Computer-assisted image analysis of the tumor microenvironment on an oral tongue squamous cell carcinoma tissue microarray
Bibliographic record
Abstract
Oral tongue squamous cell carcinoma (OTSCC) displays variable levels of immune cells within the tumor microenvironment. The quantity and localization of tumor infiltrating lymphocytes (TILs), specific functional TIL subsets (e.g., CD8+), and biomarker-expressing cells (e.g., PD-L1+) may have prognostic and predictive value. The purpose of this study was to evaluate the robustness and utility of computer-assisted image analysis tools to quantify and localize immunohistochemistry-based biomarkers within the tumor microenvironment on a tissue microarray (TMA). We stained a 91-patient OTSCC TMA with antibodies targeting CD3, CD4, CD8, FOXP3, IDO, and PD-L1. Cell populations were segmented into epithelial (tumor) or stromal compartments according to a mask derived from a pan-cytokeratin stain. Definiens Tissue Studio was used to enumerate marker-positive cells or to quantify the staining intensity. Automated methods were validated against manual tissue segmentation, cell count, and stain intensity quantification. Univariate associations of cell count and stain intensity with smoking status, stage, overall survival (OS), and disease-free survival (DFS) were determined. Our results revealed that the accuracy of automated tissue segmentation was dependent on the distance of the tissue section from the cytokeratin mask and the proportion of the tissue containing tumor vs. stroma. Automated and manual cell counts and stain intensities were highly correlated (Pearson coefficient range: 0.46-0.90; p < 0.001). Within this OTSCC cohort, smokers had significantly stronger PD-L1 stain intensity and higher numbers of CD3+, CD4+ and FOXP3+ TILs. In the subset of patients who had received adjuvant radiotherapy, a higher number of CD8+ TILs was associated with inferior OS and DFS. Taken together, this proof-of-principle study demonstrates the robustness and utility of computer-assisted image analysis for high-throughput assessment of multiple IHC markers on TMAs, with potential implications for studies on prognostic and predictive biomarkers.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".