Preoperative Staging in Women with Known Breast Cancer: Comparison between Digital Breast Tomosynthesis (DBT) and Magnetic Resonance Imaging (MRI)
Bibliographic record
Abstract
Objectives To prospectively evaluate the accuracy in tumor extent and size assessment of Digital Breast Tomosynthesis (DBT) and Magnetic Resonance Imaging (MRI) in women with known breast cancer, with pathological size as the gold standard. Methods From May 2014 to April 2016, 50 patients with known breast cancer were enrolled in our prospective study. All patients underwent MRI on a 3T magnet and DBT projections. Two radiologists, with 15 and 7 years of experience in breast imaging respectively, evaluated in consensus each imaging set unaware of the final histological examination. MR and DBT sensitivity, PPV and accuracy were calculated, using histology as the gold standard. McNemar test was used to compare MR and DBT sensitivity. Correlation and regression analyses were used to evaluate MRI vs Histology, DBT vs Histology and MRI vs DBT lesions tumor size agreement to histological results. Results On histological examination 70 lesions were detected. MRI showed 100% sensitivity, 96% PPV and 96% accuracy; DBT sensitivity was 81%, PPV 92% and accuracy 77%. McNemar test p-value was 0.0003. Lesions size Pearson correlation coefficient was 0.97 for MRI vs Histology, 0.92 for DBT vs Histology, (p-value<0.0001). MRI vs DBT regression coefficient was 0.83. Conclusions MRI confirmed to be the most accurate imaging technique in preoperative staging of breast cancer. However, DBT showed very good accuracy, sensitivity and tumor size assessment and could be a valid tool for preoperative staging when MRI is contraindicated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".