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 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 | 0.024 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".