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Hintergrund Die MR-Mammografie (MRM) ist als sensitivstes Verfahren zur Detektion von Brustkrebs integraler Bestandteil der modernen Mammadiagnostik. Aufgrund umfangreicher multiparametrischer Bildinformationen gilt die Befundung der MRM jedoch als schwierig. Klinische Entscheidungsregeln kombinieren diagnostische Kriterien in einem Algorithmus. Damit unterstützen sie Radiologen dabei, objektive und exakte sowie weitgehend von der Untersuchererfahrung unabhängige MRM-Diagnosen zu stellen. Methodik Narrativer review. Der Kaiser-Score (KS) als klinische Entscheidungsregel für die MRM wird eingeführt. Befundkriterien werden erläutert, Strategien zur klinischen Entscheidungsfindung diskutiert und illustriert. Ergebnisse Entwickelt mit Methoden des maschinellen Lernens wurde der Kaiser-Score in internationalen Studien unabhängig validiert. Dabei ist der KS unabhängig von der Untersuchungstechnik. Anhand von auf T2w- und kontrastangehobenen T1w-Aufnahmen fassbaren diagnostischen BI-RADS-Kriterien ermöglicht der KS die objektive und genaue Differenzialdiagnose von benignen und malignen Befunden in der MRM. Ein Flowchart leitet den Leser über maximal 3 Zwischenschritte zu einem Punktwert, entsprechend einer Malignomwahrscheinlichkeit. Damit lässt sich der KS direkt einer konkreten BI-RADS-Kategorie zuordnen. Individuelle Managemententscheidungen sollten dabei auch den klinischen Kontext berücksichtigen, was anhand von typischen Beispielen dargestellt wird. Kernaussagen: Zitierweise

Objectives To develop an intuitive and generally applicable system for the reporting, assessment, and documentation of ADC to complement standard BI-RADS criteria. Methods This was a multicentric, retrospective analysis of 11 independently conducted institutional review board–approved studies from seven institutions performed between 2007 and 2019. Breast Apparent Diffusion coefficient (ADC-B) categories comprised ADC-B0 (ADC non-diagnostic), ADC-B1 (no enhancing lesion), and ADC-B2-5. The latter was defined by plotting ADC versus cumulative malignancy rates. Statistics comprised ANOVA with post hoc testing and ROC analysis. p values ≤ 0.05 were considered statistically significant. Results A total of 1625 patients (age: 55.9 years (± 13.8)) with 1736 pathologically verified breast lesions were included. The mean ADC (× 10−3 mm2/s) differed significantly between benign (1.45, SD .40) and malignant lesions (.95, SD .39), and between invasive (.92, SD .22) and in situ carcinomas (1.18, SD .30) (p 24.5%). At the latter threshold, a positive predictive value of 95.8% (95% CI 0.94–0.97) for invasive versus non-invasive breast carcinomas was reached. Conclusions The breast apparent diffusion coefficient system (ADC-B) provides a simple and widely applicable categorization scheme for assessment, documentation, and reporting of apparent diffusion coefficient values in contrast-enhancing breast lesions on MRI. Clinical relevance statement The ADC-B system, based on diverse MRI examinations, is clinically relevant for stratifying breast cancer risk via apparent diffusion coefficient measurements, and complements BI-RADS for improved clinical decision-making and patient outcomes. Key Points • The breast apparent diffusion coefficient category system (ADC-B) is a simple tool for the assessment, documentation, and reporting of ADC values in contrast-enhancing breast lesions on MRI. • The categories comprise ADC-B0 for non-diagnostic examinations, ADC-B1 for examinations without an enhancing lesion, and ADC-B2-5 for enhancing lesions with an increasing malignancy rate. • The breast apparent diffusion coefficient category system may be used to complement BI-RADS in clinical decision-making.

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.

Breast MRI is the most sensitive method for the detection of breast cancer and is an integral part of modern breast imaging. On the other hand, interpretation of breast MRI exams is considered challenging due to the complexity of the available information. Clinical decision rules that combine diagnostic criteria in an algorithm can help the radiologist to read breast MRI by supporting objective and largely experience-independent diagnosis.Narrative review. In this article, the Kaiser Score (KS) as a clinical decision rule for breast MRI is introduced, its diagnostic criteria are defined, and strategies for clinical decision making using the KS are explained and discussed.The KS is based on machine learning and has been independently validated by international research. It is largely independent of the examination technique that is used. It allows objective differentiation between benign and malignant contrast-enhancing breast MRI findings using diagnostic BI-RADS criteria taken from T2w and dynamic contrast-enhanced T1w images. A flowchart guides the reader in up to three steps to determine a score corresponding to the probability of malignancy that can be used to assign a BI-RADS category. Individual decision making takes the clinical context into account and is illustrated by typical scenarios.· The KS as an evidence-based decision rule to objectively distinguish benign from malignant breast lesions is based on information contained in T2w und dynamic contrast-enhanced T1w sequences and is largely independent of specific examination protocols.. · The KS diagnostic criteria are in line with the MRI BI-RADS lexicon. We focused on defining a default category to be applied in the case of equivocal imaging criteria.. · The KS reflects increasing probabilities of malignancy and, together with the clinical context, assists individual decision making..· Baltzer PA, Krug KB, Dietzel M. Evidence-Based and Structured Diagnosis in Breast MRI using the Kaiser Score. Fortschr Röntgenstr 2022; 194: 1216 - 1228. Die MR-Mammografie (MRM) ist als sensitivstes Verfahren zur Detektion von Brustkrebs integraler Bestandteil der modernen Mammadiagnostik. Aufgrund umfangreicher multiparametrischer Bildinformationen gilt die Befundung der MRM jedoch als schwierig. Klinische Entscheidungsregeln kombinieren diagnostische Kriterien in einem Algorithmus. Damit unterstützen sie Radiologen dabei, objektive und exakte sowie weitgehend von der Untersuchererfahrung unabhängige MRM-Diagnosen zu stellen. Narrativer review. Der Kaiser-Score (KS) als klinische Entscheidungsregel für die MRM wird eingeführt. Befundkriterien werden erläutert, Strategien zur klinischen Entscheidungsfindung diskutiert und illustriert. Entwickelt mit Methoden des maschinellen Lernens wurde der Kaiser-Score in internationalen Studien unabhängig validiert. Dabei ist der KS unabhängig von der Untersuchungstechnik. Anhand von auf T2w- und kontrastangehobenen T1w-Aufnahmen fassbaren diagnostischen BI-RADS-Kriterien ermöglicht der KS die objektive und genaue Differenzialdiagnose von benignen und malignen Befunden in der MRM. Ein Flowchart leitet den Leser über maximal 3 Zwischenschritte zu einem Punktwert, entsprechend einer Malignomwahrscheinlichkeit. Damit lässt sich der KS direkt einer konkreten BI-RADS-Kategorie zuordnen. Individuelle Managemententscheidungen sollten dabei auch den klinischen Kontext berücksichtigen, was anhand von typischen Beispielen dargestellt wird. · Der evidenzbasierte KS zur objektiven Differenzierung von gutartigen und bösartigen Brustläsionen basiert auf T2w- und dynamisch kontrastangehobenen T1w-Informationen und ist weitgehend unabhängig von der verwendeten Untersuchungstechnik.. · Der KS basiert auf den etablierten diagnostischen Kriterien des MRT BI-RADS-Lexikons. Wir setzen einen Fokus auf die Definition einer Basiskategorie, welche bei Unklarheiten greift.. · Der KS reflektiert steigende Malignomwahrscheinlichkeiten und kann in Zusammenschau mit dem klinischen Kontext als Entscheidungshilfe das Patientenmanagement unterstützen..· Baltzer PA, Krug KB, Dietzel M. Evidence-Based and Structured Diagnosis in Breast MRI using the Kaiser Score. Fortschr Röntgenstr 2022; 194: 1216 – 1228.

Purpose:Diffusion-weighted imaging with the calculation of an apparent diffusion coefficient (ADC) has been proposed as a quantitative biomarker on contrast-enhanced MRI (CE-MRI) of the breast. There is a need to approve a generalizable ADC cutoff. The purpose of this study was to evaluate whether a predefined ADC cutoff allows downgrading of BI-RADS 4 lesions on CE-MRI, avoiding unnecessary biopsies.Experimental Design:This was a retrospective, multicentric, cross-sectional study. Data from five centers were pooled on the individual lesion level. Eligible patients had a BI-RADS 4 rating on CE-MRI. For each center, two breast radiologists evaluated the images. Data on lesion morphology (mass, non-mass), size, and ADC were collected. Histology was the standard of reference. A previously suggested ADC cutoff (≥1.5 × 10−3 mm2/second) was applied. A negative likelihood ratio of 0.1 or lower was considered as a rule-out criterion for breast cancer. Diagnostic performance indices were calculated by ROC analysis.Results:There were 657 female patients (mean age, 42; SD, 14.1) with 696 BI-RADS 4 lesions included. Disease prevalence was 59.5% (414/696). The area under the ROC curve was 0.784. Applying the investigated ADC cutoff, sensitivity was 96.6% (400/414). The potential reduction of unnecessary biopsies was 32.6% (92/282).Conclusions:An ADC cutoff of ≥1.5 × 10−3 mm2/second allows downgrading of lesions classified as BI-RADS 4 on breast CE-MRI. One-third of unnecessary biopsies could thus be avoided.

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ZusammenfassungDie MRT der Brust gilt als das sensitivste Verfahren zum Nachweis von Brustkrebs. Bleiben die konventionellen Befunde unklar, ist die MRT ein hochspezifisches diagnostisches Verfahren, mit dem sich Biopsien vermeiden lassen. Aktueller Standard ist dabei die multiparametrische MRM, die Kombination aus T2w, diffusionsgewichteten und kontrastangehobenen T1w Aufnahmen.

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

Dedicated breast computed tomography (BCT) is an emerging breast imaging modality. The latest development has been the introduction of a spiral breast computed tomography scanner equipped with a photon-counting detector (SBCT). SBCT promises multiple advantages: Unlike conventional mammography, contrast enhanced spectral mammography (CESM: both 2D), and digital breast tomosynthesis (DBT: pseudo 3D), SBCT enables 3D breast imaging without tissue overlap. SBCT achieves high isotropic spatial resolution of breast tissue enabling the assessment of both soft tissue and microcalcifications. Similar to CESM and MRI, SBCT supports contrast-enhanced imaging, enabling the assessment of breast neovascularization. Unlike mammography and its derived methods (CESM, DBT), SBCT does not require compression of the breast. Accordingly, women consistently report significantly increased patient comfort compared to mammography in a previous investigation. Radiation safety is crucial in breast imaging. Studies showed different results in terms of dose, with some staying within the limits of two-view FFDM defined by the ACR and others exceeding the limit by up to 21%. Therefore, a higher radiation dose compared to state-of-the-art mammography and DBT systems has to be acknowledged. SBCT is currently under scientific investigation in multiple trials. Three major indications are currently explored: Whereas our colleagues in Zurich/Switzerland investigate the role of SBCT for opportunistic screening, in our department SBCT is mainly indicated for the work-up of equivocal lesions, and for preoperative staging. In this narrative review, we summarize the concepts of SBCT and potential implications for patient care. We report on our initial clinical experience with the technology and outline future developments of SBCT.

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.

This study aimed to develop a tool for the classification of masses in breast MRI, based on ultrafast TWIST-VIBE Dixon (TVD) dynamic sequences combined with DWI. TVD sequences allow to abbreviate breast MRI protocols, but provide kinetic information only on the contrast wash-in, and because of the lack of the wash-out kinetics, their diagnostic value might be hampered. A special focus of this study was thus to maintain high diagnostic accuracy in lesion classification. Sixty-one patients who received breast MRI between 02/2014 and 04/2015 were included, with 83 reported lesions (60 malignant). Our institute’s standard breast MRI protocol was complemented by an ultrafast TVD sequence. ADC and peak enhancement of the TVD sequences were integrated into a generalised linear model (GLM) for malignancy prediction. For comparison, a second GLM was calculated using ADC and conventional DCE curve type. The resulting GLMs were evaluated for standard diagnostic parameters. For easy application of the GLMs, nomograms were created. The GLM based on peak enhancement of the TVD and ADC was as equally accurate as the GLM based on conventional DCE and ADC, with no significant differences (sensitivity, 93.3%/93.3%; specificity, 91.3%/87.0%; PPV, 96.6%/94.9%; NPV, 84.0%/83.3%; all, p ≥ 0.315). This study presents a method to integrate ultrafast TVD sequences into a breast MRI protocol, allowing a reduction of the examination time while maintaining diagnostic accuracy. A GLM based on the combination of TVD-derived peak enhancement and ADC provides high diagnostic accuracy, and can be easily applied using a nomogram. • Ultrafast TWIST-VIBE Dixon sequence protocols in combination with diffusion-weighted imaging allow to shorten breast MRI examinations, while diagnostic accuracy is maintained. • Integrating peak enhancement from the TWIST-VIBE Dixon sequence and the apparent diffusion coefficient into a generalised linear model provides a comprehensible image evaluation approach. • This approach is further facilitated by nomograms.

Magnetic resonance imaging (MRI) is an important part of breast cancer diagnosis and multimodal workup. It provides unsurpassed soft tissue contrast to analyse the underlying pathophysiology, and it is adopted for a variety of clinical indications. Predictive and prognostic breast MRI (P2-bMRI) is an emerging application next to these indications. The general objective of P2-bMRI is to provide predictive and/or prognostic biomarkers in order to support personalisation of breast cancer treatment. We believe P2-bMRI has a great clinical potential, thanks to the in vivo examination of the whole tumour and of the surrounding tissue, establishing a link between pathophysiology and response to therapy (prediction) as well as patient outcome (prognostication). The tools used for P2-bMRI cover a wide spectrum: standard and advanced multiparametric pulse sequences; structured reporting criteria (for instance BI-RADS descriptors); artificial intelligence methods, including machine learning (with emphasis on radiomics data analysis); and deep learning that have shown compelling potential for this purpose. P2-bMRI reuses the imaging data of examinations performed in the current practice. Accordingly, P2-bMRI could optimise clinical workflow, enabling cost savings and ultimately improving personalisation of treatment. This review introduces the concept of P2-bMRI, focusing on the clinical application of P2-bMRI by using semantic criteria.

ObjectivesTo evaluate the cost-effectiveness of MR-mammography (MRM) vs. x-ray based mammography (XM) in two-yearly screening women of intermediate risk for breast cancer in the light of recent literature.MethodsDecision analysis and Markov modelling were used to compare cumulative costs (in US-$) and outcomes (in QALYs) of MRM vs. XM over the model runtime of 20 years. The perspective of the U.S. healthcare system was selected. Incremental cost-effectiveness ratios (ICER) were calculated and related to a willingness to pay-threshold of $ 100,000 per QALY in order to evaluate the cost-effectiveness. Deterministic and probabilistic sensitivity analyses were conducted to test the impact of variations of the input parameters. In particular, variations of the rate of false positive findings beyond the first screening round and their impact on cost-effectiveness were assessed.ResultsBreast cancer screening with MRM resulted in increased costs and superior effectiveness. Cumulative average costs of $ 6,081 per woman and cumulative effects of 15.12 QALYs were determined for MRM, whereas screening with XM resulted in costs of $ 5,810 and 15.10 QALYs, resulting in an ICER of $ 13,493 per QALY gained. When the specificity of MRM in the second and subsequent screening rounds was varied from 92% to 99%, the ICER resulted in a range from $ 38,849 to $ 5,062 per QALY.ConclusionsBased on most recent data on the diagnostic performance beyond the first screening round, MRM may remain the economically preferable alternative in screening women of intermediate risk for breast cancer due to their dense breast tissue.

Objectives Abbreviated breast MRI (AB-MRI) was introduced to reduce both examination and image reading times and to improve cost-effectiveness of breast cancer screening. The aim of this model-based economic study was to analyze the cost-effectiveness of full protocol breast MRI (FB-MRI) vs. AB-MRI in screening women with dense breast tissue for breast cancer. Methods Decision analysis and a Markov model were designed to model the cumulative costs and effects of biennial screening in terms of quality-adjusted life years (QALYs) from a US healthcare system perspective. Model input parameters for a cohort of women with dense breast tissue were adopted from recent literature. The impact of varying AB-MRI costs per examination as well as specificity on the resulting cost-effectiveness was modeled within deterministic sensitivity analyses. Results At an assumed cost per examination of $ 263 for AB-MRI (84% of the cost of a FB-MRI examination), the discounted cumulative costs of both MR-based strategies accounted comparably. Reducing the costs of AB-MRI below $ 259 (82% of the cost of a FB-MRI examination, respectively), the incremental cost-effectiveness ratio of FB-MRI exceeded the willingness to pay threshold and the AB-MRI-strategy should be considered preferable in terms of cost-effectiveness. Conclusions Our preliminary findings indicate that AB-MRI may be considered cost-effective compared to FB-MRI for screening women with dense breast tissue for breast cancer, as long as the costs per examination do not exceed 82% of the cost of a FB-MRI examination. Key Points • Cost-effectiveness of abbreviated breast MRI is affected by reductions in specificity and resulting false positive findings and increased recall rates. • Abbreviated breast MRI may be cost-effective up to a cost per examination of 82% of the cost of a full protocol examination. • Abbreviated breast MRI could be an economically preferable alternative to full protocol breast MRI in screening women with dense breast tissue.

Dynamic contrast-enhanced imaging of the initial (IP) and delayed phase (DP) is an integral part of any clinical breast MRI protocol. Furthermore, DWI is increasingly used as an add-on sequence by the breast-imaging community. We investigated whether DWI could be used as a substitute DP. One hundred thirty-two consecutive patients with equivocal or suspicious findings at ultrasound and/or mammography received a full diagnostic breast MRI according to international recommendations. Histopathological verification served as reference standard. We evaluated three sections of the MRI protocol: IP, DP, and apparent diffusion coefficient (ADC) maps derived from DWI. Circular ROIs (regions of interest, mean size 5–10 mm2) were drawn into the enhancing parts of the lesion (first postcontrast). ROIs were transferred to the corresponding location on ADC maps and IP and DP images. Mean ROI values were investigated signal intensity (SI): (1) Initial-phase enhancement = (SI(IP) − SI(precontrast))/SI(precontrast); (2) Delayed-phase enhancement = (SI(DP) − SI(IP))/SI(IP); (3) ADC. Multiparametric combinations were computed using logistic regression analysis: (1) IP+: Initial-phase enhancement and ADC; (2) Curve: Initial-phase enhancement and delayed-phase enhancement; (3) Curve+: Curve and ADC. The diagnostic performances of these feature combinations to diagnose malignancy were compared by the area under the receiver-operating characteristics curve (AUC). One hundred thirty-two patients (age: mean = 57.1 years, range 23–83 years) with 145 lesions were included (malignant/benign 101/44). IP+ (AUC = 0.877) outperformed Curve (AUC = 0.788, p = 0.03). Curve+ was not superior to IP+ (p = 1). DWI could substitute DP. Because DWI is typically used as an add-on to IP and DP, our results might help to abbreviate and to simplify current practice of breast MRI. • DWI provides similar but superior diagnostic information for diagnosis of malignancy in enhancing breast lesions compared to DP. • Adding DP to DWI does not provide incremental information to distinguish benign from malignant lesions. • DWI could substitute DP. As DWI is typically used as an add-on to IP and DP, our findings might help to abbreviate and to simplify current breast MRI practice.

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..

Purpose: Diffusion-weighted imaging with the calculation of an apparent diffusion coefficient (ADC) has been proposed as a quantitative biomarker on contrast-enhanced MRI (CE-MRI) of the breast. There is a need to approve a generalizable ADC cutoff. The purpose of this study was to evaluate whether a predefined ADC cutoff allows downgrading of BI-RADS 4 lesions on CE-MRI, avoiding unnecessary biopsies. Experimental Design: This was a retrospective, multicentric, cross-sectional study. Data from five centers were pooled on the individual lesion level. Eligible patients had a BI-RADS 4 rating on CE-MRI. For each center, two breast radiologists evaluated the images. Data on lesion morphology (mass, non-mass), size, and ADC were collected. Histology was the standard of reference. A previously suggested ADC cutoff (≥1.5 × 10−3 mm2/second) was applied. A negative likelihood ratio of 0.1 or lower was considered as a rule-out criterion for breast cancer. Diagnostic performance indices were calculated by ROC analysis. Results: There were 657 female patients (mean age, 42; SD, 14.1) with 696 BI-RADS 4 lesions included. Disease prevalence was 59.5% (414/696). The area under the ROC curve was 0.784. Applying the investigated ADC cutoff, sensitivity was 96.6% (400/414). The potential reduction of unnecessary biopsies was 32.6% (92/282). Conclusions: An ADC cutoff of ≥1.5 × 10−3 mm2/second allows downgrading of lesions classified as BI-RADS 4 on breast CE-MRI. One-third of unnecessary biopsies could thus be avoided.

Simple Summary Women with dense breasts have an increased risk of breast cancer and a smaller likelihood of detection in conventional mammographic screening. It is an ongoing challenge of breast imaging techniques to increase sensitivity in dense breasts. Tomosynthesis, as well as abbreviated breast MR, intends to close the gap between optimal cancer detection rate and cost-effectiveness. The aim of this economic evaluation was to analyse the cost-effectiveness of these two imaging techniques in screening women of intermediate risk of breast cancer due to dense breasts. The model-based analysis revealed that abbreviated breast MRI can be considered cost-effective, being below the willingness to pay-threshold of $100,000 per quality-adjusted life-year gained. Abstract Background: Digital breast tomosynthesis (DBT) and abbreviated breast MRI (AB-MRI) offer superior diagnostic performance compared to conventional mammography in screening women with intermediate risk of breast cancer due to dense breast tissue. The aim of this model-based economic evaluation was to analyze whether AB-MRI is cost-effective in this cohort compared to DBT. Methods: Decision analysis and Markov simulations were used to model the cumulative costs and quality-adjusted life-years (QALYs) over a time horizon of 30 years. Model input parameters were adopted from recent literature. Deterministic and probabilistic sensitivity analyses were applied to test the stability of the model. Results: In the base-case scenario, the costs of an AB-MRI examination were defined to equal the costs of a full protocol acquisition. Two-yearly screening of women with dense breasts resulted in cumulative discounted costs of $8798 and $9505 for DBT and AB-MRI, and cumulative discounted effects of 19.23 and 19.27 QALYs, respectively, with an incremental cost-effectiveness ratio of $20,807 per QALY gained in the base-case scenario. By reducing the cost of an AB-MRI examination below a threshold of $241 in sensitivity analyses, AB-MRI would become cost-saving compared to DBT. Conclusion: In comparison to DBT, AB-MRI can be considered cost-effective up to a price per examination of $593 in screening patients at intermediate risk of breast cancer.

In this multicentric study, individual patient data from 3 different centers were analyzed. Consecutive patients receiving standardized multiparametric breast magnetic resonance imaging for standard nonscreening indications were included. At each center, 2 experienced radiologists with more than 5 years of experience retrospectively interpreted the examinations in consensus and applied the KS to every histologically verified lesion. The corresponding mean ADC of each lesion was measured using a Wielema type 4 region of interest. According to established methods, the KS and ADC were combined, yielding the KS+ score. Diagnostic accuracy was evaluated by the area under the receiver operating characteristics curve (AUROC) and compared between the KS, ADC, and KS+ (DeLong test). Likewise, the potential to help avoid unnecessary biopsies was compared between the KS, ADC, and KS+ based on established high sensitivity thresholds (McNemar test).A total of 450 lesions in 414 patients (mean age, 51.5 years; interquartile range, 42-60.8 years) were included, with 219 lesions being malignant (48.7%; 95% confidence interval [CI], 44%-53.4%). The performance of the KS (AUROC, 0.915; CI, 0.886-0.939) was significantly better than that of the ADC (AUROC, 0.848; CI, 0.811-0.880; P0.001). The largest difference between these parameters was observed when assessing subcentimeter lesions (AUROC, 0.909 for KS; CI, 0.849-0.950 vs 0.811 for ADC; CI, 0.737-0.871; P = 0.02).The use of the KS+ (AUROC, 0.918; CI, 0.889-0.942) improved the performance slightly, but without any significant difference relative to a single KS or ADC reading (P = 0.64).When applying high sensitivity thresholds for avoiding unnecessary biopsies, the KS and ADC achieved equal sensitivity (97.7% for both; cutoff values,4 for KS and ≤1.4 × 10-3 mm2/s for ADC). However, the rate of potentially avoidable biopsies was higher when using the KS (specificity: 65.4% for KS vs 32.9% for ADC; P0.0001). The KS was superior to the KS+ in avoiding unnecessary biopsies.Both the KS and ADC may be used to distinguish benign from malignant breast lesions. However, KS proved superior in this task including, most of all, when assessing small lesions less than 1 cm. Using the KS may avoid twice as many unnecessary biopsies, and the combination of both the KS and ADS does not improve diagnostic performance.

Purpose Aim of this study was to analyze the comparative cost-effectiveness of MR-mammography vs conventional imaging in a screening setting for women with high risk of breast cancer, with particular focus on the impact of specificity of MRM. Method Decision analytic modelling and Markov Modelling were applied to evaluate cumulative costs of each screening modality and their subsequent treatments as well as cumulative outcomes in quality adjusted life years (QALYs). For the selected time horizon of 30 years, false positive and false negative results were included. Model input parameters for women with high risk of breast cancer were estimated based on published data from a US healthcare system perspective. Major influence factors were identified and evaluated in a deterministic sensitivity analysis. Based on current recommendations for economic evaluations, a probabilistic sensitivity analysis was conducted to test the model stability. Results In a base-case analysis, screening with XM vs. MRM and treatment resulted in overall costs of $36,201.57 vs. $39,050.97 and a cumulative effectiveness of 19.53 QALYs vs. 19.59 QALYs. This led to an incremental cost-effectiveness ratio (ICER) of $ 45,373.94 per QALY for MRM. US and XM + US resulted in ICER values higher than the willingness to pay (WTP). In the sensitivity analyses, MRM remained a cost-effective strategy for screening high-risk patients as long as the specificity of MRM did not drop below 86.7 %. Conclusion In high-risk breast cancer patients, MRM can be regarded as a cost-effective alternative to XM in a yearly screening setting. Specificity may be an important cost driver in settings with yearly screening intervals.

Purpose The aim of this study was to analyze the cost-effectiveness of screening patients of intermediate risk of breast cancer with MR-Mammography (MRM) versus conventional mammography (XM). Method A decision model for both diagnostic modalities and a subsequent markov model for the simulation of follow-up costs and outcomes was developed. Input parameters were acquired from published literature for this markov modelling study. The expected cumulative costs and outcomes were calculated for both modalities in a 30-year timeframe in US-dollar ($) and quality-adjusted life years (QALYs). A deterministic sensitivity analysis and a probabilistic sensitivity analysis incorporating 30,000 Monte Carlo iterations were performed to investigate the model stability. Results In total, XM with its consecutive treatments resulted in total costs of $ 5,492.68 and an average cumulative quality of life of 18.87 QALYs, compared to MRM with costs of $ 5,878.66 and 18.92 QALYs. The corresponding incremental cost-effectiveness ratio (ICER) for MRM was $ 8,797.60 per QALY - distinctly below international willingness-to-pay thresholds for cost-effectiveness. The results were confirmed within the limits of the sensitivity analyses. Conclusions In patients with intermediate risk for breast cancer due to their dense breast tissue, two-yearly screening with MRM may be considered as cost-effective.

Key Points• Although radiomics is potentially a promising approach to analyze medical image data, many pitfalls need to be considered to avoid a reproducibility crisis.• There is a translation gap in radiomics research, with many studies being published but so far little to no translation into clinical practice.• Going forward, more studies with higher levels of evidence are needed, ideally also focusing on prospective studies with relevant clinical impact.

To investigate whether automated volumetric radiomic analysis of breast cancer vascularization (VAV) can improve survival prediction in primary breast cancer. 314 consecutive patients with primary invasive breast cancer received standard clinical MRI before the initiation of treatment according to international recommendations. Diagnostic work-up, treatment, and follow-up was done at one tertiary care, academic breast-center (outcome: disease specific survival/DSS vs. disease specific death/DSD). The Nottingham Prognostic Index (NPI) was used as the reference method with which to predict survival of breast cancer. Based on the MRI scans, VAV was accomplished by commercially available, FDA-cleared software. DSD served as endpoint. Integration of VAV into the NPI gave NPIVAV. Prediction of DSD by NPIVAV compared to standard NPI alone was investigated (Cox regression, likelihood-test, predictive accuracy: Harrell’s C, Kaplan Meier statistics and corresponding hazard ratios/HR, confidence intervals/CI). DSD occurred in 35 and DSS in 279 patients. Prognostication of the survival outcome by NPI (Harrell’s C = 75.3%) was enhanced by VAV (NPIVAV: Harrell’s C = 81.0%). Most of all, the NPIVAV identified patients with unfavourable outcome more reliably than NPI alone (hazard ratio/HR = 4.5; confidence interval/CI = 2.14-9.58; P = 0.0001). Automated volumetric radiomic analysis of breast cancer vascularization improved survival prediction in primary breast cancer. Most of all, it optimized the identification of patients at higher risk of an unfavorable outcome. Future studies should integrate MRI as a “gate keeper” in the management of breast cancer patients. Such a “gate keeper” could assist in selecting patients benefitting from more advanced diagnostic procedures (genetic profiling etc.) in order to decide whether are a more aggressive therapy (chemotherapy) is warranted.

Background Motion artifacts can reduce image quality of breast magnetic resonance imaging (MRI). There is a lack of data regarding their effect on diagnostic estimates. Purpose To evaluate factors that potentially influence readers’ diagnostic estimates in breast MRI: motion artifacts; amount of fibroglandular tissue; background parenchymal enhancement; lesion size; and lesion type. Material and Methods This Institutional Review Board-approved, retrospective, cross-sectional, single-center study included 320 patients (mean age = 55.1 years) with 334 histologically verified breast lesions (139 benign, 195 malignant) who underwent breast MRI. Two expert breast radiologists evaluated the images considering: motion artifacts (1 = minimal to 4 = marked); fibroglandular tissue (BI-RADS FGT); background parenchymal enhancement (BI-RADS BPE); lesion size; lesion type; and BI-RADS score. Univariate (Chi-square) and multivariate (Generalized Estimation Equations [GEE]) statistics were used to identify factors influencing sensitivity, specificity, and accuracy. Results Lesions were: 230 mass (68.9%) and 59 non-mass (17.7%), no foci. Forty-five lesions (13.5%) did not enhance in MRI but were suspicious or unclear in conventional imaging. Sensitivity, specificity, and accuracy were 93.8%, 83.4%, and 89.8% for Reader 1 and 95.4%, 87.8%, and 91.9% for Reader 2. Lower sensitivity was observed in case of increased motion artifacts ( P = 0.007), non-mass lesions ( P Conclusion Motion artifacts can impair lesion characterization with breast MRI, but lesion type and small size have the strongest influence on diagnostic estimates.

PURPOSE To intra-individually compare patient comfort of spiral breast computed tomography (SBCT) versus digital mammography (DM). METHOD This prospective IRB approved study included 79 patients undergoing both SBCT and DM for the assessment of BI-RADS 4 - 6 lesions. Following SBCT and DM patients answered a standardized questionnaire regarding "Overall patient comfort" and "Pain" on a 5-point Likert Scale. On the same Likert Scale, experienced radiologic technicians rated the workflow of the SBCT regarding patients' "Mobility", ease of patient "Positioning", patients' adherence to the examination ("Compliance") and expected image quality. Visibility of fibroglandular tissue in SBCT was independently rated by two breast radiologists on a 10-point Likert Scale. Subgroups stratified by menopausal status and body mass index (BMI) were analyzed. RESULTS Patients reported significantly lower pain during SBCT (4.73 ± 0.57) compared to DM (4.09 ± 0.90; P