The Impact of Slice Thickness on Diagnostic Accuracy in Digital Breast Tomosynthesis
Bibliographic record
Abstract
Purpose: To evaluate the effect of slice thickness on diagnostic accuracy in Digital Breast Tomosynthesis (DBT). Method: Two readers retrospectively interpreted 150 DBT (125 normal and 25 pathology-proven cancer) cases scanned between October 2017–November 2020. The DBT studies were randomised and reviewed independently by the two readers. DBT studies were reviewed using a standard protocol (1 mm slices, no overlap and synthetic 2D-mammography (SM)) and an experimental protocol (10 mm slabs, 5 mm overlap and SM). Any abnormality and BIRADS scores were recorded by each reader. Sensitivity, specificity, interobserver and intraobserver agreement were calculated (Cohen’s Kappa κ). For diagnostic accuracy, the reference standard was histopathology or a normal mammogram at 2 years. Results: The sensitivity and specificity for reader 1 and 2 for cancer detection was reader 1 (97% and 79% for the standard protocol, 97% and 76% for the experimental protocol) and reader 2 (97% and 74% for both protocols). Reader 1 had 97.6% intraobserver agreement (κ .95) and reader 2 had 96.4% intraobserver agreement (κ .92) when assessing the standard and experimental protocols. There was 90.5% agreement between the readers for the standard protocol (κ .80). There was 90.9% agreement between the readers for the experimental protocol (κ .81). Of the 25 DBT studies with pathology-proven cancer, one cancer was missed by both readers using both protocols. Conclusion: The diagnostic accuracy was similar between the standard and experimental DBT protocols, demonstrating excellent interobserver and intraobserver agreement. This suggests 10-mm thick slabs can potentially replace 1-mm thin slices in the interpretation of DBT.
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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 | 0.020 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".