Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: A review.

Purpose Of The Review: We aim to review the methods, current research evidence, and future directions in body composition analysis (BCA) with CT imaging.

Recent Findings: CT images can be used to evaluate muscle tissue, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) compartments. Manual and semiautomatic segmentation methods are still the gold standards.

Radiological and pathological assessment of response to neoadjuvant CDK4/6 inhibitor and endocrine treatments in a real-life setting-initial results.

Background: Neoadjuvant endocrine therapy is an alternative to neoadjuvant chemotherapy in women with inoperable luminal-like breast cancers. Neoadjuvant cyclin-dependent kinase 4/6 inhibitor treatment combined with endocrine treatment (CDK4/6I + E) is interesting given the combination's utility in the treatment of metastatic breast cancer. Currently, the literature on the radiological response evaluation of patients treated with neoadjuvant CDK4/6I + E in a real-life setting is scarce.

A Comparison of Regions of Interest in Parenchymal Analysis for Breast Cancer Risk Assessment.

Computerized parenchymal analysis has shown potential to be utilized as an imaging biomarker to estimate the risk of breast cancer. Parenchymal analysis of digital mammograms is based on the extraction of computerized measures to build machine learning-based models for the prediction of breast cancer risk. However, the choice of the region of interest (ROI) for feature extraction within the breast remains an open problem.

A New Benchmark and Method for the Evaluation of Chest Wall Detection in Digital Mammography.

CAD systems have shown good potential for improving breast cancer diagnosis and anomaly detection in mammograms. A basic enabling step for the utilization of CAD systems in mammographic analysis is the correct identification of the breast region. Therefore, several methods to segment the pectoral muscle in the medio-lateral oblique (MLO) mammographic view have been proposed in the literature.

Do Mammographic Systems Affect the Performance of Computerized Parenchymal Analysis?

Early identification of women at high risk of developing breast cancer is fundamental for timely diagnosis and treatment. Recently, researchers have demonstrated that the computerized analysis of parenchymal (breast tissue) patterns in mammograms can be utilized to assess the risk level of patients. However, parenchymal analysis being an image-based biomarker, its performance may be affected by the acquisition parameters of the mammogram.

Morphological Area Gradient: System-independent Dense Tissue Segmentation in Mammography Images.

Breast density has been identified as one of the strongest risk factors for breast cancer. However, the development of reliable and reproducible methods for the automatic dense tissue segmentation has been an important challenge. Due to the complexity of the acquisition process of mammography images, current approaches need to be calibrated for specific mammographic systems or require access to raw mammograms.

Clinical evaluation of a fully-automated parenchymal analysis software for breast cancer risk assessment: A pilot study in a Finnish sample.

Purpose: To assess the association between breast cancer risk and mammographic parenchymal measures obtained using a fully-automated, publicly available software, OpenBreast.

Methods: This retrospective case-control study involved screening mammograms of asymptomatic women diagnosed with breast cancer between 2016 and 2017. The 114 cases were matched with corresponding healthy controls by birth and screening years and the mammographic system used.