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Purpose To assess whether a radiomics and machine learning (ML) model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18 F-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 18 F-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 18 F-FDG PET/MRI images can accurately discriminate between benign and malignant breast lesions.

PurposeRisk classification of primary prostate cancer in clinical routine is mainly based on prostate-specific antigen (PSA) levels, Gleason scores from biopsy samples, and tumor-nodes-metastasis (TNM) staging. This study aimed to investigate the diagnostic performance of positron emission tomography/magnetic resonance imaging (PET/MRI) in vivo models for predicting low-vs-high lesion risk (LH) as well as biochemical recurrence (BCR) and overall patient risk (OPR) with machine learning.MethodsFifty-two patients who underwent multi-parametric dual-tracer [18F]FMC and [68Ga]Ga-PSMA-11 PET/MRI as well as radical prostatectomy between 2014 and 2015 were included as part of a single-center pilot to a randomized prospective trial (NCT02659527). Radiomics in combination with ensemble machine learning was applied including the [68Ga]Ga-PSMA-11 PET, the apparent diffusion coefficient, and the transverse relaxation time-weighted MRI scans of each patient to establish a low-vs-high risk lesion prediction model (MLH). Furthermore, MBCR and MOPR predictive model schemes were built by combining MLH, PSA, and clinical stage values of patients. Performance evaluation of the established models was performed with 1000-fold Monte Carlo (MC) cross-validation. Results were additionally compared to conventional [68Ga]Ga-PSMA-11 standardized uptake value (SUV) analyses.ResultsThe area under the receiver operator characteristic curve (AUC) of the MLH model (0.86) was higher than the AUC of the [68Ga]Ga-PSMA-11 SUVmax analysis (0.80). MC cross-validation revealed 89% and 91% accuracies with 0.90 and 0.94 AUCs for the MBCR and MOPR models respectively, while standard routine analysis based on PSA, biopsy Gleason score, and TNM staging resulted in 69% and 70% accuracies to predict BCR and OPR respectively.ConclusionOur results demonstrate the potential to enhance risk classification in primary prostate cancer patients built on PET/MRI radiomics and machine learning without biopsy sampling.

Purpose To intra-individually compare the diagnostic image quality of Dixon and spectral fat suppression at 3 T. Methods Fifty consecutive patients (mean age 55.1 years) undergoing 3 T breast MRI were recruited for this prospective study. The image protocol included pre-contrast and delayed post-contrast spectral and Dixon fat-suppressed T1w series. Two independent blinded readers compared spectral and Dixon fat-suppressed series by evaluating six ordinal (1 worst to 5 best) image quality criteria (image quality, delineation of anatomical structures, fat suppression in the breast and axilla, lesion delineation and internal enhancement). Breast density and size were assessed. Data analysis included Spearman’s rank correlation coefficient and visual grading characteristics (VGC) analysis. Results Four examinations were excluded; 48 examinations in 46 patients were evaluated. In VGC analysis, the Dixon technique was superior regarding image quality criteria analysed ( P  < 0.01). Smaller breast size and lower breast density were significantly ( P  < 0.01) correlated with impaired spectral fat suppression quality. No such correlation was identified for the Dixon technique, which showed reconstruction-based water-fat mixups leading to insufficient image quality in 20.8 %. Conclusions The Dixon technique outperformed spectral fat suppression in all evaluated criteria ( P  < 0.01). Non-diagnostic examinations can be avoided by fat and water image reconstruction. The superior image quality of the Dixon technique can improve breast MRI interpretation. Key Points • Optimal fat suppression quality is necessary for optimal image interpretation • Superior fat suppression quality is achieved using the Dixon technique • Lesion margin and internal enhancement evaluation improves using the Dixon technique • Superior image quality of the Dixon technique improves breast MRI interpretation

Ultrasound-guided, large-core needle biopsy (US-LCNB) of suspicious breast lesions is acknowledged as less invasive and less expensive and less time consuming than surgical biopsy, and provides a histologic diagnosis with a comparable high degree. US-LCNB has been proven to help reduce the number of unnecessary surgeries for benign disease. Its limitations, however, are false-negative results and underestimation of disease. Thus, the demand for breast teams is to carefully adhere to the principles of triple assessment and imaging-histologic correlation, and follow-up of lesions with a specific benign histology after biopsy. Also, the acceptance of guidelines and rigorous quality controls help to reliably minimize the delay in the diagnosis of breast cancer in patients with false-negative biopsies. This paper aims to summarize the equipment and methods as well as the benefits and limitations of US-LCNB. Also, guidelines of quality assessment are suggested. Finally, recent developments which may help to overcome the limitations of US-LCNB will be discussed, i.e., directional vacuum-assisted biopsy (VAB), three-dimensional (3D) US-guided biopsy, as well as the use of tissue harmonic imaging (THI) and compound imaging (CI) during biopsy.

Purpose To evaluate the inter-/intra-observer agreement of BI-RADS-based subjective visual estimation of the amount of fibroglandular tissue (FGT) with magnetic resonance imaging (MRI), and to investigate whether FGT assessment benefits from an automated, observer-independent, quantitative MRI measurement by comparing both approaches. Materials and methods Eighty women with no imaging abnormalities (BI-RADS 1 and 2) were included in this institutional review board (IRB)-approved prospective study. All women underwent un-enhanced breast MRI. Four radiologists independently assessed FGT with MRI by subjective visual estimation according to BI-RADS. Automated observer-independent quantitative measurement of FGT with MRI was performed using a previously described measurement system. Inter-/intra-observer agreements of qualitative and quantitative FGT measurements were assessed using Cohen’s kappa (k). Results Inexperienced readers achieved moderate inter-/intra-observer agreement and experienced readers a substantial inter- and perfect intra-observer agreement for subjective visual estimation of FGT. Practice and experience reduced observer-dependency. Automated observer-independent quantitative measurement of FGT was successfully performed and revealed only fair to moderate agreement (k = 0.209–0.497) with subjective visual estimations of FGT. Conclusion Subjective visual estimation of FGT with MRI shows moderate intra-/inter-observer agreement, which can be improved by practice and experience. Automated observer-independent quantitative measurements of FGT are necessary to allow a standardized risk evaluation. Key Points • Subjective FGT estimation with MRI shows moderate intra-/inter-observer agreement in inexperienced readers . • Inter-observer agreement can be improved by practice and experience. • Automated observer-independent quantitative measurements can provide reliable and standardized assessment of FGT with MRI.

ObjectivesTo investigate whether the application of the Kaiser score for breast magnetic resonance imaging (MRI) might downgrade breast lesions that present as mammographic calcifications and avoid unnecessary breast biopsiesMethodsThis IRB-approved, retrospective, cross-sectional, single-center study included 167 consecutive patients with suspicious mammographic calcifications and histopathologically verified results. These patients underwent a pre-interventional breast MRI exam for further diagnostic assessment before vacuum-assisted stereotactic-guided biopsy (95 malignant and 72 benign lesions). Two breast radiologists with different levels of experience independently read all examinations using the Kaiser score, a machine learning–derived clinical decision-making tool that provides probabilities of malignancy by a formalized combination of diagnostic criteria. Diagnostic performance was assessed by receiver operating characteristics (ROC) analysis and inter-reader agreement by the calculation of Cohen’s kappa coefficients.ResultsApplication of the Kaiser score revealed a large area under the ROC curve (0.859–0.889). Rule-out criteria, with high sensitivity, were applied to mass and non-mass lesions alike. The rate of potentially avoidable breast biopsies ranged between 58.3 and 65.3%, with the lowest rate observed with the least experienced reader.ConclusionsApplying the Kaiser score to breast MRI allows stratifying the risk of breast cancer in lesions that present as suspicious calcifications on mammography and may thus avoid unnecessary breast biopsies.Key Points• The Kaiser score is a helpful clinical decision tool for distinguishing malignant from benign breast lesions that present as calcifications on mammography.• Application of the Kaiser score may obviate 58.3–65.3% of unnecessary stereotactic biopsies of suspicious calcifications.• High Kaiser scores predict breast cancer with high specificity, aiding clinical decision-making with regard to re-biopsy in case of negative results.

Percutaneous imaging-guided needle biopsy has increasingly become an alternative to surgical biopsy for the histologic assessment of breast lesions. Percutaneous biopsy is faster, less invasive, and less expensive than surgical biopsy. Tissue acquisition is performed with automated core needles or directional vacuum-assisted biopsy probes. Guidance for percutaneous biopsy is usually provided by stereotaxis, ultrasound, and, more recently, under the guidance of MR imaging. Imaging guidance depends on lesion type and the results of diagnostic imaging studies. This article reviews indications, advantages, limitations, and controversial issues in percutaneous imaging-guided biopsy of breast lesions under stereotactic and ultrasound guidance. The potential for new research opportunities and directions is also discussed.

Objective To develop and assess a combined reading for contrast-enhanced magnetic resonance (CE-MRI) and diffusion weighted imaging (DWI) adapted to the BI-RADS for multiparametric MRI of the breast at 3 T. Methods A total of 247 patients with histopathologically verified breast lesions were included in this IRB-approved prospective study. All patients underwent CE-MR and DWI at 3 T. MRIs were classified according to BI-RADS and assessed for apparent diffusion coefficient (ADC) values. A reading method that adapted ADC thresholds to the assigned BI-RADS classification was developed. Sensitivity, specificity, diagnostic accuracy and the area under the curve were calculated. BI-RADS-adapted reading was compared with previously published reading methods in the same population. Inter- and intra-reader variability was assessed. Results Sensitivity of BI-RADS-adapted reading was not different from the high sensitivity of CE-MRI ( P  = 0.4). BI-RADS-adapted reading maximised specificity (89.4 %), which was significantly higher compared with CE-MRI ( P  < 0.001). Previous reading methods did not perform as well as the BI-RADS method except for a logistic regression model. BI-RADS-adapted reading was more sensitive in non-mass-like enhancements (NMLE) and was more robust to inter- and intra-reader variability. Conclusion Multiparametric 3-T MRI of the breast using a BI-RADS-adapted reading is fast, simple to use and significantly improves the diagnostic accuracy of breast MRI. Keypoints • Multiparametric breast 3-T MRI with BI-RADS-adapted reading improves diagnostic accuracy. • BI-RADS-adapted reading of CE-MRI and DWI is based on established reporting guidelines. • BI-RADS-adapted reading is fast and easy to use in routine clinical practice. • BI-RADS-adapted reading is robust to intra- and inter-reader variability.

Objective The objective of our study was to evaluate the clinical application of bilateral high spatial and temporal resolution dynamic contrast-enhanced magnetic resonance imaging (HR DCE-MRI) of the breast at 7 T. Methods Following institutional review board approval 23 patients with a breast lesion (BIRADS 0, 4–5) were included in our prospective study. All patients underwent bilateral HR DCE-MRI of the breast at 7 T (spatial resolution of 0.7 mm 3 voxel size, temporal resolution of 14 s). Two experienced readers (r1, r2) and one less experienced reader (r3) independently assessed lesions according to BI-RADS®. Image quality, lesion conspicuity and artefacts were graded from 1 to 5. Sensitivity, specificity and diagnostic accuracy were assessed using histopathology as the standard of reference. Results HR DCE-MRI at 7 T revealed 29 lesions in 23 patients (sensitivity 100 % (19/19); specificity of 90 % (9/10)) resulting in a diagnostic accuracy of 96.6 % (28/29) with an AUC of 0.95. Overall image quality was excellent in the majority of cases (27/29) and examinations were not hampered by artefacts. There was excellent inter-reader agreement for diagnosis and image quality parameters (κ = 0.89–1). Conclusion Bilateral HR DCE-MRI of the breast at 7 T is feasible with excellent image quality in clinical practice and allows accurate breast cancer diagnosis. Key points • Dynamic contrast-enhanced 7-T MRI is being developed in several centres. • Bilateral high resolution DCE-MRI of the breast at 7 T is clinically applicable. • 7-T HR DCE-MRI of the breast provides excellent image quality. • 7-T HR DCE-MRI should detect breast cancer with high diagnostic accuracy.

Objective To identify which breast lesion descriptors in the ACR BI-RADS® MRI lexicon are most strongly associated with the diagnosis of breast cancer when performing breast MR imaging at 3 T. Methods 150 patients underwent breast MR imaging at 3 T. Lesion size, morphology and enhancement kinetics were assessed according to the BI-RADS® classification. Sensitivity, specificity and diagnostic accuracy were assessed. The effects of the BI-RADS® descriptors on sensitivity and specificity were evaluated. Data were analysed using logistic regression. Histopathological diagnoses were used as the standard of reference. Results The sensitivity, specificity and diagnostic accuracy of breast MRI at 3 T was 99%, 81% and 93%, respectively. In univariate analysis, the final diagnosis of malignancy was positively associated with irregular shape ( p  < 0.001), irregular margin ( p  < 0.001), heterogeneous enhancement ( p  < 0.001), Type 3 enhancement kinetics ( p  = 0.02), increasing patient age ( p  = 0.02) and larger lesion size ( p  < 0.001). In multivariate analysis, significant associations with malignancy remained for mass shape ( p  = 0.06), mass margin ( p  < 0.001), internal enhancement pattern ( p  = 0.03) and Type 3 enhancement kinetics ( p  = 0.06). Conclusion The ACR BI-RADS® breast lesion descriptors that are mostly strongly associated with breast cancer in breast MR imaging at 3 T are lesion shape, lesion margin, internal enhancement pattern and Type 3 enhancement kinetics. Key Points • 3 Tesla breast MRI allows an accurate diagnosis of breast cancer • The BI-RADS® descriptors help provide a confident diagnosis • The shape, margin, enhancement pattern and kinetics are the most important features • An irregular shape and margin, heterogeneous enhancement and type-3 kinetics indicate malignancy