Model predictions for the WAXS signals of healthy and malignant breast duct biopsies
Bibliographic record
Abstract
A wide-angle x-ray scatter (WAXS) measurement could potentially be used to determine whether a biopsy of a breast duct is healthy or malignant. A ductal carcinoma in situ (DCIS) occurs when the epithelial cells lining the wall start to replicate and invade the duct interior. Since cells are composed mainly of water a WAXS signal of DCIS could contain a larger component due to water. A model approximates that a breast duct biopsy consists of connective tissue (c.t.) and cells. For a 2 mm diameter 3.81 mm thick healthy duct biopsy, the volumes in cubic mm are 11.56 c.t. and 0.41 cells whereas 6.64 c.t. and 5.33 cells for DCIS. The differential linear scattering coefficients (μs) for both types of biopsies were calculated using the sum vc.t.μsc.t. + vcellμscell where v denotes fractional volume. The cell was assumed to be composed of water, lipids (fat), and other atoms associated with RNA, DNA, proteins, and carbohydrates. The μscell was calculated using the sum 0.771μswater + 0.023μsfat + 0.206μsother. The μs of c.t., water, and fat were available from literature whereas the independent atomic model approximation was used to calculate values for μsother. A WAXS model provided predictions of the number of 6 degree scattered photons Ns for incident 50 kV beams on healthy and malignant ducts. The sum of Ns between 31.5 ≤ E ≤ 45 keV were 1402 and 1529 for respectively the healthy and malignant biopsies. Using Poisson statistics, two Gaussian distributions, and a descision threshold set at their intersection, the false positive and false negative probabilities were 4.7% and 5.0%. This work suggests that DCIS could potentially be diagnosed via energy dispersive WAXS measurements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".