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Objectives Due to its high sensitivity, DCE MRI of the breast (MRIb) is increasingly used for both screening and assessment purposes. The Kaiser score (KS) is a clinical decision algorithm, which formalizes and guides diagnosis in breast MRI and is expected to compensate for lesser reader experience. The aim was to evaluate the diagnostic performance of untrained residents using the KS compared to off-site radiologists experienced in breast imaging using only MR BI-RADS. Methods Three off-site, board-certified radiologists, experienced in breast imaging, interpreted MRIb according to the MR BI-RADS scale. The same studies were read by three residents in radiology without prior training in breast imaging using the KS. All readers were blinded to clinical information. Histology was used as the gold standard. Statistical analysis was conducted by comparing the AUC of the ROC curves. Results A total of 80 women (median age 52 years) with 93 lesions (32 benign, 61 malignant) were included. The individual within-group performance of the three expert readers (AUC 0.723–0.742) as well as the three residents was equal (AUC 0.842–0.928), p > 0.05, respectively. But, the rating of each resident using the KS significantly outperformed the experts’ ratings using the MR BI-RADS scale (p ≤ 0.05). Conclusion The KS helped residents to achieve better results in reaching correct diagnoses than experienced radiologists empirically assigning MR BI-RADS categories in a clinical “problem solving MRI” setting. These results support that reporting breast MRI benefits more from using a diagnostic algorithm rather than expert experience. Key Points • Reporting breast MRI benefits more from using a diagnostic algorithm rather than expert experience in a clinical “problem solving MRI” setting. • The Kaiser score, which provides a clinical decision algorithm for structured reporting, helps residents to reach an expert level in breast MRI reporting and to even outperform experienced radiologists using MR BI-RADS without further formal guidance.

Purpose To assess 18F-Fluoroethylcholine (18F-FEC) as a PET/MRI tracer in the evaluation of breast lesions, breast cancer aggressiveness, and prediction of lymph node status. Materials and methods This prospective, monocentric study was approved by the ethics committee and patients gave written, informed consent. This clinical trial was registered in the EudraCT database (Number 2017-003089-29). Women who presented with suspicious breast lesions were included. Histopathology was used as reference standard. Simultaneous 18F-FEC PET/MRI of the breast was performed in a prone position with a dedicated breast coil. MRI was performed using a standard protocol before and after contrast agent administration. A simultaneous read by nuclear medicine physicians and radiologists collected the imaging data of MRI-detected lesions, including the maximum standardized 18F-FEC-uptake value of breast lesions (SUVmaxT) and axillary lymph nodes (SUVmaxLN). Differences in SUVmax were evaluated with the Mann–Whitney U test. To calculate diagnostic performance, the area under the receiver operating characteristics curve (ROC) was used. Results There were 101 patients (mean age 52.3 years, standard deviation 12.0) with 117 breast lesions included (30 benign, 7 ductal carcinomas in situ, 80 invasive carcinomas). 18F-FEC was well tolerated by all patients. The ROC to distinguish benign from malignant breast lesions was 0.846. SUVmaxT was higher if lesions were malignant (p p = 0.011), and were HER2-positive (p = 0.041). SUVmaxLN was higher in metastatic lymph nodes, with an ROC of 0.761 for SUVmaxT and of 0.793 for SUVmaxLN. Conclusion Simultaneous 18F-FEC PET/MRI is safe and has the potential to be used for the evaluation of breast cancer aggressiveness, and prediction of lymph node status.

Background: Breast ultrasound identifies additional carcinomas not detected in mammography, but has a higher rate of false-positive findings which result in more unnecessary breast biopsies. Shear-Wave Elastography (SWE), an ultrasound technique used to quantify the stiffness of a lesion, showed promising results to improve the diagnostic performance of B-mode breast ultrasound but also to miss some cancers. As the stiffness of a lesion is found to be influenced by individual patient characteristics, incorporation of lesion stiffness in more individualized assessments may be key to the problem of reducing unnecessary breast biopsies without impairing the breast cancer detection rate. Thus, in this study, we evaluated whether an intelligent algorithm incorporating traditional SWE values as well as other patient and clinical variables (hereafter “intelligent SWE”) could reduce the number of unnecessary breast biopsies without impairing the breast cancer detection rate compared to traditional SWE and B-mode breast ultrasound for patients with suspicious breast lesions. Methods: We trained, tested, and validated machine learning algorithms using patient, clinical, ultrasound, and SWE information to classify breast masses. We used international, multicenter data from 857 women with BI-RADS 4 breast masses at 12 study sites in 7 countries. Patients underwent B-mode breast ultrasound, SWE, and subsequent histopathologic evaluation. 10-fold cross-validation was used to train and test the algorithms on data from 11 of the 12 sites which were further validated using the additional site’s data. The results of B-mode breast ultrasound, traditional SWE, and intelligent SWE were compared to the gold standard of histopathologic evaluation. We calculated sensitivity, specificity, and AUROC and used McNemar tests to test for significant differences in diagnostic performance. Results: The mean age was 49.5 years (SD 16.3) and 42.2% breast masses (n=362 of 857) were found to be malignant as confirmed by histopathology. In the external validation set (n=285), traditional SWE showed a significantly higher diagnostic performance compared to B-mode breast ultrasound (P < 0.001), whereas intelligent SWE outperformed both B-mode breast ultrasound and traditional SWE (P < 0.001). The neural network algorithm showed a significantly higher diagnostic performance compared to the Logistic Regression with Elastic Net Penalty (P = 0.004). The neural network algorithm achieved a sensitivity of 100% (95% CI 97.1 to 100%, 126 of 126) and a specificity of 50.3% (95% CI 42.3 to 58.3%, 80 of 159); the number of unnecessary biopsies were reduced by 50.3% (79 vs. 159) without missing any cancer compared to B-mode breast ultrasound. Model-agnostic variable importance plots to provide insights into the model predictions showed that the three most important variables for intelligent SWE were patient age followed by Shear-Wave velocity and orientation of the lesion (parallel vs. not parallel) in B-mode ultrasound. Conclusion: This is the first evidence which suggests that the majority of false-positive breast biopsies could be safely avoided by using intelligent SWE without impairing breast cancer detection rates. These results may be helpful in their ability to reduce treatment burden for patients, providers, and healthcare systems. Trial registration: NCT02638935. Funding: Siemens Medical Solutions USA, Inc Diagnostic Performance ComparisonB-mode Breast UltrasoundTraditional Shear-Wave ElastographyIntelligent Shear-Wave Elastography – Logistic Regression with Elastic Net PenaltyIntelligent Shear-Wave Elastography – neural networkAUROC – value (95% CI)–0.84 (0.79-0.89)0.93 (0.90-0.95)0.93 (0.90-0.96)Sensitivity – % (95% CI); no.100% (97.1-100%); 126 of 12697.6% (93.2-99.5%); 123 of 126100% (97.1-100%); 126 of 126100% (97.1-100%); 126 of 126Specificity – % (95% CI); no.0% (0.0-2.3%); 0 of 15923.9% (17.5-31.3%); 38 of 15936.5% (29.0-44.5%); 58 of 15950.3% (42.3-58.3%); 80 of 159Negative predictive value – % (95% CI); no.–92.7% (80.1-98.5%); 38 of 41100% (93.8-100%); 58 of 58100% (95.5-100%); 80 of 80Positive predictive value – % (95% CI); no.44.2% (38.4-50.2); 126 of 28550.4% (44.0-56.8%); 123 of 24455.5% (48.8-62.1%); 126 of 22761.5% (54.4-68.2%); 126 of 205 Citation Format: André Pfob, Chris Sidey-Gibbons, Richard G. Barr, Volker Duda, Zaher Alwafai, Corinne Balleyguier, Dirk-André Clevert, Sarah Fastner, Christina Gomez, Manuela Goncalo, Ines Gruber, Markus Hahn, André Hennigs, Chi Ho, Panagiotis Kapetas, Sheng-Chieh Lu, Juliane Nees, Ralf Ohlinger, Fabian Riedel, Matthieu Rutten, Benedikt Schaefgen, Anne Stieber, Riku Togawa, Mitsuhiro Tozaki, Sebastian Wojcinski, Cai Xu, Geraldine Rauch, Joerg Heil, Michael Golatta. Intelligent shear-wave elastography to reduce unnecessary biopsies in breast cancer diagnosis (INSPiRED 002): An international, multicenter analysis [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD11-05.

Hintergrund Radiologische Untersuchungen nicht nur als bloße Bilder, sondern als Datenquelle zu betrachten, ist zum modernen Paradigma der diagnostischen Bildgebung geworden. Dieser Perspektivwechsel hat sich besonders in der Brustbildgebung durchgesetzt, ermöglicht er doch, aus der Informatik abgeleitete Verfahren anzuwenden, innovative klinische Anwendungen zu realisieren und bereits etablierte Methoden zu verfeinern. In diesem Zusammenhang sind die Begriffe „bildgebender Biomarker“, „Radiomics“ und „künstliche Intelligenz“ von zentraler Bedeutung. Diese Methoden versprechen nichtinvasive, kostengünstige (z. B. im Vergleich zu Multigen-Arrays), workflow-freundliche (automatisiert, nur eine Untersuchung, sofortige Ergebnisse) und klinisch relevante Informationen. Methoden und Ergebnisse Dieser Artikel wurde als narratives Review zu dem besagten Paradigma im Bereich der Brustbildgebung konzipiert. Der Schwerpunkt liegt auf den Schlüsselkonzepten und wichtigen Schlagworten. Für alle Bereiche der Brustbildgebung werden beispielhafte Studien diskutiert. Schlussfolgerung Die Interpretation von radiologischen Untersuchungen als Datenquelle verspricht eine Optimierung der Behandlung von Brustkrebspatientinnen im Zeitalter der Präzisionsmedizin, weil damit die Diagnose verfeinert und eine individualisierte Behandlung erreicht werden könnte. Kernaussagen

Objectives To evaluate the diagnostic performance in the assessment setting of three protocols: one-view wide-angle digital breast tomosynthesis (WA-DBT) with synthetic mammography (SM), two-view WA-DBT/SM, and two-view digital mammography (DM). Methods Included in this retrospective study were patients who underwent bilateral two-view DM and WA-DBT. SM were reconstructed from the WA-DBT data. The standard of reference was histology and/or 2 years follow-up. Included were 205 women with 179 lesions (89 malignant, 90 benign). Four blinded readers randomly evaluated images to assess density, lesion type, and level of suspicion according to BI-RADS. Three protocols were evaluated: two-view DM, one-view (mediolateral oblique) WA-DBT/SM, and two-view WA-DBT/SM. Detection rate, sensitivity, specificity, and accuracy were calculated and compared using multivariate analysis. Reading time was assessed. Results The detection rate was higher with two-view WA-DBT/SM (p = 0.063). Sensitivity was higher for two-view WA-DBT/SM compared to two-view DM (p = 0.001) and one-view WA-DBT/SM (p = 0.058). No significant differences in specificity were found. Accuracy was higher with both one-view WA-DBT/SM and two-view WA-DBT/SM compared to DM (p = 0.003 and > 0.001, respectively). Accuracy did not differ between one- and two-view WA-DBT/SM. Two-view WA-DBT/SM performed better for masses and asymmetries. Reading times were significantly longer when WA-DBT was evaluated. Conclusions One-view and two-view WA-DBT/SM can achieve a higher diagnostic performance compared to two-view DM. The detection rate and sensitivity were highest with two-view WA-DBT/SM. Two-view WA-DBT/SM appears to be the most appropriate tool for the assessment of breast lesions. Key Points • Detection rate with two-view wide-angle digital breast tomosynthesis (WA-DBT) is significantly higher than with two-view digital mammography in the assessment setting. • Diagnostic accuracy of one-view and two-view WA-DBT with synthetic mammography (SM) in the assessment setting is higher than that of two-view digital mammography. • Compared to one-view WA-DBT with SM, two-view WA-DBT with SM seems to be the most appropriate tool for the assessment of breast lesions.

To investigate whether a machine learning (ML)-based radiomics model applied toEighty-six patients with 98 BC lesions (Luminal A = 10, Luminal B = 51, HER2+ = 12, TN = 25) were included and underwent simultaneousEight radiomics models were built based on different combinations of quantitative parameters and/or radiomic features. The best performance (AUROC 0.887, accuracy 82.8%, sensitivity 79.7%, specificity 86%, PPV 85.3%, NPV 80.8%) was found for the model combining first order, neighborhood gray level dependence matrix and size zone matrix-based radiomics features extracted from ADC and PET images.A ML-based radiomics model applied to

Purpose To analyze the rate of potentially avoidable needle biopsies in mammographically suspicious calcifications if supplementary Contrast-Enhanced MRI (CE-MRI) is negative. Methods 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, Highlights • 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.

Objectives MRI is an integral part of breast cancer screening in high-risk patients. We investigated whether the application of the Kaiser score, a clinical decision-support tool, may be used to exclude malignancy in contrast-enhancing lesions classified as BI-RADS 4 on breast MRI screening exams. Methods This retrospective study included 183 consecutive, histologically proven, suspicious (MR BI-RADS 4) lesions detected within our local high-risk screening program. All lesions were evaluated according to the Kaiser score for breast MRI by three readers blinded to the final histopathological diagnosis. The Kaiser score ranges from 1 (lowest, cancer very unlikely) to 11 (highest, cancer very likely) and reflects increasing probabilities of malignancy, with scores greater than 4 requiring biopsy. Receiver operating characteristic (ROC) curve analysis was used to evaluate diagnostic accuracy. Results There were 142 benign and 41 malignant lesions, diagnosed in 159 patients (mean age, 43.6 years). Median Kaiser scores ranged between 2 and 5 in benign and 7 and 8 in malignant lesions. For all lesions, the Kaiser score’s accuracy, represented by the area under the curve (AUC), ranged between 86.5 and 90.2. The sensitivity of the Kaiser score was high, between 95.1 and 97.6% for all lesions, and was best in mass lesions. Application of the Kaiser score threshold for malignancy (≤ 4) could have potentially avoided 64 (45.1%) to 103 (72.5%) unnecessary biopsies in 142 benign lesions previously classified as BI-RADS 4. Conclusions The use of Kaiser score in high-risk MRI screening reliably excludes malignancy in more than 45% of contrast-enhancing lesions classified as BI-RADS 4. Key Points • The Kaiser score shows high diagnostic accuracy in identifying malignancy in contrast-enhancing lesions in patients undergoing high-risk screening for breast cancer. • The application of the Kaiser score may avoid > 45% of unnecessary breast biopsies in high-risk patients. • The Kaiser score aids decision-making in high-risk breast cancer MRI screening programs.

Purpose To assess the additional value of quantitative tCho evaluation to diagnose malignancy and lymph node metastases in suspicious lesions on multiparametric breast MRI (mpMRI, BI-RADS 4, and BI-RADS 5). Methods One hundred twenty-one patients that demonstrated suspicious multiparametric breast MRI lesions using DCE, T2w, and diffusion-weighted (DW) images were prospectively enrolled in this IRB-approved study. All underwent single-voxel proton MR spectroscopy (1H-MRS, point-resolved spectroscopy sequence, TR 2000 ms, TE 272 ms) with and without water suppression. The total choline (tCho) amplitude was measured and normalized to millimoles/liter according to established methodology by two independent readers (R1, R2). ROC-analysis was employed to predict malignancy and lymph node status by tCho results. Results One hundred three patients with 74 malignant and 29 benign lesions had full 1H-MRS data. The area under the ROC curve (AUC) for prediction of malignancy was 0.816 (R1) and 0.809 (R2). A cutoff of 0.8 mmol/l tCho could diagnose malignancy with a sensitivity of > 95%. For prediction of lymph node metastases, tCho measurements achieved an AUC of 0.760 (R1) and 0.788 (R2). At tCho levels Conclusion Quantitative tCho evaluation from 1H-MRS allowed diagnose malignancy and lymph node status in breast lesions suspicious on multiparametric breast MRI. tCho therefore demonstrated the potential to downgrade suspicious mpMRI lesions and stratify the risk of lymph node metastases for improved patient management. Key Points • Quantitative tCho evaluation can distinguish benign from malignant breast lesions suspicious after multiparametric MRI assessment. • Quantitative tCho levels are associated with lymph node status in breast cancer. • Quantitative tCho levels are higher in hormonal receptor positive compared to hormonal receptor negative lesions.

Objectives AI-based algorithms for medical image analysis showed comparable performance to human image readers. However, in practice, diagnoses are made using multiple imaging modalities alongside other data sources. We determined the importance of this multi-modal information and compared the diagnostic performance of routine breast cancer diagnosis to breast ultrasound interpretations by humans or AI-based algorithms. Methods Patients were recruited as part of a multicenter trial (NCT02638935). The trial enrolled 1288 women undergoing routine breast cancer diagnosis (multi-modal imaging, demographic, and clinical information). Three physicians specialized in ultrasound diagnosis performed a second read of all ultrasound images. We used data from 11 of 12 study sites to develop two machine learning (ML) algorithms using unimodal information (ultrasound features generated by the ultrasound experts) to classify breast masses which were validated on the remaining study site. The same ML algorithms were subsequently developed and validated on multi-modal information (clinical and demographic information plus ultrasound features). We assessed performance using area under the curve (AUC). Results Of 1288 breast masses, 368 (28.6%) were histopathologically malignant. In the external validation set (n = 373), the performance of the two unimodal ultrasound ML algorithms (AUC 0.83 and 0.82) was commensurate with performance of the human ultrasound experts (AUC 0.82 to 0.84; p for all comparisons > 0.05). The multi-modal ultrasound ML algorithms performed significantly better (AUC 0.90 and 0.89) but were statistically inferior to routine breast cancer diagnosis (AUC 0.95, p for all comparisons ≤ 0.05). Conclusions The performance of humans and AI-based algorithms improves with multi-modal information. Key Points • The performance of humans and AI-based algorithms improves with multi-modal information. • Multimodal AI-based algorithms do not necessarily outperform expert humans. • Unimodal AI-based algorithms do not represent optimal performance to classify breast masses.

Breast ultrasound identifies additional carcinomas not detected in mammography but has a higher rate of false-positive findings. We evaluated whether use of intelligent multi-modal shear wave elastography (SWE) can reduce the number of unnecessary biopsies without impairing the breast cancer detection rate.We trained, tested, and validated machine learning algorithms using SWE, clinical, and patient information to classify breast masses. We used data from 857 women who underwent B-mode breast ultrasound, SWE, and subsequent histopathologic evaluation at 12 study sites in seven countries from 2016 to 2019. Algorithms were trained and tested on data from 11 of the 12 sites and externally validated using the additional site's data. We compared findings to the histopathologic evaluation and compared the diagnostic performance between B-mode breast ultrasound, traditional SWE, and intelligent multi-modal SWE.In the external validation set (n = 285), intelligent multi-modal SWE showed a sensitivity of 100% (95% CI, 97.1-100%, 126 of 126), a specificity of 50.3% (95% CI, 42.3-58.3%, 80 of 159), and an area under the curve of 0.93 (95% CI, 0.90-0.96). Diagnostic performance was significantly higher compared to traditional SWE and B-mode breast ultrasound (P 0.001). Unlike traditional SWE, positive-predictive values of intelligent multi-modal SWE were significantly higher compared to B-mode breast ultrasound. Unnecessary biopsies were reduced by 50.3% (79 versus 159, P 0.001) without missing cancer compared to B-mode ultrasound.The majority of unnecessary breast biopsies might be safely avoided by using intelligent multi-modal SWE. These results may be helpful to reduce diagnostic burden for patients, providers, and healthcare systems.

Item does not contain fulltext BACKGROUND: Shear wave elastography (SWE) and strain elastography (SE) have shown promising potential in breast cancer diagnostics by evaluating the stiffness of a lesion. Combining these two techniques could further improve the diagnostic performance. We aimed to exploratorily define the cut-offs at which adding combined SWE and SE to B-mode breast ultrasound could help reclassify Breast Imaging Reporting and Data System (BI-RADS) 3-4 lesions to reduce the number of unnecessary breast biopsies. METHODS: We report the secondary results of a prospective, multicentre, international trial (NCT02638935). The trial enrolled 1288 women with BI-RADS 3 to 4c breast masses on conventional B-mode breast ultrasound. All patients underwent SWE and SE (index test) and histopathologic evaluation (reference standard). Reduction of unnecessary biopsies (biopsies in benign lesions) and missed malignancies after recategorising with SWE and SE were the outcome measures. RESULTS: On performing histopathologic evaluation, 368 of 1288 breast masses were malignant. Following the routine B-mode breast ultrasound assessment, 53.80% (495 of 920 patients) underwent an unnecessary biopsy. After recategorising BI-RADS 4a lesions (SWE cut-off >/=3.70 m/s, SE cut-off >/=1.0), 34.78% (320 of 920 patients) underwent an unnecessary biopsy corresponding to a 35.35% (320 versus 495) reduction of unnecessary biopsies. Malignancies in the new BI-RADS 3 cohort were missed in 1.96% (12 of 612 patients). CONCLUSION: Adding combined SWE and SE to routine B-mode breast ultrasound to recategorise BI-RADS 4a patients could help reduce the number of unnecessary biopsies in breast diagnostics by about 35% while keeping the rate of undetected malignancies below the 2% ACR BI-RADS 3 definition.

Purpose Head-to-head comparison of Diffusion Weighted Imaging (DWI) and Acoustic Radiation Force Impulse (ARFI) elastography regarding the characterization of breast lesions in an assessment setting. Method Patients undergoing an ultrasound examination including ARFI and an MRI protocol including DWI for the characterization of a BI-RADS 3–5 breast lesion between 06/2013 and 10/2016 were eligible for inclusion in this retrospective, IRB-approved study. 60 patients (30–84 years, median 50) with a median lesion size of 16 mm (range 5–55 mm) were included. The maximum shear wave velocity (SWVmax) and mean apparent diffusion coefficient (ADCmean) for each lesion were retrospectively evaluated by a radiologist experienced in the technique. Histology was the reference standard. Diagnostic performances of ARFI and DWI were assessed using ROC curve analysis. Spearman’s rank correlation coefficient and multivariate logistic regression were used to investigate the independence of both tests regarding their diagnostic information to distinguish benign from malignant lesions. Results Corresponding areas under the ROC curve for differentiation of benign (n = 16) and malignant (n = 49) lesions were 0.822 (ARFI) and 0.871 (DWI, p-value = 0.48). SWVmax and ADCmean values showed a significant negative correlation (ρ = −0.501, p-value Conclusion Significant correlation between quantitative findings of ARFI and DWI in breast lesions exists. Thus, ARFI provides similar diagnostic information as a DWI-including protocol of an additional “problem-solving” MRI for the characterization of a sonographically evident breast lesion, improving the immediate patient management in the assessment setting.

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. 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). 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

Background Contrast-enhanced breast magnetic resonance imaging (MRI) is the most sensitive method for detection of breast cancer. The further spread of breast MRI is limited by the complicated examination procedure and the need for intravenously administered contrast media. Objectives Can diffusion-weighted imaging (DWI) replace contrast-enhanced sequences to achieve an unenhanced breast MRI examination? Materials and methods Narrative review and meta-analytic assessment of previously published studies. Results DWI can visualize breast lesions and distinguish benign from malignant findings. It is thus a valid alternative to contrast-enhanced sequences. As an additional technique, the use of DWI can reduce the numbers of unnecessary breast biopsies. The lack of robustness leading to variable sensitivity that is currently lower than that of contrast-enhanced breast MRI is a disadvantage of DWI. Conclusions Presently, DWI can be recommended as an integral part of clinical breast MRI protocols. The application as a stand-alone technique within unenhanced protocols is still under evaluation.

Purpose To predict detection rates (DR) in second-look ultrasound of MRI-detected breast lesions by systematically combining clinical and anthropomorphic features. Methods A total of 104 suspicious breast-lesions, that were initially detected on breast MRI and underwent subsequent SLU from January 2013 through December 2013, were evaluated in this retrospective analysis. All images were reviewed by an experienced radiologist for this study. Both anthropomorphic, spatial and BI-RADS lesion features were recorded. Uni- and multivariate Classification and Regression Trees (CRT) statistics were used to predict SLU DR by these features. Results Among 104 MRI-detected lesions, 58 (55.8%) showed a correlate on SLU. In univariate analysis, homogeneous fatty or dense fibro-glandular-tissue-composition (FGT) as assessed by ultrasound, segmental non-mass-distribution pattern and small breast size as assessed by MRI were significantly associated with higher DR on SLU. The remaining BI-RADS features did not significantly affect SLU DR according to our data. The predictive model could stratify the likelihood of SLU correlates as high, intermediate and low according to FGT, lesion type, size and position. Conclusions By systematically combining the features FGT, lesion type, size and position, we could predict SLU DR of MRI-detected breast lesions. This may help to decide the preferable method for lesion biopsy or follow-up in clinical practice.

The aim of this study was to assess the potential of machine learning with multiparametric magnetic resonance imaging (mpMRI) for the early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) and of survival outcomes in breast cancer patients.This institutional review board-approved prospective study included 38 women (median age, 46.5 years; range, 25-70 years) with breast cancer who were scheduled for NAC and underwent mpMRI of the breast at 3 T with dynamic contrast-enhanced (DCE), diffusion-weighted imaging (DWI), and T2-weighted imaging before and after 2 cycles of NAC. For each lesion, 23 features were extracted: qualitative T2-weighted and DCE-MRI features according to BI-RADS (Breast Imaging Reporting and Data System), quantitative pharmacokinetic DCE features (mean plasma flow, volume distribution, mean transit time), and DWI apparent diffusion coefficient (ADC) values. To apply machine learning to mpMRI, 8 classifiers including linear support vector machine, linear discriminant analysis, logistic regression, random forests, stochastic gradient descent, decision tree, adaptive boosting, and extreme gradient boosting (XGBoost) were used to rank the features. Histopathologic residual cancer burden (RCB) class (with RCB 0 being a pCR), recurrence-free survival (RFS), and disease-specific survival (DSS) were used as the standards of reference. Classification accuracy with area under the receiving operating characteristic curve (AUC) was assessed using all the extracted qualitative and quantitative features for pCR as defined by RCB class, RFS, and DSS using recursive feature elimination. To overcome overfitting, 4-fold cross-validation was used.Machine learning with mpMRI achieved stable performance as shown by mean classification accuracies for the prediction of RCB class (AUC, 0.86) and DSS (AUC, 0.92) based on XGBoost and the prediction of RFS (AUC, 0.83) with logistic regression. The XGBoost classifier achieved the most stable performance with high accuracies compared with other classifiers. The most relevant features for the prediction of RCB class were as follows: changes in lesion size, complete pattern of shrinkage, and mean transit time on DCE-MRI; minimum ADC on DWI; and peritumoral edema on T2-weighted imaging. The most relevant features for prediction of RFS were as follows: volume distribution, mean plasma flow, and mean transit time; DCE-MRI lesion size; minimum, maximum, and mean ADC with DWI. The most relevant features for prediction of DSS were as follows: lesion size, volume distribution, and mean plasma flow on DCE-MRI, and maximum ADC with DWI.Machine learning with mpMRI of the breast enables early prediction of pCR to NAC as well as survival outcomes in breast cancer patients with high accuracy and thus may provide valuable predictive information to guide treatment decisions.

OBJECTIVES The aim of this study was to evaluate the detection rate and diagnostic performance of 2-dimensional synthetic mammography (SM) as an adjunct to wide-angle digital breast tomosynthesis (WA-DBT) compared with digital mammography (DM) alone or to DM in combination with WA-DBT. MATERIALS AND METHODS There were 205 women with 179 lesions included in this retrospective reader study. Patients underwent bilateral, 2-view (2v) DM and WA-DBT between March and June 2015. The standard of reference was histology and/or 1-year stability at follow-up. Four blinded readers randomly evaluated images according to the BI-RADS lexicon from 3 different protocols: 2v DM alone, 2v DM with 2v WA-DBT, and 2v SM with 2v WA-DBT. Detection rate, sensitivity, specificity, and accuracy were calculated and compared using multivariate analysis. Readers' confidence and image quality were evaluated. RESULTS The detection rate ranged from 68.7% to 79.9% for DM, 76.5% to 84.4% for DM with WA-DBT, and 73.2% to 84.9% for SM with WA-DBT. Sensitivity and accuracy were significantly higher when DBT was available (P < 0.001). Specificity did not differ significantly between DM only, DM with WA-DBT, or SM with WA-DBT (P ≥ 0.846). Wide-angle DBT combined readings did not differ between SM and DM in terms of sensitivity, specificity, and accuracy (P ≥ 0.341). Readers' confidence and image quality was rated good to excellent. CONCLUSIONS Wide-angle DBT combined with DM or SM increases sensitivity and accuracy without reducing specificity compared with DM alone. Wide-angle DBT combined readings did not differ between SM and DM; therefore, SM should replace DM for combined readings with WA-DBT.

Purpose: to assess whether a radiomics and machine learning (ML) model combining quantitative parameters and radiomics features extracted from synchronized multiparametric 18F-FDG PET/MRI images can differentiate benign and malignant breast lesions.Methods: 102 consecutive patients with 120 BI-RADS 0, 4 and 5 breast lesions (101 malignant, 19 benign) detected by ultrasound and/or mammography were prospectively enrolled and underwent hybrid 18F-FDG PET/MRI for diagnostic purposes. Quantitative parameters and radiomics features were extracted from dynamic contrast-enhanced (MTT, VD, PF), diffusion (ADCmean of breast lesions and contralateral breast parenchyma), PET (SUVmax, mean and minimum of breast lesions, SUVmean of uni- and contralateral breast parenchyma) and T2-w images. Different diagnostic models were developed using a fine gaussian support vector machine algorithm and exploring different combinations of quantitative parameters and radiomics features to obtain the highest accuracy in discriminating benign from malignant breast lesions using a 5-fold cross validation. The performance of the best radiomics and ML model was compared with that of expert readers review physician using the McNemar 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) and was higher (AUC 0.868) yet not significant to expert readers review (p=0.508).Conclusion: A radiomics and ML model combining quantitative parameters and radiomics features extracted from synchronized multiparametric 18F-FDG PET/MRI images can accurately discriminate benign from malignant breast lesions.

Objectives Due to its high sensitivity, DCE MRI of the breast (bMRI) is increasingly used for both screening and assessment purposes. The high number of detected lesions poses a significant logistic challenge in clinical practice. The aim was to evaluate a temporally and spatially resolved (4D) radiomics approach to distinguish benign from malignant enhancing breast lesions and thereby avoid unnecessary biopsies. Methods This retrospective study included consecutive patients with MRI-suspicious findings (BI-RADS 4/5). Two blinded readers analyzed DCE images using a commercially available software, automatically extracting BI-RADS curve types and pharmacokinetic enhancement features. After principal component analysis (PCA), a neural network–derived A.I. classifier to discriminate benign from malignant lesions was constructed and tested using a random split simple approach. The rate of avoidable biopsies was evaluated at exploratory cutoffs (C1, 100%, and C2, ≥ 95% sensitivity). Results Four hundred seventy (295 malignant) lesions in 329 female patients (mean age 55.1 years, range 18–85 years) were examined. Eighty-six DCE features were extracted based on automated volumetric lesion analysis. Five independent component features were extracted using PCA. The A.I. classifier achieved a significant (p < .001) accuracy to distinguish benign from malignant lesion within the test sample (AUC: 83.5%; 95% CI: 76.8–89.0%). Applying identified cutoffs on testing data not included in training dataset showed the potential to lower the number of unnecessary biopsies of benign lesions by 14.5% (C1) and 36.2% (C2). Conclusion The investigated automated 4D radiomics approach resulted in an accurate A.I. classifier able to distinguish between benign and malignant lesions. Its application could have avoided unnecessary biopsies. Key Points • Principal component analysis of the extracted volumetric and temporally resolved (4D) DCE markers favored pharmacokinetic modeling derived features. • An A.I. classifier based on 86 extracted DCE features achieved a good to excellent diagnostic performance as measured by the area under the ROC curve with 80.6% (training dataset) and 83.5% (testing dataset). • Testing the resulting A.I. classifier showed the potential to lower the number of unnecessary biopsies of benign breast lesions by up to 36.2%, p < .001 at the cost of up to 4.5% (n = 4) false negative low-risk cancers.

Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology "imaging biomarker", "radiomics", and "artificial intelligence" are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information. This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed. Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine. · In conventional breast imaging, examinations are interpreted based on patterns perceivable by visual inspection.. · The radiomics paradigm treats breast images as a source of data, containing information beyond what is visible to our eyes.. · This results in radiomic signatures that may be considered as imaging biomarkers, as they provide diagnostic, predictive, and prognostic information.. · Radiomics derived imaging biomarkers may be used to individualize breast cancer treatment in the era of precision medicine.. · The concept and key research of radiomics in the field of breast imaging will be discussed in this narrative review..· Dietzel M, Clauser P, Kapetas P et al. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. Fortschr Röntgenstr 2021; 193: 898 - 908. Radiologische Untersuchungen nicht nur als bloße Bilder, sondern als Datenquelle zu betrachten, ist zum modernen Paradigma der diagnostischen Bildgebung geworden. Dieser Perspektivwechsel hat sich besonders in der Brustbildgebung durchgesetzt, ermöglicht er doch aus der Informatik abgeleitete Verfahren anzuwenden, innovative klinische Anwendungen zu realisieren und bereits etablierte Methoden zu verfeinern. In diesem Zusammenhang sind die Begriffe „bildgebender Biomarker“, „Radiomics“ und „künstliche Intelligenz“ von zentraler Bedeutung. Diese Methoden versprechen nichtinvasive, kostengünstige (z. B. im Vergleich zu Multigen-Arrays), Workflow-freundliche (automatisiert, nur eine Untersuchung, sofortige Ergebnisse) und klinisch relevante Informationen. Dieser Artikel wurde als narratives Review zu dem besagten Paradigma im Bereich der Brustbildgebung konzipiert. Der Schwerpunkt liegt auf den Schlüsselkonzepten und wichtigen Schlagworten. Für alle Bereiche der Brustbildgebung werden beispielhafte Studien diskutiert. Die Interpretation von radiologischen Untersuchungen als Datenquelle verspricht eine Optimierung der Behandlung von Brustkrebspatientinnen im Zeitalter der Präzisionsmedizin, da hiermit die Diagnose verfeinert und eine individualisierte Behandlung erreicht werden könnte. · In der konventionellen Brustbildgebung werden Untersuchungen anhand von visuell erkennbaren Mustern interpretiert.. · Das Radiomics-Paradigma behandelt radiologische Brustuntersuchungen hingegen als abstrakte Datenquelle, in der Informationen zu finden sind, die über visuell erkennbare Muster hinausgehen.. · Derartige radiomische Signaturen können als bildgebende Biomarker angesehen werden, da sie diagnostische, prädiktive und prognostische Informationen liefern.. · Derartige bildgebende Biomarker können im Zeitalter der Präzisionsmedizin zur Individualisierung der Brustkrebsbehandlung eingesetzt werden.. · In diesem narrativen Übersichtsartikel stellen wir das Radiomics-Paradigma auf dem Gebiet der Brustkrebsbildgebung anhand von exemplarischen Literaturbeispielen dar..

To investigate a risk stratification strategy for lesions of uncertain malignant potential (B3) diagnosed by vacuum-assisted breast biopsy (VABB) of mammographic microcalcifications.Patients who underwent VABB for microcalcification-only lesions with a diagnosis of B3 and subsequent surgery were included in this retrospective, IRB-approved study. Seventy-six B3-lesions (final histology: 66 benign, 10 malignant) were included (Tr). Data on B3 lesion type and presence of atypia, microcalcification characteristics (BI-RADS), removal at biopsy and concomitant lesions were collected. After univariate analysis (Chi-square test), data were combined into a risk stratification algorithm by using a ten-fold, cross-validated Classification and Regression Tree analysis (CRT). The algorithm was tested on a testing dataset (Te) of 23 B3-lesions (six malignant, 17 benign).Malignancy was more frequent in women with a concomitant cancer (P 0.001) and highly suspicious microcalcifications (P 0.001). The CRT algorithm retained three characteristics: morphology; presence of atypia; presence of concomitant cancer. The algorithm identified 25/76 (32.9 %,Tr) and 6/23 (26.1 %,Te) lesions at low risk of malignancy. No malignant cases were identified at surgery (0/31). There were 3/76 (3.9 %,Tr) and 1/23 (4.3 %,Te) lesions assigned as high-risk by the algorithm and confirmed at surgery (4/4). In the remaining lesions (48/76, 63.1 %,Tr; 16/23, 69.6 %,Te), malignancy rates varied between 9% and 88.4 %; thus, surgery could not have been avoided.We constructed and tested a risk stratification algorithm for B3 microcalcifications, including clinical, imaging, and pathological features, to assign probabilities of malignancy, which has the potential to reduce unnecessary surgeries.

The aim of this study was to investigate the potential of magnetic resonance imaging (MRI) for a non-invasive synergistic assessment of tumor microenvironment (TME) hypoxia and induced neovascularization for the identification of aggressive breast cancer. Fifty-three female patients with breast cancer underwent multiparametric breast MRI including quantitative blood-oxygen-level-dependent (qBOLD) imaging for hypoxia and vascular architecture mapping for neovascularization. Quantitative MRI biomarker maps of oxygen extraction fraction (OEF), metabolic rate of oxygen (MRO2), mitochondrial oxygen tension (mitoPO2), microvessel radius (VSI), microvessel density (MVD), and microvessel type indicator (MTI) were calculated. Histopathology was the standard of reference. Histopathological markers (vascular endothelial growth factor receptor 1 (FLT1), podoplanin, hypoxia-inducible factor 1-alpha (HIF-1alpha), carbonic anhydrase 9 (CA IX), vascular endothelial growth factor C (VEGF-C)) were used to confirm imaging biomarker findings. Univariate and multivariate regression analyses were performed to differentiate less aggressive luminal from aggressive non-luminal (HER2-positive, triple negative) malignancies and assess the interplay between hypoxia and neoangiogenesis markers. Aggressive non-luminal cancers (n = 40) presented with significantly higher MRO2 (i.e., oxygen consumption), lower mitoPO2 values (i.e., hypoxia), lower MTI, and higher MVD than less aggressive cancers (n = 13). Data suggest that a model derived from OEF, mitoPO2, and MVD can predict tumor proliferation rate. This novel MRI approach, which can be easily implemented in routine breast MRI exams, aids in the non-invasive identification of aggressive breast cancer.

PURPOSE: To determine whether MRI-detected suspicious (BIRADS 4 & 5) breast lesions can be downgraded using second-look ultrasound (SLU) and thus reduce unnecessarily performed breast biopsies. MATERIALS METHODS: A retrospective single-center review of consecutive patients, who underwent breast MRI studies during a 12-month time period was performed. 94 patients with 103 lesions undergoing SLU of incidentally detected MRI BI-RADS 4&5 lesions which were not identified on previous ultrasound were included in the study. The SLU detection rate and SLU features of the lesions were assessed. Histology (91/103) or two year follow up (n=12) were defined as the reference standard for lesion diagnosis. RESULTS: 57 (55.3%) of the 103 lesions were identified on SLU. 17 of the identified lesions were malignant (29.8%). Lesions detected on ultrasound presented on MRI as masses in 66.7% (38/57) and non-mass in 33.3% (19/57). Our findings showed that it is possible to distinguish between malignant and benign lesions with SLU. The results were significant (p

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.

ObjectivesThe aim of this study was to assess the potential of noncontrast magnetic resonance imaging (NC-MRI) with diffusion-weighted imaging (DWI) in characterization of breast lesions in comparison to dynamic contrast-enhanced MRI (DCE-MRI) at 3 T.Materials and MethodsConsecutive patients with co

Microcalcifications are a common finding in mammography and usually require invasive procedures to diagnose or exclude malignancy. As many microcalcifications are due to benign lesions, we wanted to assess whether breast MRI as an additional diagnostic tool may be used to distinguish benign from malignant in this setting.Eligible for this retrospective, IRB-approved observational study were 858 consecutive patients (mean age 54±11years) undergoing stereotactically-guided biopsies of suspicious mammographic microcalcifications during three year at our institution. Finally included were 152 patients who also underwent breast MRI8weeks prior to biopsy. In case of malignant or lesions of uncertain malignant potential, subsequent surgery was performed. Benign findings were confirmed by imaging follow-up. BI-RADS category assignments from the original mammography and breast MRI reports were compared to the final diagnosis (benign vs. malignant) to determine diagnostic benchmarks.Histopathology revealed 81 benign (53.3%), 41 DCIS (27%) and 30 (19.7%) invasive cancers. Sensitivity, specificity, positive and negative predictive values for breast MRI were 97.2% (69/71), 39.5% (32/81), 58.5% (69/118) and 94.1% (32/34), respectively. Thus, 32/81 unnecessary biopsies in benign lesions (39.5%) may have been avoided, missing 2/71 malignant lesions (2.8%), both DCIS G2.Breast MRI as an additional diagnostic tool can be used to accurately distinguish benign from malignant mammographic microcalcifications and may thus be helpful to reduce unnecessary breast biopsies.

To assess the diagnostic performance of digital breast tomosynthesis (DBT), with a wide scan-angle, compared to full-field digital mammography (FFDM), for the detection and characterization of microcalcifications.IRB approval was obtained for this retrospective study. We selected 150 FFDM and DBT (50 benign and 50 malignant histologically verified microcalcifications, 50 cases classified as BI-RADS 1). Four radiologists evaluated, in separate sessions and blinded to patients' history and histology, the presence of microcalcifications. Cases with microcalcifications were assessed for visibility, characteristics, and grade of suspicion using BI-RADS categories. Detection rate and diagnostic performance were calculated. Visibility, lesions' characteristics and reading time were analysed.Detection rate and visibility were good for both FFDM and DBT, without intra-reader differences (P=0.510). Inter-reader differences were detected (P0.018). Only two lesions were not detected by any reader on either FFDM or DBT. Diagnostic performance with DBT was as good as that of FFDM, but a significant inter-reader difference was found (P=0.041). High inter-reader variability in the use of the descriptors was found. Reading time for DBT was almost twice that for FFDM (44 and 25s, respectively).Wide scan-angle DBT enabled the detection and characterization of microcalcifications with no significant differences from FFDM. Inter-reader variability was seen.

Contrast-enhanced MRI (CE-MRI) is the most sensitive technique for breast cancer detection. Contrast-enhanced mammography (CEM) is emerging as a possible alternative to CE-MRI. Purpose To evaluate the diagnostic performance of a low radiation dose contrast-enhanced mammography (L-CEM) in women with suspicious findings on conventional imaging compared to CE-MRI of the breast. Study type Prospective, single center. Population Women with suspicious findings on mammography, tomosynthesis, or ultrasound, and no contraindications for L-CEM or CE-MRI. Eighty women were included. Field strength/sequence 1.5 and 3T CE-MRI, standard protocol for breast, with dedicated coils, according to international guidelines. L-CEM was performed using a dedicated prototype. Assessment Three, off-site, blinded readers evaluated the images according to the BI-RADS lexicon in a randomized order, each in two separate reading sessions. Histology served as a gold standard. Statistical test Lesion detection rate, sensitivity, specificity, and negative and positive predictive values (NPV, PPV) were calculated and compared with multivariate statistics. Results Included were 80 women (mean age, 54.3 years ±11.2 standard deviation) with 93 lesions (32 benign, 61 malignant). The detection rate was significantly higher with CE-MRI (92.5-94.6%; L-CEM 79.6-91.4%, P = 0.014). Sensitivity (L-CEM 65.6-90.2%; CE-MRI 83.6-93.4%, P = 0.086) and NPV (L-CEM 59.6-71.4%; CE-MRI 63.0-76.5%, P = 0.780) did not differ between the modalities. Specificity (L-CEM 46.9-96.9%; CE-MRI 37.5-53.1%, P = 0.001) and PPV (L-CEM 76.4-97.6%; CE-MRI 73.3-77.3%, P = 0.007) were significantly higher with L-CEM. Variations between readers were significant for sensitivity and NPV. The accuracy of L-CEM was as good as CE-MRI (75.3-76.3% vs. 72.0-75.3%, P = 0.514). Data conclusion L-CEM showed a high sensitivity and accuracy in women with suspicious findings on conventional imaging. Compared to CE-MRI, L-CEM has the potential to increase specificity and PPV. L-CEM might help to reduce false-positive biopsies while obtaining sensitivity comparable to that of CE-MRI LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:589-595.

OBJECTIVES: The aim of this study was to evaluate breast multiparametric ultrasound (mpUS) and its potential to reduce unnecessary breast biopsies with 1, 2, or 3 additional quantitative parameters (Doppler, elastography, and contrast-enhanced ultrasound [CEUS]) to B-mode and investigate possible variations with different reader experience. MATERIALS AND METHODS: This prospective study included 124 women (age range, 18-82 years; mean, 52 years), each with 1 new breast lesion, scheduled for ultrasound-guided biopsy between October 2015 and September 2016. Each lesion was examined with B-mode, elastography (Virtual Touch IQ [VTIQ]), Doppler, and CEUS, and different quantitative parameters were recorded for each modality. Four readers (2 experienced breast radiologists and 2 in-training) independently evaluated B-mode images of each lesion and assigned a BI-RADS (Breast Imaging Reporting and Data System) score. Using the area under the receiver operating characteristic curve (AUC), the most accurate quantitative parameter for each modality was chosen. These were then combined with the BI-RADS scores of all readers. Descriptive statistics and AUC were used to evaluate the diagnostic performance of mpUS. RESULTS: Sixty-five lesions were malignant. MpUS with B-mode and 2 additional quantitative parameters (VTIQ and CEUS or Doppler) showed the highest diagnostic performance for all readers (averaged AUCs, 0.812-0.789 respectively vs 0.683 for B-mode, P = 0.0001). Both combinations significantly reduced the number of false-positive findings up to 46.9% (P < 0.0001). CONCLUSIONS: Quantitative mpUS with 2 different triple assessment modalities (B-mode, VTIQ elastography, CEUS, or Doppler) shows the best diagnostic performance for breast cancer diagnosis and leads to a significant reduction of false-positive biopsy recommendations, for both experienced and inexperienced readers.

PURPOSE: Skin-sparing and nipple-sparing mastectomies (SSM; NSM) remove the breast’s fibroglandular tissue (FGT), thereby reducing breast cancer risk. The postoperative presence of residual FGT (RFGT) is associated with remaining cancer risk. This study evaluated the role of MRI in the quantitative assessment of RFGT and its impact on the estimation of the remaining breast cancer risk. METHODS: The postoperative MRI scans (following EUSOMA recommendations) of 58 patients who had undergone SSM or NSM between 2003 and 2013, as well as preoperative MRI scans that were available in 25 of these patients, were retrospectively evaluated for the presence and location of RFGT by three radiologists. Two different observers quantitatively assessed the volume and percentage of retromamillary and other RFGT (RFGT(rm) and RFGT(other)) were assessed. The Fisher’s exact test, the Student’s t-test, and intraclass coherence were used to compare patient groups and to assess reproducibility. RESULTS: RFGT was found in 20% of all breasts and significantly more frequently after NSM than SSM (50% vs. 13%, p = .003). RFGT(rm) and RFGT(other) were more prevalent after NSM (p < 0.001; p = .127). RFGT ranged from 0.5 to 26% of the preoperative FGT, with higher percentages after NSM than SSM (p = .181). CONCLUSIONS: The prevalence and percentage of RFGT found on MRI indicate a considerable remaining postoperative breast cancer risk in some women.

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.

The rationale was to assess whether there are differences in multiparametric (18)F-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 (18)F-FDG PET/MRI of the breast at 3T with dynamic contrast-enhanced MRI, diffusion-weighted imaging, and the radiotracer (18)F-FDG. In all patients, the following imaging biomarkers were recorded for the contralateral (tumor-free) breast: breast parenchymal uptake (BPU) (from (18)F-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 (18)F-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 (18)F-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: Contrast-enhanced magnetic resonance imaging (CE-MRI) of the breast is highly sensitive for breast cancer detection. Multichannel coils and 3T scanners can increase signal, spatial, and temporal resolution. In addition, the T1 -reduction effect of a gadolinium-based contrast agent (GBCA) is higher at 3T. Thus, it might be possible to reduce the dose of GBCA at 3T without losing diagnostic information. PURPOSE: To compare a three-quarter (0.075 mmol/kg) dose of the high-relaxivity GBCA gadobenate dimeglumine, with a 1.5-fold higher than on-label dose (0.15 mmol/kg) of gadoterate meglumine for breast lesion detection and characterization at 3T CE-MRI. STUDY TYPE: Prospective, randomized, intraindividual comparative study. POPULATION: Eligible were patients with imaging abnormalities (BI-RADS 0, 4, 5) on conventional imaging. Each patient underwent two examinations, 24-72 hours apart, one with 0.075 mmol/kg gadobenate and the other with 0.15 mmol/kg gadoterate administered in a randomized order. In all, 109 patients were prospectively recruited. FIELD STRENGTH/SEQUENCE: 3T MRI with a standard breast protocol (dynamic-CE, T2 w-TSE, STIR-T2 w, DWI). ASSESSMENT: Histopathology was the standard of reference. Three blinded, off-site breast radiologists evaluated the examinations using the BI-RADS lexicon. STATISTICAL TESTS: Lesion detection, sensitivity, specificity, and diagnostic accuracy were calculated per-lesion and per-region, and compared by univariate and multivariate analysis (Generalized Estimating Equations, GEE). RESULTS: Five patients were excluded, leaving 104 women with 142 histologically verified breast lesions (109 malignant, 33 benign) available for evaluation. Lesion detection with gadobenate (84.5-88.7%) was not inferior to gadoterate (84.5-90.8%) (P ≥ 0.165). At per-region analysis, gadobenate demonstrated higher specificity (96.4-98.7% vs. 92.6-97.3%, P ≤ 0.007) and accuracy (96.3-97.8% vs. 93.6-96.1%, P ≤ 0.001) compared with gadoterate. Multivariate analysis demonstrated superior, reader-independent diagnostic accuracy with gadobenate (odds ratio = 1.7, P < 0.001 using GEE). DATA CONCLUSION: A 0.075 mmol/kg dose of the high-relaxivity contrast agent gadobenate was not inferior to a 0.15 mmol/kg dose of gadoterate for breast lesion detection. Gadobenate allowed increased specificity and accuracy. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1157-1165.

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-Wilk's 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: We hypothesized that different quantitative ultrasound (US) parameters may be used as complementary diagnostic criteria and aimed to develop a simple classification algorithm to distinguish benign from malignant breast lesions and aid in the decision to perform biopsy or not. PROCEDURES: One hundred twenty-four patients, each with one biopsy-proven, sonographically evident breast lesion, were included in this prospective, IRB-approved study. Each lesion was examined with B-mode US, Color/Power Doppler US and elastography (Acoustic Radiation Force Impulse-ARFI). Different quantitative parameters were recorded for each technique, including pulsatility (PI) and resistive Index (RI) for Doppler US and lesion maximum, intermediate, and minimum shear wave velocity (SWVmax, SWVinterm, and SWVmin) as well as lesion-to-fat SWV ratio for ARFI. Receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of each quantitative parameter. Classification analysis was performed using the exhaustive chi-squared automatic interaction detection method. Results include the probability for malignancy for every descriptor combination in the classification algorithm. RESULTS: Sixty-five lesions were malignant and 59 benign. Out of all quantitative indices, maximum SWV (SWVmax), and RI were included in the classification algorithm, which showed a depth of three ramifications (SWVmax ≤ or > 3.16; if SWVmax ≤ 3.16 then RI ≤ 0.66, 0.66-0.77 or > 0.77; if RI ≤ 0.66 then SWVmax ≤ or > 2.71). The classification algorithm leads to an AUC of 0.887 (95 % CI 0.818-0.937, p < 0.0001), a sensitivity of 98.46 % (95 % CI 91.7-100 %), and a specificity of 61.02 % (95 % CI 47.4-73.5 %). By applying the proposed algorithm, a false-positive biopsy could have been avoided in 61 % of the cases. CONCLUSIONS: A simple classification algorithm incorporating two quantitative US parameters (SWVmax and RI) shows a high diagnostic performance, being able to accurately differentiate benign from malignant breast lesions and lower the number of unnecessary breast biopsies in up to 60 % of all cases, avoiding any subjective interpretation bias.

the Kaiser score is increasingly recognized as a valuable tool to improve breast MRI interpretation. Contrast enhancement kinetics are the second most important diagnostic criterion, thus defining the curve type plays a crucial role in Kaiser score assessment. We investigate whether the timepoint used to determine the initial enhancement (earlyor peak) for the signal-intensity time curve analysis affects the diagnostic performance of the Kaiser score.This IRB-approved, retrospective, single-center study included 70 consecutives histologically verified breast MRI cases. Two off-site breast radiologists independently read all examinations using the Kaiser score, assessing the initial enhancement using three approaches: -first (1 st), second (2nd) and peak (maximum) of either 1 st or 2nd post-contrast timepoints. The initial enhancement was then compared to the last timepoint (delayed enhancement) to determine the curve type. Visual assessment of curve types was used for this study. Diagnostic performance was evaluated by receiver operating characteristics (ROC) analysis.Kaiser score reading results using the peak enhancement of either the first or second timepoint performed significantly better than the other approaches (P 0.05, respectively) and specifically achieved higher sensitivity. Diagnostic accuracy (AUC area under the curve) ranged between 85.4 % and 91.6 %, without significant differences between the two readers (P 0.5).Diagnostic performance of the Kaiser score is significantly influenced by how the initial enhancement timepoint is determined. Peak enhancement should be used as initial timepoint to avoid pitfalls due to timing or physiological differences.

Diffusion-weighted MRI (DWI) provides insights into tissue microstructure by visualization and quantification of water diffusivity. Quantitative evaluation of the apparent diffusion coefficient (ADC) obtained from DWI has been proven helpful for differentiating between malignant and benign breast lesions, for cancer subtyping in breast cancer patients, and for prediction of response to neoadjuvant chemotherapy. However, to further establish DWI of breast lesions it is important to evaluate the quantitative imaging biomarker (QIB) characteristics of reproducibility, repeatability, and diagnostic accuracy. In this intra-individual prospective clinical study 40 consecutive patients with suspicious findings, scheduled for biopsy, underwent an identical 3T breast MRI protocol of the breast on two consecutive days (>24 h). Mean ADC of target lesions was assessed (two independent readers) in four separate sessions. Reproducibility, repeatability, and diagnostic accuracy between examinations (E1, E2), readers (R1, R2), and measurements (M1, M2) were assessed with intraclass correlation coefficients (ICCs), coefficients of variation (CVs), Bland–Altman plots, and receiver operating characteristic (ROC) analysis with calculation of the area under the ROC curve (AUC). The standard of reference was either histopathology (n = 38) or imaging follow-up of up to 24 months (n = 2). Eighty breast MRI examinations (median E1–E2, 2 ± 1.7 days, 95% confidence interval (CI) 1–2 days, range 1–11 days) in 40 patients (mean age 56, standard deviation (SD) ±14) were evaluated. In 55 target lesions (mean size 25.2 ± 20.8 (SD) mm, range 6–106 mm), mean ADC values were significantly (P 0.9, CVs 3.2–8%), indicating little variation. Bland–Altman plots demonstrated no systematic differences, and diagnostic accuracy was not significantly different in the two repeated examinations (all ROC curves >0.91, P > 0.05). High reproducibility, repeatability, and diagnostic accuracy of DWI provide reliable characteristics for its use as a potential QIB, to further improve breast lesion detection, characterization, and treatment monitoring of breast lesions.