Aim: False-negative mammograms may result in a delay in breast carcinoma diagnosis and have important implications for patient care. In this study, the characteristics of symptomatic patients with false-negative mammograms were analysed.

Methods: Patients with symptomatic breast carcinoma were identified over a 10-year period (1994-2004). One hundred and twenty-four patients had false-negative preoperative mammograms and 1241 patients had abnormal preoperative mammograms. Clinical presentation, diagnostic methods and pathology were analysed. False-negative mammograms were reviewed by a specialist breast radiologist.

Results: Following retrospective review, 42% of false-negative mammograms were re-categorised as suspicious. The most commonly misinterpreted lesion was architectural distortion/asymmetrical density. Adjuvant ultrasound, where performed (n = 27), raised the level of suspicion in 93% of cases. Patients with false-negative mammograms were more likely to be younger (P < 0.0001), present with nipple discharge (P = 0.002) and have smaller tumours (P < 0.0001). Their tumours were more frequently located outside the upper outer quadrant (P = 0.002). False-negative mammography led to a delay in diagnosis of >2 months in 12 patients.

Conclusion: Symptomatic patients with false-negative mammograms often demonstrate definite abnormalities on imaging, the most common of which is architectural distortion/asymmetrical density. Those at particular risk were younger patients, those with nipple discharge, and patients with lesions located outside the upper outer quadrant.

Download full-text PDF

Source
http://dx.doi.org/10.1002/jso.20801DOI Listing

Publication Analysis

Top Keywords

false-negative mammograms
24
patients false-negative
16
breast carcinoma
12
symptomatic breast
8
false-negative
8
mammograms
8
patients
8
symptomatic patients
8
preoperative mammograms
8
architectural distortion/asymmetrical
8

Similar Publications

Objectives: Limited understanding exists regarding non-detected cancers in digital breast tomosynthesis (DBT) screening. This study aims to classify non-detected cancers into true or false negatives, compare them with true positives, and analyze reasons for non-detection.

Materials And Methods: Conducted between 2010 and 2015, the prospective single-center Malmö Breast Tomosynthesis Screening Trial (MBTST) compared one-view DBT and two-view digital mammography (DM).

View Article and Find Full Text PDF

Introduction: Research concerning artificial intelligence in breast cancer detection has primarily focused on population screening. However, Hong Kong lacks a population-based screening programme. This study aimed to evaluate the potential of artificial intelligence-based computer-assisted diagnosis (AI-CAD) program in symptomatic clinics in Hong Kong and analyse the impact of radio-pathological breast cancer phenotype on AI-CAD performance.

View Article and Find Full Text PDF

Background And Aims: To analyze the radiologic and histologic characteristics of screening and interval cancers diagnosed in the period comprising 2007 through 2018 in a total of six rounds of a population-based breast cancer screening program.

Material And Methods: We analyzed 1395 carcinomas detected at screening and 300 interval carcinomas diagnosed in women aged 50-69 years old who underwent digital mammography every two years during the study period. Screening mammograms were read once.

View Article and Find Full Text PDF

An approach for classification of breast cancer using lightweight deep convolution neural network.

Heliyon

October 2024

Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.BOX 84428, Riyadh, 11671, Saudi Arabia.

The rapid advancement of deep learning has generated considerable enthusiasm regarding its utilization in addressing medical imaging issues. Machine learning (ML) methods can help radiologists to diagnose breast cancer (BCs) barring invasive measures. Informative hand-crafted features are essential prerequisites for traditional machine learning classifiers to achieve accurate results, which are time-consuming to extract.

View Article and Find Full Text PDF
Article Synopsis
  • A total of 150 women underwent bilateral mammography and breast ultrasounds, revealing a 5.1% false-negative rate, with major contributing factors being lesion-related (39.7%), patient-related (26.7%), provider-related (19.3%), and technical-related issues (26.7%).
  • The findings emphasize the impact of dense breast tissue on missed diagnoses and recommend incorporating additional imaging methods like ultrasounds to improve detection rates, particularly for younger women, suggesting potential updates to Pakistan's national breast cancer screening guidelines.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!