A Metric for Reducing False Positives in the Computer-Aided Detection of Breast Cancer from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Screening Examinations of High-Risk Women.

Magnetic resonance imaging (MRI)-enabled cancer screening has been shown to be a highly sensitive method for the early detection of breast cancer. Computer-aided detection systems have the potential to improve the screening process by standardizing radiologists to a high level of diagnostic accuracy. This retrospective study was approved by the institutional review board of Sunnybrook Health Sciences Centre.

A vector machine formulation with application to the computer-aided diagnosis of breast cancer from DCE-MRI screening examinations.

This study investigates the use of a proposed vector machine formulation with application to dynamic contrast-enhanced magnetic resonance imaging examinations in the context of the computer-aided diagnosis of breast cancer. This paper describes a method for generating feature measurements that characterize a lesion's vascular heterogeneity as well as a supervised learning formulation that represents an improvement over the conventional support vector machine in this application. Spatially varying signal-intensity measures were extracted from the examinations using principal components analysis and the machine learning technique known as the support vector machine (SVM) was used to classify the results.

A margin sharpness measurement for the diagnosis of breast cancer from magnetic resonance imaging examinations.

Rationale And Objectives: Cancer screening by magnetic resonance imaging (MRI) has been shown to be one of the most sensitive methods available for the early detection of breast cancer. There is high variability in the diagnostic accuracy of radiologists analyzing the large amounts of data acquired in a breast MRI examination, and this has motivated substantial research toward the development of computer-aided detection and diagnosis systems. Most computer-aided diagnosis systems for breast MRI focus on dynamic information (how a lesion's brightness changes over the course of an examination after the injection of a contrast agent).

Effect of the enhancement threshold on the computer-aided detection of breast cancer using MRI.

Rationale And Objectives: To evaluate the effect that variations in the enhancement threshold have on the diagnostic accuracy of two computer-aided detection (CAD) systems for magnetic resonance based breast cancer screening.

Materials And Methods: Informed consent was obtained from all patients participating in cancer screening and this study was approved by the participating institution's review board. This retrospective study was nested in a prospective, single-institution, high-risk, breast screening study involving dynamic contrast-enhanced magnetic resonance imaging.