Quantifying masking in clinical mammograms via local detectability of simulated lesions
Bibliographic record
Abstract
PURPOSE: High mammographic density is known to be associated with decreased sensitivity of mammography. Recent changes in the BI-RADS density assessment address the effect of masking by densities, but the BI-RADS assessment remains qualitative and achieves only moderate agreement between radiologists. An automated, quantitative algorithm that estimates the likelihood of masking of simulated masses in a mammogram by dense tissue has been developed. The algorithm considers both the effects of loss of contrast due to density and the distracting texture or appearance of dense tissue. METHODS: A local detectability (dL) map is created by tessellating the mammograms into overlapping regions of interest (ROIs), for which the detectability by a non-prewhitening observer is computed using local estimates of the noise power spectrum and volumetric breast density (VBD). The dL calculation was validated in a 4-alternative forced-choice observer study on the ROIs of 150 craniocaudal digital mammograms. The dL metric was compared against the inverse threshold contrast, (ΔμT)(-1) from the observer study, the anatomic noise parameter β, the radiologist's BI-RADS density category, and a validated measure of VBD (Cumulus). RESULTS: The mean dL had a high correlation of r = 0.915 and r = 0.699 with (ΔμT)(-1) in the computerized and human observer study, respectively. In comparison, the local VBD estimate had a low correlation of 0.538 with (ΔμT)(-1). The mean dL had a correlation of 0.663, 0.835, and 0.696 with BI-RADS density, β, and Cumulus VBD, respectively. CONCLUSIONS: The proposed dL metric may be useful in characterizing the potential for lesion masking by dense tissue. Because it uses information about the anatomic noise or tissue appearance, it is more closely linked to lesion detectability than VBD metrics.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".