Depth-Sensitive Raman Spectroscopy of Intact Formalin-Fixed and Paraffin-Embedded Tissue Blocks for Objective Diagnosis of Cancer- An Exploratory Study
Bibliographic record
Abstract
Histopathology, the current “gold standard is prone to human errors as it depends on expert interpretation of the microscopically derived cellular and sub-cellular information for tissue diagnosis. Further, this light microscope based approach requires preparation of appropriately stained specimens of micro-thin tissue sections from the formalin-fixed and paraffin-embedded (FFPE) blocks of tissue samples. We report a method that provides quantitative feedback about tissue diagnosis by measuring depth-sensitive Raman spectra from the intact FFPE tissue blocks without requiring preparation of any thin tissue sections or any other processing. The FFPE blocks of pathologically certified cancerous and normal breast tissues were used for validating the approach. The measured depth-sensitive Raman spectra were mathematically de-paraffinized for retrieving the characteristic tissue Raman signatures using scaled-subtraction. A multivariate analysis of the scaled-subtracted, depth-sensitive Raman spectra employing a probability-based diagnostic algorithm developed using the framework of sparse multinomial logistic regression (SMLR) provided a sensitivity and specificity of up to 100% towards cancer based on leave-one-block-out cross validation. The results of this exploratory study suggest that depth-sensitive Raman spectroscopy along with a multivariate statistical algorithm can provide a valuable alternate diagnostic modality in clinical pathology setting for discriminating cancerous from normal FFPE tissue blocks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".