Model of malignant breast biopsies and predictions of their WAXS energy integrated signals
Bibliographic record
Abstract
A numerical study of the wide angle x-ray scatter (WAXS) energy integrated signals (EISs) from breast biopsies was conducted. A benign biopsy was chosen as fibroglandular (fib) tissue whereas biopsies with cancer were approximated as consisting of fib tissue and a cluster of epithelial cells. The grouping of cells represented the malignant portion. The EISs due to scatter were computed for biopsies of thickness dbio = 2 mm, 5 mm, 10 mm and 20 mm. For the malignant biopsies, the fractional volumes of cells (νcell) studied ranged from 0.01 to 0.1. Incident 2 mm diam beams of 30 kV, 50 kV, 80 kV, and 140 kV were considered. The tube current and exposure time were 3 mA and 1 minute, respectively. The WAXS signals were computed by adding the signals from annular detectors subtending scattering angles θ = 2°, 3°, , 23° and solid angles Ωθ = 2.0 x π x [cos(θ - ∆θ/2) - cos(θ + ∆θ/2)] x cos θ where ∆θ = 1°. Let the EIS due to a malignant biopsy be EISms and that of a benign one, EISbs. With these signals, values of SNR were computed. The 30 kV beam provided SNRs < 5 for the lowest entrance exposure, X = 0.0015 C/kg. For biopsies with dbio = 2, 5, 10, 20 mm and νcell = 0.1, the SNRs were 28.0, 38.2, 43.0, and 40.0. The findings suggest that there is potential to use WAXS EISs to diagnose malignancy in breast biopsies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".