Diagnostic Value of Hook Wire Localization Technique for Non-Palpable Breast Lesions
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to investigate the validity of hook wire localization biopsy for non-palpable breast lesions which were detected by ultrasonography (USG) or mammography (MMG). METHODS: In this retrospective study, USG or MMG-guided hook wire localization technique was performed on 83 patients who had non-palpable breast lesions. Then histopathological examination was performed on surgically removed specimens. All patients' mammograms or ultrasonograms were categorized using Breast Imaging-Reporting and Data System (BI-RADS) classification. RESULTS: Radiologically, 27 (32.53%) patients were classified as BI-RADS 3, 49 (59.04%) BI-RADS 4, one (1.2%) BIRADS 5 and six (7.23%) BI-RADS 0. Histopathological results were benign in 68 (81.9%) and malignant in 15 (18.1%) patients. Twenty-seven patients were classified as BI-RADS 3 and definitive diagnoses for all were benign. Besides, 49 patients were classified as BI-RADS 4 and histopathologically 14 of them were reported as malignant, and 35 as benign. Sensitivity of MMG was 93% and specificity was 55%. For USG, the sensitivity was 100% and the specificity was 73%. CONCLUSION: In early diagnosis of breast cancer, the validity of the imaging-guided hook wire localization biopsy of non-palpable breast lesions has been proved. The cooperation of surgeon, radiologist and pathologist increases the successfull results of hook wire localization technique.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".