Biomarkers and Imaging of Breast Cancer

Abstract

Clinical breast care and breast cancer-related research rely on imaging biomarkers for decision support. In the era of precision medicine and big data, the practice of radiology is likely to change. A closer integration of breast imaging with related biomedical fields and the creation of large integrated and shareable databases of clinical, molecular, and imaging biomarkers should allow the field to continue guiding breast cancer care and research.

Advanced Quantitative Spine Imaging

“…Quantitative imaging data can potentially be used in a variety of ways. 1 On an individual patient basis, quantitative spine imaging data can be used in these arenas:…”

“…The objective data provided should be precise, reproducible, and clinically relevant. 1 One of the frustrating features currently of quantitative spine imaging is the variability of data reported by different, seemingly rigorously conducted studies. For example, comparing studies reporting cervical cord crosssectional area (CSA), two studies on asymptomatic European and Japanese subjects, respectively, reported values of $ 72 to 79 mm 2 for cervical cord CSA.…”

Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis

“…Obviously CAD can be considered part of radiomics, but in contrast to CAD's simplicity and ability for answering only simple clinical questions, radiomic analysis considers more complex computational processes aiding decision support, by utilizing a plethora of quantitative imaging features—potential imaging biomarkers, extracted from digital images [ 26 , 28 ]. Furthermore, the correlation of these large-scale radiological phenotypic characteristics with the rich breast histopathological data available, e.g., the expression statuses of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor 2 receptor (HER2), and triple negative (lack of expression of ER, PR, and HER2), facilitates their strong association with molecular subtypes, which eventually results in the generation of pathology prognostic and predictive models [ 4 , 26 , 27 , 29 ].…”

“…After tumor delineation, radiomic features are extracted from the information contained in the segmented ROIs that can be used to qualitatively assess tumor phenotype, aggressiveness, treatment response, and cancer genetics, and differentiate between benign and malignant tumors [ 5 ]. The further processing and selection between the varieties of radiomic features derived leads to the potential definition of qualitative imaging biomarkers (QIB) that holds prognostic and predictive values for cancer outcome [ 28 ]. Therefore, when found to have significant correlation with tumor's biological properties, these parameters may possibly serve as useful endpoints for the assessment of the severity, degree of change, or status of a cancer lesion, relative to normal [ 22 ].…”

“…Higher order statistics derived features, referred to as textural features, have been widely utilized in breast tumor DCE-MRI parametric maps in the past, for improving characterization of breast lesions and their response to treatment [ 43 – 47 ]. Second-order histograms such as gray-level co-occurrence matrices (GLCMs) [ 28 , 48 ] and gray-level run-length matrices (GLRLMs) [ 29 , 49 ] characterize spatial relationships between pixel intensities in different 2D or 3D directions and thus are robust in quantifying tumor structural properties and various patterns of heterogeneity. In particular, GLCM analysis of DCE MR data has been proved to be robust in differentiating between benign and malignant breast lesions [ 50 ].…”