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PURPOSE: To develop a novel magnetic resonance imaging (MRI) approach for the noninvasive assessment of hypoxia and neovascularization in breast tumors. PROCEDURES: In this IRB-approved prospective study, 20 patients with suspicious breast lesions (BI-RADS 4/5) underwent multiparametric breast MRI including quantitative BOLD (qBOLD) and vascular architecture mapping (VAM). Custom-made in-house MatLab software was used for qBOLD and VAM data postprocessing and calculation of quantitative MRI biomarker maps of oxygen extraction fraction (OEF), metabolic rate of oxygen (MRO(2)), mitochondrial oxygen tension (mitoPO(2)) to measure tissue hypoxia and neovascularization including vascular architecture including microvessel radius (VSI), density (MVD), and type (MTI). Histopathology was used as standard of reference. Appropriate statistics were performed to assess and compare correlations between MRI biomarkers for hypoxia and neovascularization. RESULTS: qBOLD and VAM data with good quality were obtained from all patients with 13 invasive ductal carcinoma (IDC) and 7 benign breast tumors with a lesion diameter of at least 10 mm in all spatial directions. MRI biomarker maps of oxygen metabolism and neovascularization demonstrated intratumoral spatial heterogeneity with a broad range of biomarker values. Bulk tumor neovasculature consisted of draining venous microvasculature with slow flowing blood. High OEF and low mitoPO(2) was associated with low MVD and vice-versa. The heterogeneous pattern of MRO(2) values showed spatial congruence with VSI. IDCs showed significantly higher MRO(2) (P = 0.007), lower mitoPO(2) (P = 0.021), higher MVD (P = 0.005), and lower (i.e., more pathologic) MTI (P = 0.001) compared with benign breast tumors. These results indicate that IDCs consume more oxygen and are more hypoxic and neovascularized than benign tumors. CONCLUSIONS: We developed a novel MRI approach for the noninvasive assessment of hypoxia and neovascularization in benign and malignant breast tumors that can be easily integrated in a diagnostic MRI protocol and provides insight into intratumoral heterogeneity.

This paper summarises the basic information to be offered to women who undergo mammography. After a delineation of the general aim of early diagnosis of breast cancer, the main difference between screening mammography and diagnostic mammography is explained. The best time for scheduling mammography in fertile women is defined. The need to bring images and reports from the previous mammogram (and from other recent breast imaging examinations) is highlighted. The technique and procedure of mammography are briefly described with particular attention to discomfort and pain experienced by a fraction of women who undergo the test. Information is given on the recall during a screening program and on the request for further work-up after a diagnostic mammography. The logic of the diagnostic mammography report and of classification systems such as BI-RADS and R1-R5 is illustrated, and brief but clear information is given about the diagnostic performance of the test, with particular reference to interval cancers. Moreover, the breast cancer risk due to radiation exposure from mammography is compared to the reduction in mortality obtained with the test, and the concept of overdiagnosis is presented. Finally, five frequently asked questions are answered.

The purpose of this study was to compare three different biopsy devices on false-negative and underestimation rates in MR-guided, vacuum-assisted breast biopsy (VABB) of MRI-only lesions. This retrospective, single-center study was IRB-approved. Informed consent was waived. 467 consecutive patients underwent 487 MR-guided VABB using three different 8-10-gauge-VABB devices (Atec-9-gauge,A; Mammotome-8-gauge,M; Vacora-10-gauge,V). VABB data (lesion-type, size, biopsy device, histopathology) were compared to final diagnosis (surgery, n = 210 and follow-up, n = 277). Chi-square, and Kruskal–Wallis tests were applied. P values

The aim of this study was to assess whether sequential multiparametric 18[F]fluoro-desoxy-glucose (18[F]FDG)/[18F]fluoromisonidazole ([18F]FMISO) PET-MRI in breast cancer patients is possible, facilitates information on tumor heterogeneity, and correlates with prognostic indicators. In this pilot study, IRB-approved, prospective study, nine patients with ten suspicious breast lesions (BIRADS 5) and subsequent breast cancer diagnosis underwent sequential combined [18F]FDG/[18F]FMISO PET-MRI. [18F]FDG was used to assess increased glycolysis, while [18F]FMISO was used to detect tumor hypoxia. MRI protocol included dynamic breast contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI). Qualitative and quantitative multiparametric imaging findings were compared with pathological features (grading, proliferation, and receptor status) and clinical endpoints (recurrence/metastases and disease-specific death) using multiple correlation analysis. Histopathology was the standard of reference. There were several intermediate to strong correlations identified between quantitative bioimaging markers, histopathologic tumor characteristics, and clinical endpoints. Based on correlation analysis, multiparametric criteria provided independent information. The prognostic indicators proliferation rate, death, and presence/development of recurrence/metastasis correlated positively, whereas the prognostic indicator estrogen receptor status correlated negatively with PET parameters. The strongest correlations were found between disease-specific death and [18F]FDGmean (R=0.83, p<0.01) and between the presence/development of metastasis and [18F]FDGmax (R=0.79, p<0.01), respectively. This pilot study indicates that multiparametric [18F]FDG/[18F]FMISO PET-MRI might provide complementary quantitative prognostic information on breast tumors including clinical endpoints and thus might be used to tailor treatment for precision medicine in breast cancer.

Cachexia represents a leading cause of morbidity and mortality in various cancers, chronic inflammation and infections. Understanding of the mechanisms that drive cachexia has remained limited, especially for infection-associated cachexia (IAC). In the present paper we describe a model of reversible cachexia in mice with chronic viral infection and identify an essential role for CD8+ T cells in IAC. Cytokines linked to cancer-associated cachexia did not contribute to IAC. Instead, virus-specific CD8+ T cells caused morphologic and molecular changes in the adipose tissue, which led to depletion of lipid stores. These changes occurred at a time point that preceded the peak of the CD8+ T cell response and required T cell-intrinsic type I interferon signaling and antigen-specific priming. Our results link systemic antiviral immune responses to adipose-tissue remodeling and reveal an underappreciated role of CD8+ T cells in IAC.

Die Magnetresonanztomographie (MRT) der Brust ist ein etabliertes nichtinvasives bildgebendes Verfahren mit vielfaltigen Indikationen. In den letzten Jahren wurden zahlreiche funktionelle MRT- und Positronenemissionstomographie(PET)-Parameter in der Brustbildgebung evaluiert, und ihre kombinierte Anwendung ist als multiparametrische Bildgebung definiert. Bisherige Daten legen nahe, dass die multiparametrische Bildgebung mit MRT und PET wertvolle Information uber die Schlusselprozesse der Krebsentstehung und Progression liefern und sowohl Diagnostik als auch Therapieplanung von Brustkrebs verbessern kann. Die multiparametrische und molekulare Bildgebung der Brust umfasst derzeit die kontrastmittelverstarkte MRT (KM-MRT), diffusionsgewichtete Bildgebung (DWI) und Protonenspektroskopie (1H-MRSI) sowie radiologisch-nuklearmedizinisch kombinierte Verfahren (PET-CT, MP-PET-MRT) unter Verwendung des Radiotracers Fluordesoxyglukose (FDG). Die multiparametrischen Bildgebung der Brust kann bei unterschiedlichen Feldstarken durchgefuhrt werden (1,5–7 T). Die multiparametrische und molekulare Bildgebung in der Mammadiagnostik ist ein sich rapide entwickelndes Forschungsfeld mit neuen vielversprechenden Techniken wie der Natriumbildgebung (23Na-MRT), der Phosphorspektroskopie (31P-MRSI), der Chemical-Exchange-Saturation-Transfer(CEST)-, Blood-Oxygen-Level-Dependent(BOLD)- und der hyperpolarisierten MRT sowie neuen zielgerichteten Radiotracern. In den letzten Jahren hat sich die multiparametrische und molekulare Bildgebung in der Mammadiagnostik etabliert. Die multiparametrische Bildgebung der Brust ist jedoch ein sich konstant weiter entwickelndes Forschungsfeld, deren Einfluss in den nachsten Jahren weiter zunehmen und so eine verbesserte Diagnose, Staging und zielgerichtete Therapie ermoglichen wird.

Funder: Horizon 2020 Framework Programme; doi: http://dx.doi.org/10.13039/100010661, Funder: Medical University of Vienna, PURPOSE: To assess whether a radiomics and machine learning (ML) model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI can discriminate between benign and malignant breast lesions. METHODS: A population of 102 patients with 120 breast lesions (101 malignant and 19 benign) detected on ultrasound and/or mammography was prospectively enrolled. All patients underwent hybrid 18F-FDG PET/MRI for diagnostic purposes. Quantitative parameters were extracted from DCE (MTT, VD, PF), DW (mean ADC of breast lesions and contralateral breast parenchyma), PET (SUVmax, SUVmean, and SUVminimum of breast lesions, as well as SUVmean of the contralateral breast parenchyma), and T2-weighted images. Radiomics features were extracted from DCE, T2-weighted, ADC, and PET images. Different diagnostic models were developed using a fine Gaussian support vector machine algorithm which explored different combinations of quantitative parameters and radiomics features to obtain the highest accuracy in discriminating between benign and malignant breast lesions using fivefold cross-validation. The performance of the best radiomics and ML model was compared with that of expert reader review using McNemar's test. RESULTS: Eight radiomics models were developed. The integrated model combining MTT and ADC with radiomics features extracted from PET and ADC images obtained the highest accuracy for breast cancer diagnosis (AUC 0.983), although its accuracy was not significantly higher than that of expert reader review (AUC 0.868) (p = 0.508). CONCLUSION: A radiomics and ML model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI images can accurately discriminate between benign and malignant breast lesions.

Background Multiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) shows clinical potential for detection and classification of breast lesions. Yet, the contribution of features for computer-aided segmentation and diagnosis (CAD) need to be better understood. We proposed a data-driven machine learning approach for a CAD system combining dynamic contrast-enhanced (DCE)-MRI, diffusion-weighted imaging (DWI), and 18F-fluorodeoxyglucose (18F-FDG)-PET. Methods The CAD incorporated a random forest (RF) classifier combined with mpPET/MRI intensity-based features for lesion segmentation and shape features, kinetic and spatio-temporal texture features, for lesion classification. The CAD pipeline detected and segmented suspicious regions and classified lesions as benign or malignant. The inherent feature selection method of RF and alternatively the minimum-redundancy-maximum-relevance feature ranking method were used. Results In 34 patients, we report a detection rate of 10/12 (83.3%) and 22/22 (100%) for benign and malignant lesions, respectively, a Dice similarity coefficient of 0.665 for segmentation, and a classification performance with an area under the curve at receiver operating characteristics analysis of 0.978, a sensitivity of 0.946, and a specificity of 0.936. Segmentation but not classification performance of DCE-MRI improved with information from DWI and FDG-PET. Feature ranking revealed that kinetic and spatio-temporal texture features had the highest contribution for lesion classification. 18F-FDG-PET and morphologic features were less predictive. Conclusion Our CAD enables the assessment of the relevance of mpPET/MRI features on segmentation and classification accuracy. It may aid as a novel computational tool for exploring different modalities/features and their contributions for the detection and classification of breast lesions. Electronic supplementary material The online version of this article (10.1186/s41747-019-0096-3) contains supplementary material, which is available to authorized users.

To compare bilateral diffusion-weighted MR imaging (DWI) at 3 T and 7 T in the same breast tumour patients. Twenty-eight patients were included in this IRB-approved study (mean age 56 ± 16 years). Before contrast-enhanced imaging, bilateral DWI with b = 0 and 850 s/mm2 was performed in 2:56 min (3 T) and 3:48 min (7 T), using readout-segmented echo planar imaging (rs-EPI) with a 1.4 × 1.4 mm2 (3 T)/0.9 × 0.9 mm2 (7 T) in-plane resolution. Apparent diffusion coefficients (ADC), signal-to-noise (SNR) and contrast-to-noise ratios (CNR) were assessed. Twenty-eight lesions were detected (18 malignant, 10 benign). CNR and SNR were comparable at both field strengths (p > 0.3). Mean ADC values at 7 T were 4–22 % lower than at 3 T (p ≤ 0.03). An ADC threshold of 1.275 × 10−3 mm2/s resulted in a diagnostic specificity of 90 % at both field strengths. The sensitivity was 94 % and 100 % at 3 T and 7 T, respectively. 7-T DWI of the breast can be performed with 2.4-fold higher spatial resolution than 3 T, without significant differences in SNR if compared to 3 T. • 7 T provides a 2.4-fold higher resolution in breast DWI than 3 T • 7 T DWI has a high diagnostic accuracy comparable to that at 3 T • At 7 T malignant lesions had 22 % lower ADC than at 3 T (p

The purposes of this study were to introduce and assess an automated user-independent quantitative volumetric (AUQV) breast density (BD) measurement system on the basis of magnetic resonance imaging (MRI) using the Dixon technique as well as to compare it with qualitative and quantitative mammographic (MG) BD measurements.Forty-three women with normal mammogram results (Breast Imaging Reporting and Data System 1) were included in this institutional review board-approved prospective study. All participants were subjected to BD assessment with MRI using the following sequence with the Dixon technique (echo time/echo time, 6 milliseconds/2.45 milliseconds/2.67 milliseconds; 1-mm isotropic; 3 minutes 38 seconds). To test the reproducibility, a second MRI after patient repositioning was performed. The AUQV magnetic resonance (MR) BD measurement system automatically calculated percentage (%) BD. The qualitative BD assessment was performed using the American College of Radiology Breast Imaging Reporting and Data System BD categories. Quantitative BD was estimated semiautomatically using the thresholding technique Cumulus4. Appropriate statistical tests were used to assess the agreement between the AUQV MR measurements and to compare them with qualitative and quantitative MG BD estimations.The AUQV MR BD measurements were successfully performed in all 43 women. There was a nearly perfect agreement of AUQV MR BD measurements between the 2 MR examinations for % BD (P0.001; intraclass correlation coefficient, 0.998) with no significant differences (P = 0.384). The AUQV MR BD measurements were significantly lower than quantitative and qualitative MG BD assessment (P0.001).The AUQV MR BD measurement system allows a fully automated, user-independent, robust, reproducible, as well as radiation- and compression-free volumetric quantitative BD assessment through different levels of BD. The AUQV MR BD measurements were significantly lower than the currently used qualitative and quantitative MG-based approaches, implying that the current assessment might overestimate breast density with MG.

Purpose: To develop a 3.0 Tesla breast imaging protocol that combines high temporal and spatial resolution three-dimensional MR sequences for quantitative time course and morphologic analysis of breast lesions. Materials and Methods: Thirty-four patients were included in the study (age range, 31–82; mean age, 54.3). The study protocol was approved by the Institutional Review Board and written informed consent was obtained from all patients. The magnetic resonance imaging protocol included: a coronal T1-weighted volume-interpolated-breathhold-examination sequence, focused on high temporal resolution for optimal assessment of the contrast-enhancement behavior of lesions (SI 1.7 mm isotropic; TA 3.45 minutes for 17 measurements); a coronal T1-weighted turbo fast-low-angle-shot-three-dimensional sequence, with water-excitation and fat suppression, focused on high spatial resolution for morphologic analysis (SI 1 mm isotropic; TA 2 minutes); and a repeated coronal volume-interpolated-breathhold-examination sequence for detection of washout. Lesion size and morphology were assessed. Region-of-interests for suspicious areas were manually drawn and evaluated for contrast-enhancement behavior by plotting intensity courses against time. Sensitivity and specificity with a 95% confidence interval and the negative predictive value and positive predictive value were calculated. Diagnostic accuracy was assessed. The histopathological diagnoses were used as a standard of reference. Results: Fifty-five lesions were detected in 34 patients. All malignant breast lesions were identified correctly. There were 5 false-positive lesions. The sensitivity of contrast-enhanced magnetic resonance imaging of the breast at 3 T was 100%, with a 95% confidence interval (CI) of 90.6% to 100%. The specificity was 72.2%, with a 95% CI of 49.1% to 87.5%. The positive predictive value was 0.88 and the negative predictive value was 1. Diagnostic accuracy was 91% with a 95% CI of 80.4% to 96.1%. Conclusion: Our prospective study demonstrates that the presented 3 Tesla MR imaging protocol, comprising both high temporal and high spatial resolution, enables accurate detection and assessment of breast lesions.

Objectives To assess whether using the Tree flowchart obviates unnecessary magnetic resonance imaging (MRI)-guided biopsies in breast lesions only visible on MRI. Methods This retrospective IRB-approved study evaluated consecutive suspicious (BI-RADS 4) breast lesions only visible on MRI that were referred to our institution for MRI-guided biopsy. All lesions were evaluated according to the Tree flowchart for breast MRI by experienced readers. The Tree flowchart is a decision rule that assigns levels of suspicion to specific combinations of diagnostic criteria. Receiver operating characteristic (ROC) curve analysis was used to evaluate diagnostic accuracy. To assess reproducibility by kappa statistics, a second reader rated a subset of 82 patients. Results There were 454 patients with 469 histopathologically verified lesions included (98 malignant, 371 benign lesions). The area under the curve (AUC) of the Tree flowchart was 0.873 (95% CI: 0.839–0.901). The inter-reader agreement was almost perfect (kappa: 0.944; 95% CI 0.889–0.998). ROC analysis revealed exclusively benign lesions if the Tree node was ≤2, potentially avoiding unnecessary biopsies in 103 cases (27.8%). Conclusions Using the Tree flowchart in breast lesions only visible on MRI, more than 25% of biopsies could be avoided without missing any breast cancer. Key Points • The Tree flowchart may obviate >25% of unnecessary MRI-guided breast biopsies. • This decrease in MRI-guided biopsies does not cause any false-negative cases. • The Tree flowchart predicts 30.6% of malignancies with >98% specificity. • The Tree’s high specificity aids in decision-making after benign biopsy results. Electronic supplementary material The online version of this article (doi:10.1007/s00330-017-4755-6) contains supplementary material, which is available to authorized users.

To evaluate the diagnostic value of combined high temporal and spatial resolution morphologic, dynamic, 1H-spectroscopic (3D-MRSI) and diffusion-weighted magnetic resonance imaging (MRI) in patients with breast lesions at 3 T using histology as the gold standard.

The objective of this study was to evaluate whether apparent diffusion coefficient (ADC) obtained through diffusion-weighted imaging magnetic resonance imaging at 3 T can be used as an imaging biomarker to differentiate invasive breast cancer from noninvasive ductal carcinoma in situ (DCIS).One hundred seventy-six histopathologically verified primary malignant breast tumors were retrospectively evaluated in 170 patients. All patients had undergone a standardized 3-T magnetic resonance imaging protocol, containing a diffusion-weighted sequence with 2 b values and a series of dynamic contrast-enhanced T1-weighted sequences. Apparent diffusion coefficient was measured manually by a reader blinded to the histopathological results. The ADC values were correlated with histopathological results. Mean ADC values were compared between invasive cancers and DCIS as well as between different tumor grades. Receiver operating characteristics curves were used to calculate diagnostic performance.There were 155 invasive cancers and 21 noninvasive DCIS. Mean (SD) values differed significantly between the invasive cancers (0.9 [0.15] ×10 mm/s) and the DCIS (1.24 [0.23] ×10 mm/s, P0.001). Area under the receiver operating characteristics curve was 0.895 (95% confidence interval [CI], 0.840-0.936). A threshold of 1.01 ×10 mm/s or less allowed an identification of invasive cancers with a sensitivity of 78.06% (95% CI, 70.7%-84.3%) and a specificity of 90.5% (95% CI, 69.6%-98.8%). No significant ADC differences were found among different tumor grades (P0.05).Apparent diffusion coefficient could be used as an imaging biomarker for the diagnosis of breast cancer. It seems to be a valuable noninvasive quantitative biomarker to assess breast cancer invasiveness. Thus, ADC measurements provide the potential to reduce overdiagnosis and subsequent overtreatment.

To explore the predictive value of parameters derived from diffusion-weighted imaging (DWI) and contrast-enhanced (CE)-MRI at different time-points during neoadjuvant chemotherapy (NACT) in breast cancer. Institutional review board approval and written, informed consent from 42 breast cancer patients were obtained. The patients were investigated before and at three different time-points during neoadjuvant chemotherapy (NACT) using tumour diameter and volume from CE-MRI and ADC values obtained from drawn 2D and segmented 3D regions of interest. Prediction of pathologic complete response (pCR) was evaluated using the area under the curve (AUC) of receiver operating characteristic analysis. There was no significant difference between pathologic complete response and non-pCR in baseline size measures (p > 0.39). Diameter change was significantly different in pCR (p