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. We identified independent prognostic factors for BRCA patients. Additionally, we obtained imaging features from the Cancer Imaging Archive (TCIA) database for 73 patients who underwent preoperative MRI procedures, and used the Least Absolute Shrinkage and Selection Operator (LASSO) to select the best imaging features for constructing an MRI-based radiomics model for evaluating immune cell infiltration levels in breast cancer patients. RESULTS: According to the results of Cox regression analysis, M2 macrophages were identified as an independent prognostic factor for BRCA patients (HR = 32.288, 95% CI: 3.100-357.478). A total of nine significant features were selected to calculate the radiomics-based score. We established an intratumoral model with AUCs (95% CI) of 0.662 (0.495-0.802) and 0.678 (0.438-0.901) in the training and testing cohorts, respectively. Additionally, a peritumoral model was created with AUCs (95% CI) of 0.826 (0.710-0.924) and 0.752 (0.525-0.957), and a combined model was established with AUCs (95% CI) of 0.843 (0.723-0.938) and 0.744 (0.491-0.965). The peritumoral model demonstrated the highest diagnostic efficacy, with an accuracy, sensitivity, and specificity of 0.773, 0.727, and 0.818, respectively, in its testing cohort. CONCLUSION: The MRI-based radiomics model has the potential to evaluate the degree of immune cell infiltration in breast cancer patients, offering a non-invasive imaging biomarker for assessing the tumor microenvironment in this disease.

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. METHODS: One hundred and seventy-six breast lesions from 166 women with dense breasts were retrospectively evaluated. The lesion visibility, area under the curve (AUC), sensitivity and specificity of cancer detection by MG, DWI + TIRM, and clinical MRI were evaluated and compared. MG plus clinical MRI served as the gold standard for lesion detection and pathology served as the gold standard for cancer detection. RESULTS: Lesion visibility of DWI + TIRM (96.6%) was significantly superior to MG (67.6%) in women with dense breasts (p < 0.001). There was no significant difference compared with clinical MRI. DWI + TIRM showed higher accuracy (AUC = 0.935) and sensitivity (93.68%) for breast cancer detection than MG (AUC = 0.783, sensitivity = 46.32%), but was comparable to clinical MRI (AUC = 0.944, sensitivity = 93.68%). The specificity of DWI + TIRM (83.95%) was lower than MG (98.77%), but higher than clinical MRI (77.78%). CONCLUSIONS: DWI combined with TIRM could be a safe, sensitive, and practical alternative for screening women with dense breasts.

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.

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

Objective. 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.Approach. With IRB approval, 40 women with histologically confirmed breast lesions (16 benign, 24 malignant) underwent DWI with 11b-values (50 to 3000 s/mm2) at 3T. Three CTRW parameters,Dm,α, andßand three IVIM parametersDdiff,Dperf, andfwere estimated from the lesions. A histogram was generated and histogram features of skewness, variance, mean, median, interquartile range; and the value of the 10%, 25% and 75% quantiles were extracted for each parameter from the regions-of-interest. Iterative feature selection was performed using the Boruta algorithm that uses the Benjamin Hochberg False Discover Rate to first determine significant features and then to apply the Bonferroni correction to further control for false positives across multiple comparisons during the iterative procedure. Predictive performance of the significant features was evaluated using Support Vector Machine, Random Forest, Naïve Bayes, Gradient Boosted Classifier (GB), Decision Trees, AdaBoost and Gaussian Process machine learning classifiers.Main Results. The 75% quantile, and median ofDm; 75% quantile off;mean, median, and skewness ofß;kurtosis ofDperf; and 75% quantile ofDdiffwere the most significant features. The GB differentiated malignant and benign lesions with an accuracy of 0.833, an area-under-the-curve of 0.942, and an F1 score of 0.87 providing the best statistical performance (p-value < 0.05) compared to the other classifiers.Significance. Our study has demonstrated that GB with a set of histogram features from the CTRW and IVIM model parameters can effectively differentiate malignant and benign breast lesions.

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. Methods: We retrospectively reviewed 114 breast DMs rated as nonpalpable BI-RADS category 0 lesions in 112 patients from January 2014 to June 2019. STIR, high b value DWI, and DCE-MRI were performed for all patients. Two breast radiologists read individual sequences (STIR, DWI, DCE-MRI) and pairs of sequences (STIR-DWI) to detect BI-RADS category 0 lesions in DMs. Receiver operating characteristic (ROC) curve analysis was used to assess diagnostic performance according to a best valuable comparator that combined MRI imaging, clinical, and pathological data. Results: Among of 114 lesions (the median age of patients was 47 years; the median size of the lesion was 19 mm), 32 (48.5%) malignant lesions were missed by STIR, 9 (13.6%) malignant lesions were missed by STIR-DWI, and 3 (4.5%) malignant lesions were missed by DCE-MRI. The principal finding of our study was that STIR-DWI and DCE-MRI showed higher diagnostic accuracy than did STIR (P<0.01). STIR-DWI showed higher accuracy [area under the curve (AUC) =0.858; sensitivity =87.8%] for BI-RADS category 0 lesions in DMs than did STIR (AUC =0.754; sensitivity =51.5%), while the performance was comparable to that of DCE-MRI (AUC =0.884; sensitivity =95.5%). Conclusions: Using pairs of sequences (STIR-DWI) is a non-contrast-enhanced MRI technique and had an equal diagnostic performance in distinguishing benign from malignant lesions among nonpalpable BI-RADS category 0 lesions to that of DCE-MRI. As a result, STIR-DWI as having the potential to improve the safety and efficacy in of breast cancer screening, especially in nonpalpable BI-RADS category 0 lesions at in DMs.

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.BL commonly showed significant enhancement with penetrating vessels and septation in masses on dynamic contrast-enhanced MRI (DCE-MRI). Diffusion limitation of BL is more pronounced than breast cancer on diffusion weighted imaging.The study suggests that the penetrating vessels and diffusion restriction of lesions are helpful for the diagnosis and differential diagnosis of BL.