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The rationale was to assess whether there are differences in multiparametric 18F-FDG PET/MRI biomarkers of contralateral healthy breast tissue in patients with benign and malignant breast tumors. Methods: In this institutional review board-approved prospective single-institution study, 141 women with imaging abnormalities on mammography or sonography (BI-RADS 4/5) underwent combined 18F-FDG PET/MRI of the breast at 3T with dynamic contrast-enhanced MRI, diffusion-weighted imaging, and the radiotracer 18F-FDG. In all patients, the following imaging biomarkers were recorded for the contralateral (tumor-free) breast: breast parenchymal uptake (BPU) (from 18F-FDG PET), mean apparent diffusion coefficient (from diffusion-weighted imaging), background parenchymal enhancement (BPE), and amount of fibroglandular tissue (FGT) (from MRI). Appropriate statistical tests were used to assess differences in 18F-FDG PET/MRI biomarkers between patients with benign and malignant lesions. Results: There were 100 malignant and 41 benign lesions. BPE was minimal in 61 patients, mild in 56, moderate in 19, and marked in 5. BPE differed significantly (P < 0.001) between patients with benign and malignant lesions, with patients with cancer demonstrating decreased BPE in the contralateral tumor-free breast. FGT approached but did not reach significance (P = 0.055). BPU was 1.5 for patients with minimal BPE, 1.9 for mild BPE, 2.2 for moderate BPE, and 1.9 for marked BPE. BPU differed significantly between patients with benign lesions (mean, 1.9) and patients with malignant lesions (mean, 1.8) (P < 0.001). Mean apparent diffusion coefficient did not differ between groups (P = 0.19). Conclusion: Differences in multiparametric 18F-FDG PET/MRI biomarkers, obtained from contralateral tumor-free breast tissue, exist between patients with benign and patients with malignant breast tumors. Contralateral BPE, BPU, and FGT are decreased in breast cancer patients and may potentially serve as imaging biomarkers for the presence of malignancy.

BACKGROUND: Available data proving the value of DWI for breast cancer diagnosis is mainly for enhancing masses; DWI may be less sensitive and specific in non-mass enhancement (NME) lesions. The objective of this study was to assess the diagnostic accuracy of DWI using different ROI measurement approaches and ADC metrics in breast lesions presenting as NME lesions on dynamic contrast-enhanced (DCE) MRI. METHODS: In this retrospective study, 95 patients who underwent multiparametric MRI with DCE and DWI from September 2007 to July 2013 and who were diagnosed with a suspicious NME (BI-RADS 4/5) were included. Twenty-nine patients were excluded for lesion non-visibility on DWI (n = 24: 12 benign and 12 malignant) and poor DWI quality (n = 5: 1 benign and 4 malignant). Two readers independently assessed DWI and DCE-MRI findings in two separate randomized readings using different ADC metrics and ROI approaches. NME lesions were classified as either benign (> 1.3 × 10-3 mm2/s) or malignant (≤ 1.3 × 10-3 mm2/s). Histopathology was the standard of reference. ROC curves were plotted, and AUCs were determined. Concordance correlation coefficient (CCC) was measured. RESULTS: There were 39 malignant (59%) and 27 benign (41%) lesions in 66 (65 women, 1 man) patients (mean age, 51.8 years). The mean ADC value of the darkest part of the tumor (Dptu) achieved the highest diagnostic accuracy, with AUCs of up to 0.71. Inter-reader agreement was highest with Dptu ADC max (CCC 0.42) and lowest with the point tumor (Ptu) ADC min (CCC = - 0.01). Intra-reader agreement was highest with Wtu ADC mean (CCC = 0.44 for reader 1, 0.41 for reader 2), but this was not associated with the highest diagnostic accuracy. CONCLUSIONS: Diagnostic accuracy of DWI with ADC mapping is limited in NME lesions. Thirty-one percent of lesions presenting as NME on DCE-MRI could not be evaluated with DWI, and therefore, DCE-MRI remains indispensable. Best results were achieved using Dptu 2D ROI measurement and ADC mean.

BACKGROUND: Diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping is one of the most useful additional MRI parameters to improve diagnostic accuracy and is now often used in a multiparameric imaging setting for breast tumor detection and characterization. PURPOSE: To evaluate whether different ADC metrics can also be used for prediction of receptor status, proliferation rate, and molecular subtype in invasive breast cancer. STUDY TYPE: Retrospective. SUBJECTS: In all, 107 patients with invasive breast cancer met the inclusion criteria (mean age 57 years, range 32-87) and underwent multiparametric breast MRI. FIELD STRENGTH/SEQUENCE: 3 T, readout-segmented echo planar imaging (rsEPI) with IR fat suppression, dynamic contrast-enhanced (DCE) T1 -weighted imaging, T2 -weighted turbo-spin echo (TSE) with fatsat. ASSESSMENT: Two readers independently drew a region of interest on ADC maps on the whole tumor (WTu), and on its darkest part (DpTu). Minimum, mean, and maximum ADC values of both WTu and DpTu were compared for receptor status, proliferation rate, and molecular subtypes. STATISTICAL TESTS: Wilcoxon rank sum, Mann-Whitney U-tests for associations between radiologic features and histopathology; histogram and q-q plots, Shapiro-Wilks test to assess normality, concordance correlation coefficient for precision and accuracy; receiver operating characteristics curve analysis. RESULTS: Estrogen receptor (ER) and progesterone receptor (PR) status had significantly different ADC values for both readers. Maximum WTu (P = 0.0004 and 0.0005) and mean WTu (P = 0.0101 and 0.0136) were significantly lower for ER-positive tumors, while PR-positive tumors had significantly lower maximum WTu values (P = 0.0089 and 0.0047). Maximum WTu ADC was the only metric that was significantly different for molecular subtypes for both readers (P = 0.0100 and 0.0132) and enabled differentiation of luminal tumors from nonluminal (P = 0.0068 and 0.0069) with an area under the curve of 0.685 for both readers. DATA CONCLUSION: Maximum WTu ADC values may be used to differentiate luminal from other molecular subtypes of breast cancer. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:836-846.

PURPOSE: To develop a multiparametric [18F]FDG positron emission tomography/magnetic resonance imaging (PET/MRI) model for breast cancer diagnosis incorporating imaging biomarkers of breast tumors and contralateral healthy breast tissue. METHODS: In this prospective study and retrospective data analysis, 141 patients (mean 57 years) with an imaging abnormality detected on mammography and/or ultrasound (BI-RADS 4/5) underwent combined multiparametric [18F]FDG PET/MRI with PET/computed tomography and multiparametric MRI of the breast at 3 T. Images were evaluated and the following were recorded: for the tumor, BI-RADS descriptors on dynamic contrast-enhanced (DCE)-MRI, mean apparent diffusion co-efficient (ADCmean) on diffusion-weighted imaging (DWI), and maximum standard uptake value (SUVmax) on [18F]FDG-PET; and for the contralateral healthy breast, background parenchymal enhancement (BPE) and amount of fibroglandular tissue (FGT) on DCE-MRI, ADCmean on DWI, and SUVmax. Histopathology served as standard of reference. Uni-, bi-, and multivariate logistic regression analyses were performed to assess the relationships between malignancy and imaging features. Predictive discrimination of benign and malignant breast lesions was examined using area under the receiver operating characteristic curve (AUC). RESULTS: There were 100 malignant and 41 benign lesions (size: median 1.9, range 0.5-10 cm). The multivariate regression model incorporating significant univariate predictors identified tumor enhancement kinetics (P = 0.0003), tumor ADCmean (P

Purpose: To assess whether a radiomics and machine learning (ML) model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI can discriminate between benign and malignant breast lesions. Methods: A population of 102 patients with 120 breast lesions (101 malignant and 19 benign) detected on ultrasound and/or mammography was prospectively enrolled. All patients underwent hybrid 18F-FDG PET/MRI for diagnostic purposes. Quantitative parameters were extracted from DCE (MTT, VD, PF), DW (mean ADC of breast lesions and contralateral breast parenchyma), PET (SUVmax, SUVmean, and SUVminimum of breast lesions, as well as SUVmean of the contralateral breast parenchyma), and T2-weighted images. Radiomics features were extracted from DCE, T2-weighted, ADC, and PET images. Different diagnostic models were developed using a fine Gaussian support vector machine algorithm which explored different combinations of quantitative parameters and radiomics features to obtain the highest accuracy in discriminating between benign and malignant breast lesions using fivefold cross-validation. The performance of the best radiomics and ML model was compared with that of expert reader review using McNemar's test. Results: Eight radiomics models were developed. The integrated model combining MTT and ADC with radiomics features extracted from PET and ADC images obtained the highest accuracy for breast cancer diagnosis (AUC 0.983), although its accuracy was not significantly higher than that of expert reader review (AUC 0.868) (p = 0.508). Conclusion: A radiomics and ML model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI images can accurately discriminate between benign and malignant breast lesions. [ABSTRACT FROM AUTHOR]

To analyze the rate of potentially avoidable needle biopsies in mammographically suspicious calcifications if supplementary Contrast-Enhanced MRI (CE-MRI) is negative. Using predefined criteria, a systematic review was performed. Studies investigating the use of supplemental CE-MRI in the setting of mammographically suspicious calcifications undergoing stereotactic biopsy and published between 2000 and 2020 were eligible. Two reviewers extracted study characteristics and true positives (TP), false positives, true negatives and false negatives (FN). Specificity, in this setting equaling the number of avoidable biopsies and FN rates were calculated. The maximum pre-test probability at which post-test probabilities of a negative CE-MRI met with BI-RADS benchmarks was determined by a Fagan nomogram. Random-effects models, I2-statistics, Deek's funnel plot testing and meta-regression were employed. P-values <0.05 were considered significant. Thirteen studies investigating 1414 lesions with a cancer prevalence of 43.6% (range: 22.7–66.9%) were included. No publication bias was found (P = 0.91). CE-MRI performed better in pure microcalcification studies compared to those also including associate findings (P < 0.001). In the first group, the pooled rate of avoidable biopsies was 80.6% (95%-CI: 64.6–90.5%) while the overall and invasive cancer FN rates were 3.7% (95%-CI: 1.2–6.2%) and 1.6% (95%-CI 0–3.6%), respectively. Up to a pre-test probability of 22%, the post-test probability did not exceed 2%. A negative supplementary CE-MRI could potentially avoid 80.6% of unnecessary stereotactic biopsies in BI-RADS 4 microcalcifications at a cost of 3.7% missed breast cancers, 1.6% invasive. BI-RADS benchmarks for downgrading mammographic calcifications would be met up to a pretest probability of 22%. • A negative breast MRI can downgrade up to 80.6% of suspicious microcalcifications, potentially avoiding vacuum-assisted breast biopsies. • Up to a pretest probability of 22% , a negative breast MRI result would not exceed the 2% cancer rate required for a BI-RADS 3 category assignment. [ABSTRACT FROM AUTHOR]