Breast Ultrasound Following a Positive Clinical Breast Examination: Does It Have a Role in Low- and Middle-Income Countries?
Bibliographic record
Abstract
Purpose: Breast cancer is the most common cancer among women worldwide, with an estimated 1.7 million new cases occurring in 2012. The majority of cases and deaths occur in low- and middle-income countries (LMICs), where population-based mammography screening is not available and countries must rely on clinical breast examination (CBE). Since ultrasound has the potential to reduce unnecessary biopsies by triaging women with palpable or focal breast findings at CBE, we searched for evidence in the literature on the effectiveness of ultrasound in detecting potential breast cancer following positive CBE findings. Methods: We reviewed the literature from 2000 to 2014 for evidence on the performance of breast ultrasound, in the absence of mammography, used to evaluate women after a positive CBE. From the studies meeting our inclusion/exclusion criteria for our analysis, we extracted data on the study design, location, ultrasound transducer parameters, patient age, method for determining positive and negative cases, and number of malignancies detected/total number of women studied. Results: We found 15 studies matching our inclusion/exclusion criteria, 9 from high-income countries and 6 from LMICs. Despite considerable variability in study design and patient populations, breast ultrasound consistently showed high sensitivity (median = 94 percent) and specificity (median = 80 percent) for detecting breast cancer and identifying normal and benign findings not requiring a biopsy. Clear patterns related to transducer frequency or income level were not discernible given the variations in patient populations and final diagnostic determinations. Conclusion: Our systematic review suggests that breast ultrasound following a positive CBE may be a powerful diagnostic test to determine those who do or do not need biopsy. We encourage further research in breast ultrasound use after a positive CBE in LMICs to assess the accuracy of ultrasound in these settings and the feasibility of widespread implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".