Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk.

We compared accuracy for breast cancer (BC) risk stratification of a new fully automated system (DenSeeMammo-DSM) for breast density (BD) assessment to a non-inferiority threshold based on radiologists' visual assessment. Pooled analysis was performed on 14,267 2D mammograms collected from women aged 48-55 years who underwent BC screening within three studies: RETomo, Florence study and PROCAS. BD was expressed through clinical Breast Imaging Reporting and Data System (BI-RADS) density classification.

AI for reading screening mammograms: the need for circumspection.

• The studies on AI reading of screening mammograms have methodological limitations that undermine the conclusion that AI could do better than radiologists. • These studies do not informon numbers of extra breast cancers found by AI that could represent overdiagnosis. • The ability of AI to detect breast cancers is overestimated because there are no result on biopsy procedures that should be performed when mammograms are positive at AI reading but not at radiology reading.

Improved cancer detection in automated breast ultrasound by radiologists using Computer Aided Detection.

Objective: To investigate the effect of dedicated Computer Aided Detection (CAD) software for automated breast ultrasound (ABUS) on the performance of radiologists screening for breast cancer.

Methods: 90 ABUS views of 90 patients were randomly selected from a multi-institutional archive of cases collected between 2010 and 2013. This dataset included normal cases (n=40) with >1year of follow up, benign (n=30) lesions that were either biopsied or remained stable, and malignant lesions (n=20).

Computer-aided detection of breast cancers using Haar-like features in automated 3D breast ultrasound.

Purpose: Automated 3D breast ultrasound (ABUS) has gained interest in breast imaging. Especially for screening women with dense breasts, ABUS appears to be beneficial. However, since the amount of data generated is large, the risk of oversight errors is substantial.

Evaluation of the effect of computer-aided classification of benign and malignant lesions on reader performance in automated three-dimensional breast ultrasound.

Rationale And Objectives: To investigate the effect of a newly developed computer-aided diagnosis (CAD) system on reader interpretation of breast lesions in automated three-dimensional (3D) breast ultrasound.

Materials And Methods: A CAD system was developed to differentiate malignant lesions from benign lesions including automated lesion segmentation in three dimensions; extraction of lesion features such as spiculation, margin contrast, and posterior acoustic behavior; and a classification stage. Eighty-eight patients with breast lesions were included for an observer study: 47 lesions were malignant and 41 were benign.