Clinical and Imaging Features of MRI Screen-Detected Breast Cancer.

Clin Breast Cancer

The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD.

Published: January 2024


Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Background: Supplemental screening with breast MRI is recommended annually for patients who have greater than 20% lifetime risk for breast cancer. While there is robust data regarding features of mammographic screen-detected breast cancers, there is limited data regarding MRI-screen-detected cancers.

Patients And Methods: Screening breast MRIs performed between August 1, 2016 and July 30, 2022 identified 50 screen-detected breast cancers in 47 patients. Clinical and imaging features of all eligible cancers were recorded.

Results: During the study period, 50 MRI-screen detected cancers were identified in 47 patients. The majority of MRI-screen detected cancers (32/50, 64%) were invasive. Pathology revealed ductal carcinoma in situ (DCIS) in 36% (18/50), invasive ductal carcinoma (IDC) in 52% (26/50), invasive lobular carcinoma in 10% (5/50), and angiosarcoma in 2% (1/50). The majority of patients (43/47, 91%) were stage 0 or 1 at diagnosis and there were no breast cancer-related deaths during the follow-up periods. Cancers presented as masses in 50% (25/50), nonmass enhancement in 48% (25/50), and a focus in 2% (1/50). DCIS was more likely to present as nonmass enhancement (94.4%, 17/18), whereas invasive cancers were more likely to present as masses (75%, 24/32) (P < .001). All cancers that were stage 2 at diagnosis were detected either on a baseline exam or more than 4 years since the prior MRI exam.

Conclusion: MRI screen-detected breast cancers were most often invasive cancers. Cancers detected by MRI screening had an excellent prognosis in our study population. Invasive cancers most commonly presented as a mass.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11328159PMC
http://dx.doi.org/10.1016/j.clbc.2023.09.012DOI Listing

Publication Analysis

Top Keywords

screen-detected breast
16
cancers
12
breast cancers
12
invasive cancers
12
clinical imaging
8
imaging features
8
mri screen-detected
8
breast
8
breast cancer
8
screening breast
8

Similar Publications

Purpose: To evaluate whether AI-assisted ipsilateral tissue matching in digital breast tomosynthesis (DBT) reduces localization errors beyond typical tumor boundaries, particularly for non-expert radiologists. The technology category is deep learning.

Materials And Methods: The study consisted of two parts.

View Article and Find Full Text PDF

The lower all-cause mortality in women with Ductal carcinoma in situ (DCIS) compared with the general population has been hypothesized to be due to a "healthy-user effect," but this has not been studied in large cohorts. In a population-based, retrospective cohort study comprising 18,942 women with primary DCIS between 1999 and 2015 in the Netherlands, the cumulative incidence of breast cancer death (BCD) was estimated using death by other cause as a competing risk. The cause-specific mortality risk of women with DCIS was compared with that of the Dutch female population.

View Article and Find Full Text PDF

BackgroundArtificial intelligence (AI)-based systems have the potential to increase the efficiency and effectiveness of breast cancer screening programs but need to be carefully validated before clinical implementation.PurposeTo retrospectively evaluate an AI system to safely reduce the workload of a double-reading breast cancer screening program.Material and MethodsAll digital mammography (DM) screening examinations of women aged 40-74 years between August 2021 and January 2022 in Östergötland, Sweden were included.

View Article and Find Full Text PDF

Background: Breast cancer screening programmes have shown to reduce mortality, but current methods face challenges such as limited mammographic sensitivity, limited resources, and variability in radiologist expertise. Artificial intelligence (AI) offers potential to improve screening accuracy and efficiency. This study simulated different screening scenarios, evaluating the performance of population-based breast cancer screening when using an AI system as a stand-alone reader or second reader.

View Article and Find Full Text PDF

Background And Purpose: Organised mammography screening reduces breast cancer mortality by 30-40% in women aged 50-69. Despite limited evidence for women aged 40-49, screening guidelines are trending toward younger ages. Iceland has offered biennial screening to women aged 40-69 since 1987.

View Article and Find Full Text PDF