonpalpable breast imaging reporting and data system (BI-RADS) category 0 lesions on digital mammograms (DMs) is very important. We compared the diagnostic performance of non-contrast-enhanced magnetic resonance imaging (MRI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for them. We sought to evaluate BI-RADS category 0 lesions using 3 MRI sequences: short tau inversion recovery (STIR), STIR combined with high b value diffusion-weighted imaging (STIR-DWI), and DCE-MRI.

Radiological analysis of breast lymphoma: Experiences from cases series studies.

This retrospective study aimed to improve the diagnostic accuracy of breast lymphoma (BL) by analyzing the findings of BL on mammography and magnetic resonance imaging (MRI).Fifteen patients with breast lymphoma (BL, Primary/Secondary: 13/2) were confirmed by pathology. The imaging findings of those patients were analyzed by 2 senior radiologists.

Correction to: Non-contrast MRI for breast screening: preliminary study on detectability of benign and malignant lesions in women with dense breasts.

In the original version of the article, the image of Figure 2 was erroneously duplicated as Figure 4. The correct version of Figure 4 is given below. The original article has been corrected.

Non-contrast MRI for breast screening: preliminary study on detectability of benign and malignant lesions in women with dense breasts.

Purpose: The importance of breast cancer screening has long been known. Unfortunately, there is no imaging modality for screening women with dense breasts that is both sensitive and without concerns regarding potential side effects. The purpose of this study is to explore the possibility of combined diffusion-weighted imaging and turbo inversion recovery magnitude MRI (DWI + TIRM) to overcome the difficulty of detection sensitivity and safety.