Breast arterial calcifications as a biomarker of cardiovascular risk: radiologists' awareness, reporting, and action : a survey among the EUSOBI members

Objectives: To investigate the knowledge of radiologists on breast arterial calcifications (BAC) and attitude about BAC reporting, communication to women, and subsequent action. Methods: An online survey was offered to EUSOBI members, with 17 questions focused on demographics, level of experience, clinical setting, awareness of BAC association with cardiovascular risk, mammographic reporting, modality of BAC assessment, and action habits. Descriptive statistics were used. Results: Among 1084 EUSOBI members, 378 (34.9%) responded to the survey, 361/378 (95.5%) radiologists, 263 females (69.6%), 112 males (29.6%), and 3 (0.8%) who did not specify their gender. Of 378 respondents, 305 (80.7%) declared to be aware of BAC meaning in terms of cardiovascular risk and 234 (61.9%) to routinely include BAC in mammogram reports, when detected. Excluding one inconsistent answer, simple annotation of BAC presence was declared by 151/233 (64.8%), distinction between low versus extensive BAC burden by 59/233 (25.3%), and usage of an ordinal scale by 22/233 (9.5%) and of a cardinal scale by 1/233 (0.4%). Among these 233 radiologists reporting BAC, 106 (45.5%) declared to orally inform the woman and, in case of severe BAC burden, 103 (44.2%) to investigate cardiovascular history, and 92 (39.5%) to refer the woman to a cardiologist. Conclusion: Among EUSOBI respondents, over 80% declared to be aware of BAC cardiovascular meaning and over 60% to include BAC in the report. Qualitative BAC assessment predominates. About 40% of respondents who report on BAC, in the case of severe BAC burden, investigate cardiovascular history and/or refer the woman to a cardiologist

Mammography: an update of the EUSOBI recommendations on information for women

Abstract: This article summarises the information to be offered to women about mammography. After a delineation of the aim of early diagnosis of breast cancer, the difference between screening mammography and diagnostic mammography is explained. The need to bring images and reports from the previous mammogram (and from other recent breast imaging examinations) is highlighted. Mammography technique and procedure are described with particular attention to discomfort and pain experienced by a small number of women who undergo the test. Information is given on the recall during a screening programme and on the request for further work-up after a diagnostic mammography. The logic of the mammography report and of classification systems such as R1-R5 and BI-RADS is illustrated, and brief but clear information is given about the diagnostic performance of the test, with particular reference to interval cancers, i.e., those cancers that are missed at screening mammography. 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 with a reliable estimation of its extent. Information about new mammographic technologies (tomosynthesis and contrast-enhanced spectral mammography) is also given. Finally, frequently asked questions are answered. Key Points: \u2022 Direct digital mammography should be preferred to film-screen or phosphor plates. \u2022 Screening (in asymptomatic women) should be distinguished from diagnosis (in symptomatic women). \u2022 A breast symptom has to be considered even after a negative mammogram. \u2022 Digital breast tomosynthesis increases cancer detection and decreases the recall rate. \u2022 Contrast-enhanced spectral mammography can help in cancer detection and lesion characterisation

Technique, protocols and adverse reactions for contrast-enhanced spectral mammography (CESM ): a systematic review

We reviewed technical parameters, acquisition protocols and adverse reactions (ARs) for contrast-enhanced spectral mammography (CESM). A systematic search in databases, including MEDLINE/EMBASE, was performed to extract publication year, country of origin, study design; patients; mammography unit/vendor, radiation dose, low-/high-energy tube voltage; contrast molecule, concentration and dose; injection modality, ARs and acquisition delay; order of views; examination time. Of 120 retrieved articles, 84 were included from 22 countries (September 2003-January 2019), totalling 14012 patients. Design was prospective in 44/84 studies (52%); in 70/84 articles (83%), a General Electric unit with factory-set kVp was used. Per-view average glandular dose, reported in 12/84 studies (14%), ranged 0.43-2.65\u2009mGy. Contrast type/concentration was reported in 79/84 studies (94%), with Iohexol 350 mgI/mL mostly used (25/79, 32%), dose and flow rate in 72/84 (86%), with 1.5 mL/kg dose at 3\u2009mL/s in 62/72 studies (86%). Injection was described in 69/84 articles (82%), automated in 59/69 (85%), manual in 10/69 (15%) and flush in 35/84 (42%), with 10-30 mL dose in 19/35 (54%). An examination time\u2009<\u200910\u2009min was reported in 65/84 studies (77%), 120 s acquisition delay in 65/84 (77%) and order of views in 42/84 (50%) studies, beginning with the craniocaudal view of the non-suspected breast in 7/42 (17%). Thirty ARs were reported by 14/84 (17%) studies (26 mild, 3 moderate, 1 severe non-fatal) with a pooled rate of 0.82% (fixed-effect model). Only half of CESM studies were prospective; factory-set kVp, contrast 1.5 mL/kg at 3\u2009mL/s and 120 s acquisition delay were mostly used; only 1 severe AR was reported. CESM protocol standardisation is advisable

NEW TRENDS IN BREAST IMAGING FOR BREAST CANCER AND CARDIOVASCULAR RISK

Background Qualitative, subjective reading of medical images have been the backbone of image interpretation for the past century, providing useful information to the treating physician. During the past two decades, advances in medical imaging technology have offered the possibility to extract high-resolution anatomic, physiologic, functional, biochemical, and metabolic information from clinical images, all of which reflect the molecular composition of the healthy or diseased tissue of organs imaged in the human body. We are now entering the era of \u2015quantitative imaging\u2016 recently formally defined as \u2015the extraction of quantifiable features from medical images for the assessment of normality, or the severity, degree of change, or status of a disease, injury, or chronic condition relative to normal\u2016. With appropriate calibration, most of the current imaging technologies can provide quantitative information about specific properties of the tissues being imaged. Purpose This doctoral thesis aims at exploring the possible use of imaging methods such as mammography and breast magnetic resonance imaging (MRI) as imaging biomarkers, measuring functional, biochemical and metabolic characteristics of the breast through medical images. Part I. Breast arterial calcifications for cardiovascular risk Breast arterial calcifications (BAC) are easily recognizable on screening mammography and are associated with coronary artery disease. We tried to implement the estimation of BAC to be easily applicable in clinical prevention of cardiovascular disease. In particular, we evaluated the intra- and inter-observer reproducibility of i) a specifically developed semi-automatic tool and of ii) a semi-quantitative scale for BAC quantification on digital mammograms. Part II. Multiparametric breast MRI for breast cancer management Multiparametric breast MRI allows to simultaneously quantify and visualize multiple functional processes at the cellular and molecular levels to further elucidate the development and progression of breast cancer (BC) and the response to treatment. The purpose of our study was to verify the correlation between enhancement parameters derived from routine breast contrast-enhancement MRI and pathological prognostic factors in invasive BC as a condition for the use of MRI-derived imaging biomarkers in adjunct to traditional prognostic tools in clinical decision making. Part III. Artificial intelligence in Breast MRI Recent enthusiasm regarding the introduction of artificial intelligence (AI) into health care and, in particular, into radiology has increased clinicians\u2018 expectations and also fears regarding the possible impact of AI on their profession. The large datasets provided by and potentially extractable from breast MRI make it the right 6 stuff for fitting AI applications. This session focuses on a systematic mapping review of the literature on AI application in breast MRI published during the past decade, analysing the phenomenon in terms of spread, clinical aim, used approach, and achieved results. Conclusions Medical images represent imaging biomarkers of considerable interest in evidence- based clinical decisionmaking, for therapeutic development and treatment monitoring. Among imaging biomarkers, BAC represent the added value of an ongoing and consolidated cancer screening to act for preventing the main cause of death among women in which traditional CV risk scores do not adequately perform. Breast MRI may act as a prognostic tool to improve BC management through the extraction of a plenty of functional cancer parameters. AI might certainly implement the use of imaging data interacting with and integrating quantitative imaging for improving patient outcome and reducing several sources of bias and variance in the quantitative results obtained from clinical images. The intrinsic multiparametric nature of MRI has the greatest potential to incorporate AI applications into the so called precision medicine. Nevertheless, AI applications are still not ready to be incorporated into clinical practice nor to replace the trained and experienced observer with the ability to interpret and judge during image reading sessions

Artificial intelligence for breast MRI in 2008-2018 : a systematic mapping review

OBJECTIVE. The purpose of this study is to review literature from the past decade on applications of artificial intelligence (AI) to breast MRI. MATERIALS AND METHODS. In June 2018, a systematic search of the literature was performed to identify articles on the use of AI in breast MRI. For each article identified, the surname of the first author, year of publication, journal of publication, Web of Science Core Collection journal category, country of affiliation of the first author, study design, dataset, study aim(s), AI methods used, and, when available, diagnostic performance were recorded. RESULTS. Sixty-seven studies, 58 (87%) of which had a retrospective design, were analyzed. When journal categories were considered, 36% of articles were identified as being included in the radiology and imaging journal category. Contrast-enhanced sequences were used for most AI applications (n = 50; 75%) and, on occasion, were combined with other MRI sequences (n = 8; 12%). Four main clinical aims were addressed: breast lesion classification (n = 36; 54%), image processing (n = 14; 21%), prognostic imaging (n = 9; 13%), and response to neoadjuvant therapy (n = 8; 12%). Artificial neural networks, support vector machines, and clustering were the most frequently used algorithms, accounting for 66%. The performance achieved and the most frequently used techniques were then analyzed according to specific clinical aims. Supervised learning algorithms were primarily used for lesion characterization, with the AUC value from ROC analysis ranging from 0.74 to 0.98 (median, 0.87) and with that from prognostic imaging ranging from 0.62 to 0.88 (median, 0.80), whereas unsupervised learning was mainly used for image processing purposes. CONCLUSION. Interest in the application of advanced AI methods to breast MRI is growing worldwide. Although this growth is encouraging, the current performance of AI applications in breast MRI means that such applications are still far from being incorporated into clinical practice

Screening mammography beyond breast cancer: breast arterial calcifications as a sex-specific biomarker of cardiovascular risk

Purpose: To highlight the importance of quantitative breast arterial calcifications (BAC) assessment for an effective stratification of cardiovascular (CV) risk in women, for whom current preventive strategies are inadequate. BAC, easily detectable on mammograms, are associated with CV disease and represent a potential imaging biomarker for CV disease prevention in women. Method: We summarized the available evidence on this topic. Results: Age, parity, diabetes, and hyperlipidemia were found to positively correlate with BAC. Women with BAC have a higher CV risk than those without BAC: the relative risk was reported to be 1.4 for transient ischemic attack/stroke, 1.5 for thrombosis, 1.8 for myocardial infarction; the reported hazard ratio was 1.32 for coronary artery disease (CAD), 1.52 for heart failure, 1.29 for CV death, 1.44 for death from CAD. However, BAC do not alarm radiologists; when reported, they are commonly mentioned as "present", not impacting on CV decision-making. Of 18 published studies, 9 reported only presence/absence of BAC, 4 used a semi-quantitative scale, and 5 a continuous scale (with manual, automatic or semiautomatic segmentation). Various appearance, topological complexity, and vessels overlap make BAC quantification difficult to standardize. Nevertheless, machine learning approaches showed promising results in BAC quantification on mammograms. Conclusions: There is a strong rationale for mammography to become a dual test for breast cancer screening and CV disease prevention. However, robust and automated quantification methods are needed for a deeper insight on the association between BAC and CV disease, to stratifying CV risk and define personalized preventive actions

Optical Imaging of the Breast: Basic Principles and Clinical Applications

OBJECTIVE. The objective of this article is to summarize the physical principles, technology features, and first clinical applications of optical imaging techniques to the breast. CONCLUSION. Light-breast tissue interaction is expressed as absorption and scattering coefficients, allowing image reconstruction based on endogenous or exogenous contrast. Diffuse optical spectroscopy and imaging, fluorescence molecular tomography, photoacoustic imaging, and multiparametric infrared imaging show potential for clinical application, especially for lesion characterization, estimation of cancer probability, and monitoring the effect of neoadjuvant therapy