Advances in the histopathological characterization of breast tissue using combined X‐ray fluorescence and X‐ray diffraction data in a multivariate analysis approach
Bibliographic record
Abstract
Previous studies have shown that the combination of X‐ray fluorescence and X‐ray diffraction data can be used as a histopathological characterization tool for breast tissue. Recent advances in energy‐dispersive X‐ray fluorescence techniques have allowed for benchtop systems to produce useful results in a reasonable time frame, allowing for clinical implementation to be realized. Using a polarized energy‐dispersive X‐ray fluorescence and energy‐dispersive X‐ray diffraction system optimized for measuring soft tissues, 38 breast tissue samples (19 normal and 19 diseased) were interrogated. The measured elemental concentrations and adipose and fibrous tissue contents were used in a principal component analysis study to determine the variables that produced the most differentiation between the normal and diseased tissues. For each sample, a soft independent modeling of class analogy technique was utilized to create classification models using the K, Fe, and Zn concentration and adipose and fibrous tissue content of all other breast samples. The class model produced from both X‐ray fluorescence and X‐ray diffraction data correctly classified 31 of 38 samples with no false positives or false negatives, showing improvement from solely X‐ray fluorescence models or X‐ray diffraction models alone, and demonstrates the usefulness of such a technique.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".