The use of X‐ray interaction data to differentiate malignant from normal breast tissue at surgical margins and biopsy analysis
Bibliographic record
Abstract
X‐ray interaction data, including measuring bio‐metal levels and scattering characteristics, are being shown to be a possible discriminating variable in the classification of human tissues. However, a major concern when using X‐ray interaction data in breast cancer material is that the samples are rarely 100% tumour because of the invasive nature of the disease. The work reported here includes a methodology to help overcome this limitation as the experimental protocol includes mapping the data to histological analysis of the measured samples. This work has shown how important it is to relate the measured X‐ray parameters to the histology of the samples, particularly the clinical information that describes the percentage of tumour within each sample. Levels of K, Ca, Zn, Fe, Cu, Br and Rb were evaluated using X‐ray fluorescence and compared between tumour breast tissue and normal surrounding breast tissue. The coherent scattering properties of each sample were also examined using an angular dispersive X‐ray diffraction technique. Multivariate modelling using soft independent modelling of class analogy was used to classify samples kept out of the modelling procedure. A significant increase ( p < 0.01) in the levels of Rb, Zn and K was found in the tumour samples. The levels of these elements show a correlation with the percentage of tumour reported to be present in a given sample. The results of classifying unknown tissue samples are presented using two‐class and three‐class models that help to reveal the importance of sample histology in studies involving breast cancers. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".