Subsurface probing of calcifications with spatially offset Raman spectroscopy (SORS): future possibilities for the diagnosis of breast cancer
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Bibliographic record
Abstract
Breast calcifications are often the only mammographic features indicating the presence of a cancerous lesion. Calcium oxalate (type I) may be found in and around benign lesions, however calcium hydroxyapatite (type II) is usually found within proliferative lesions, which can include both benign and malignant pathologies. However, the composition of type II calcifications has been demonstrated to vary between benign and malignant proliferative lesions, and could be an indicator for the possible disease state. Raman spectroscopy has previously been demonstrated as a powerful tool for non-destructive analysis of tissues, utilising laser light to probe chemical composition. Raman spectroscopy is traditionally a surface technique. However, we have recently developed methods that permit its application for obtaining sample composition to clinically relevant depths of many mm. We report the first demonstration of spatially offset Raman spectroscopy (SORS) for potential in vivo breast analysis. This study evaluates the possibility of utilising SORS for measuring calcification composition through varying thicknesses of tissues (2 to 10 mm), which is about one to two orders of magnitude deeper than has been possible with conventional Raman approaches. SORS can be used to distinguish non-invasively between calcification types I and II (and carbonate substitution of phosphate in calcium hydroxyapatite) within tissue of up to 10 mm deep. This result secures the first step in taking this technique forward for clinical applications seeking to use Raman spectroscopy as an adjunct to mammography for early diagnosis of breast cancer, by utilising both soft tissue and calcification signals. Non-invasive elucidation of calcification composition, and hence type, associated with benign or malignant lesions, could eliminate the requirement for biopsy in many patients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it