Number/quality of endoscopic biopsy samples in gastrointestinal cancers for biomarker testing: All that glitters is not gold
Bibliographic record
Abstract
BACKGROUND: Management of gastrointestinal (GI) cancers has shifted from conventional chemotherapy to biomarker-based precision oncology. Biomarker assessment requires adequate endoscopic biopsy tissue both in gastro-esophageal/gastric and colorectal carcinomas. AIMS: This study evaluated real-world endoscopic biopsy adequacy, focusing on tissue quality and suitability for biomarker analysis. METHODS: We retrospectively reviewed 819 endoscopic procedures (274 upper-GI and 545 lower-GI; time-window: January 2021-2024). Gastrointestinal pathologists reviewed 4,908 biopsies to assess diagnostic yield, number of invasive carcinoma-containing biopsies, and tumor cellularity. Biopsy adequacy was evaluated against European Society of Gastrointestinal Endoscopy (ESGE) recommendations and biomarker-specific cellularity thresholds. RESULTS: A histologic diagnosis of invasive carcinoma was established in 96 % of upper-GI and 84 % of lower-GI procedures (p<0.001). However, 41-43 % of procedures yielded fewer than six biopsies, which is below ESGE guidance. Importantly, only 66.7 % of upper-GI and 49.7 % of lower-GI biopsies contained invasive carcinoma, while the rest were composed of samples inadequate for biomarker testing (such as non-invasive lesions, mucin, necrosis, granulation tissue, and normal mucosa). Low neoplastic cellularity (<1000 tumor cells) was observed in 27 % of upper-GI and 5 % of lower-GI cases, while <20 % tumor cellularity was present in 41.7 % of colorectal biopsies. CONCLUSION: Optimizing sampling strategies and ensuring representative, high-cellularity specimens are essential to support precision oncology in GI cancers.
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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".