Window Settings to Improve Detection of Enhancing Breast Findings on CT
Bibliographic record
Abstract
Breast findings can be detected incidentally on contrast-enhanced CT (CE-CT). In some instances, such as findings at the periphery of the breast or in mammographically dense breasts, CE-CT may provide additional information compared to standard mammography because of the CE-CT’s larger field of view and because contrast is used. Both benign and malignant enhancing breast findings may be detected by CE-CT. The CE-CT imaging characteristics of invasive ductal carcinoma are described as a dense, spiculated mass with marked early and/or peripheral enhancement. Invasive lobular carcinomas may manifest as a mass or as an asymmetric soft-tissue density. Because they are not symptomatic, breast cancers detected incidentally by CE-CT may be relatively small and, therefore, prone to being overlooked. Satisfaction of search when reviewing the chest CE-CT for lung or cardiac abnormalities can also lead to missing breast lesions. CT uses a wide range of window width (WW) and window level (WL), which can be adjusted (windowed) to alter the image contrast and brightness, respectively. As the WW decreases, the contrast increases because structures that are close in density will have different shades of grayscale.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".