IR Spectral Imaging for Histopathological Characterization of Xenografted Human Colon Carcinomas
Bibliographic record
Abstract
This study aims to develop IR imaging of tumor tissues for generating an automated IR-based histology. Formalin-fixed paraffin-embedded xenografts of human colon carcinomas were analyzed. Chemometric and statistical multivariate treatments of spectral data permitted to probe the intrinsic chemical composition of tissues, directly from paraffinized sections without previous dewaxing. Reconstructed color-coded spectral images revealed a marked tumor heterogeneity. We identified three spectral clusters associated to tumoral tissues, whereas HE staining revealed only a single structure. Nine other clusters were assigned to either necrotic or host tissues. This spectral histology proved to be consistent over multiple passages of the same xenografted tumor confirming that intratumoral heterogeneity was maintained over time. In addition, developing an innovative image analysis, based on the quantification of neighboring pixels, permitted the identification of two main sequences of spectral clusters related to the tissue spatial organization. Molecular attribution of the spectral differences between the tumor clusters revealed differences of transcriptional activity within these tumor tissue subtypes. In conclusion, IR spectral imaging proves to be highly effective both for reproducible tissue subtype recognition and for tumor heterogeneity characterization. This may represent an attractive tool for routine high throughput diagnostic challenges, independent from visual morphology.
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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 | 0.000 | 0.000 |
| 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".