NEW TRENDS IN BREAST IMAGING FOR BREAST CANCER AND CARDIOVASCULAR RISK
Creators: name: TRIMBOLI, RUBINA MANUELA
Publisher: name: Universit� degli Studi di Milano
Year: 2020

description: 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 ?quantitative imaging? recently formally defined as ?the 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?. 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? 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.