A sensitivity and specificity comparison of fine needle aspiration cytology and core needle biopsy in evaluation of suspicious breast lesions: A systematic review and meta-analysis
Bibliographic record
Abstract
Purpose Breast cancer detections for women with suspicious lesions mainly depend on two non-operative pathological tests-fine needle aspiration cytology (FNAC) and core needle biopsy (CNB). The aim of this systematic review was to compare the sensitivity and specificity of CNB and FNAC in this setting. Methods The data sources included MEDLINE, EMBASE, PubMed, and the Cochrane Central Register of Controlled Trials (CENTRAL) till February 2016. We included prospective series of studies which directly compared the accuracy of FNAC and CNB. We used forest plots to display the sensitivity and specificity of FNAC and CNB respectively. Pre-specified subgroup analyses and sensitivity analysis were conducted. Results Ultimately, 12 articles (1802 patients) were included in the final analysis. The pooled analysis shows that the sensitivity of CNB is better than that of FNAC [87% (95% CI, 84%–88%, I 2 = 88.5%) versus 74% (95% CI, 72%–77%, I 2 = 88.3%)] and the specificity of CNB is similar to that of FNAC [98% (95% CI, 96%–99%, I 2 = 76.2%) versus 96% (95% CI, 94%–98%, I 2 = 39.0%)]. For subgroup analysis, the sensitivities of both tests are better for palpable lesions than that of non-palpable lesions. Sensitivity analysis shows the robustness of the primary analysis. Conclusion Our study suggests that both of FNAC and CNB have good clinical performance. In similar circumstances, the sensitivity of CNB is better than that of FNAC, while their specificities are similar. FNAC could be still considered the first choice to evaluate suspicious nonpalpable breast lesions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.052 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".