Clinical Utility of Ultrasonography BI-RADS in the Evaluation of Breast Cancer in Patients with Palpable Breast Masses: A Diagnostic Test Accuracy Original Article
Gami, V.; Desai, D.; Shah, S.; Rana, D.
Show abstract
IntroductionDiagnosing and staging breast cancer with an easy and widely useable method that can be employed worldwide in the poorest and wealthiest settings is important. Mammography is a technique that might not be available in faraway clinics and it is technically challenging whereas USG can be available in most remote areas and small hospitals far from tertiary care hospitals. Even a trainee Radiology resident can use USG BIRADS and can be used diagnostically for that, it is important to define its, diagnostic accuracy with Sensitivity, Specificity, and other diagnostic parameters. AimsTo determine the Diagnostic accuracy of USG BIRADS compared to the gold standard Histopathology report MethodologyA Retrospective cohort study was conducted at a tertiary care hospital. a total of 84 female patients presenting to Surgical OPD with complaints of a breast lump or pain were enrolled from their records. Their Breast USG results were analyzed to identify their BIRADS stage correctly and then their corresponding Histopathology report was considered the gold standard to compare the USG results against. Excel, SPSS, and Revman were used to conduct analysis and create results. Results36 of these 84 patients belonged to BIRADS 1, 2, and 5 where Sensitivity, Specificity, and PPV were calculated at 100%. No one was diagnosed with BIRADS III from USG reports. For USG BIRADS 4, in total 48 patients Sensitivity was 0.667, specificity was 0.883, and PPV was 0.364. ConclusionPatients whose USG shows Benign growth or can be diagnosed in BIRADS 1, 2, 3, and 5 can be counted as accurate and precise. When the USG diagnosis describes the patient to be in BIRADS 4, the sensitivity and PPV show poor results showing a very low probability of the patient being truly positive when the diagnosis gives a positive result.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Factors associated with breast lesions among women attending select teaching and referral health facilities in Kenya: A cross-sectional study 98%
- Knowledge towards breast cancer, and breast self-examination practices and its barriers among university female students in Bangladesh: Findings from a cross-sectional study 97%
- National diagnostic reference levels for digital diagnostic and screening mammography in Uganda. 96%
Similar papers in this journal
- Effectiveness of educational intervention on breast cancer knowledge and breast self-examination among female university students in Bangladesh: a pre-post quasi-experimental one group study 97%
- The role of KPNA2 mutations in breast cancer prognosis: A survey of publicly available databases 92%
- PDAC-ANN: an artificial neural network to predict Pancreatic Ductal Adenocarcinoma based on gene expression 91%
Similar papers in this journal
Similar papers in this journal
- Mammographic density assessed using deep learning in women at high risk of developing breast cancer: the effect of weight change on density 91%
- Model uncertainty estimates for deep learning mammographic density prediction using ordinal and classification approaches 90%
- Breast density prediction from low and standard dose mammograms using deep learning: effect of image resolution and model training approach on prediction quality 84%
Similar papers in this journal
- Validation of the patient reported outcome measures tool “Catquest” in Odia language 91%
- Cervical Cancer Screening in South Florida Veteran Population 2014-2020: Cytology and High-Risk HPV Correlation and HPV Epidemiology 91%
- Comparative study between first and second wave of COVID-19 deaths in India - a single center study 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.