BPE and computer-extracted parenchymal enhancement for breast cancer risk, response monitoring, and prognosis
Functional behavior of breast cancer - representing underlying biology - can be analyzed using MRI. The most widely used breast MR imaging protocol is dynamic contrast-enhanced T1-weighted imaging. The cancer enhances on dynamic contrast-enhanced MR imaging because the contrast agent leaks from the leaky vessels into the interstitial space. The contrast agent subsequently leaks back into the vascular space, creating a washout effect. The normal parenchymal tissue of the breast can also enhance after contrast injection. This enhancement generally increases over time. Typically, a radiologist assesses this background parenchymal enhancement (BPE) using the Breast Imaging Reporting and Data System (BI-RADS). According to the BI-RADS, BPE refers to the volume of enhancement and the intensity of enhancement and is divided in four incremental categories: minimal, mild, moderate, and marked. Researchers have developed semi-automatic and automatic methods to extract properties of BPE from MR images. For clarity, in this syllabus the BI-RADS definition will be referred to as BPE, whereas the computer-extracted properties will not. Both BPE and computer-extracted parenchymal enhancement properties have been linked to screening and diagnosis, hormone status and age, risk of development of breast cancer, response monitoring, and prognosis.
Code (0)
등록된 구현이 없습니다.
Tasks
PrognosisMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs
Characterization of breast parenchyma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Existing quantitative approaches, lik…
Deep LearningMulti Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification
Classification of malignancy for breast cancer and other cancer types is usually tackled as an object detection problem: Individual lesions are first localized and then classified with respect to malignancy. However, the…
General ClassificationObjectobject-detectionObject DetectionFibroglandular Tissue Segmentation in Breast MRI using Vision Transformers -- A multi-institutional evaluation
Accurate and automatic segmentation of fibroglandular tissue in breast MRI screening is essential for the quantification of breast density and background parenchymal enhancement. In this retrospective study, we developed…
SegmentationTopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer
Characterization of breast parenchyma on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Current quantitative approaches, incl…
Research on the Detection Method of Breast Cancer Deep Convolutional Neural Network Based on Computer Aid
Traditional breast cancer image classification methods require manual extraction of features from medical images, which not only require professional medical knowledge, but also have problems such as time-consuming and l…
ClassificationGeneral Classificationimage-classificationImage Classification