ArticleCureus2024
The Accuracy of Breast Cancer Diagnostic Tools.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Distinguishing Benign from Malignant Small Enhancing Breast Lesions Using Radiomics.Diagnostics (Basel, Switzerland) · 2026Article
- Machine Learning Empowered a Graphical User Interface on Native Fluorescence to Predict Breast Cancer.ACS omega · 2025Article
- Comparison of Contrast-enhanced Mammography and Low-Energy Imaging with or without Supplemental Whole-Breast US in Breast Cancer Detection.Radiology · 2025Observational
- Early and hereditary breast cancer: advances in risk stratification and imaging approaches.Therapeutic advances in medical oncology · 2025Review
- Study on the classification of benign and malignant breast lesions using a multi-sequence breast MRI fusion radiomics and deep learning model.European journal of radiology open · 2024Article
- Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Breast cancer (BC) remains a significant health concern, leading to illness and death among women globally. It is essential to detect BC early using imaging techniques that accurately reflect the final pathology, guiding suitable intervention strategies. Objectives This study aimed to evaluate the agreement between radiological findings and histopathological results in BC cases. Methods We conducted a retrospective review of breast core needle biopsies (CNBs) in women over a six-year period (2017-2022) at Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. The pathological diagnoses were compared with the findings from preceding radiological investigations. We also compared the tumour sizes in the resection specimens with their radiological counterparts. Results A total of 641 cases were included in the study. Ultrasound (US), mammography, and magnetic resonance imaging (MRI) yielded diagnostic accuracies of 85%, 77.9%, and 86.9%, respectively. MRI had the highest sensitivity at 72.2%, while US had the lowest at 61%. MRI provided the best agreement with the final resected tumor size. By contrast, mammography tended to overestimate the size (41.9%), and US most frequently underestimated it (67.7%). The connection between basal-like molecular subtypes and the Breast Imaging Reporting and Data System (BIRADS)-5 classifications was only statistically significant for MRI (p = 0.04). The luminal subtype was more likely to show speculation in mammography. Meanwhile, BIRADS-4 revealed a considerable number of benign pathologies across all the three modalities. Conclusions MRI demonstrated the highest accuracy, sensitivity, specificity, and positive predictive value (PPV) for diagnosing and estimating the tumor size. Mammography outperformed US in terms of sensitivity and yielded the highest negative predictive value (NPV). US, meanwhile, offered superior specificity, PPV, and accuracy. Therefore, combining these diagnostic methods could yield significant benefits.
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