The characterization of breast anatomical metrics using dedicated breast CT
Bibliographic record
Abstract
PURPOSE: Accurate anatomical characterization of the breast is useful in breast phantom development and computer modeling of breast imaging technologies. Capitalizing on the three-dimensional capabilities of dedicated breast CT (bCT), a number of parameters which describe breast shape and fibroglandular distribution are defined. METHODS: Among 219 bCT data sets, the effective diameter and length of the pendant breast as well as the breast volume were measured and characterized for each bra cup size. The volume glandular fraction (VGF) was determined as a function of patient age, BIRADS density, bra cup size, and breast diameter. The glandular fraction was examined in coronal and sagittal planes of the breast, and the radial distribution of breast glandular fraction within a coronal bCT image was examined for three breast regions. The areal glandular fraction (AGF) was estimated from two-dimensional projections of the breast (simulated by projecting bCT data sets) and was compared to the corresponding VGF. RESULTS: The effective breast diameter and length increase with increasing bra cup size. The mean breast diameters (+/- standard error) of bra cup sizes A/AA, B, C, and D/DD were 11.1 +/- 0.5, 11.4 +/- 0.3, 13.0 +/- 0.2, and 13.7 +/- 0.2 cm, respectively. VGF was lower among older women and those with larger breast diameter and larger bra cup size. VGF increased as a function of the reported BIRADS density. AGF increased with VGF. Fibroglandular tissue was distributed primarily in the central portion of the breast. CONCLUSIONS: Breast metrics were examined and a number of parameters were defined which may be useful for breast modeling. The reported data may provide researchers with useful information for characterizing the breast for various imaging or dosimetry tasks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".