Early Lesions Seen in Colonoscopic Surveillance Biopsies from Regions where Colorectal Carcinoma Developed in Patients with Inflammatory Bowel Diseases are Often Not Overtly Dysplastic
Bibliographic record
Abstract
ABSTRACT BACKGROUND & AIMS Screening for precancerous lesions by colonoscopic surveillance in patients with longstanding inflammatory bowel disease (IBD) has been a standard practice. However, failure of detecting precursors and preventing colorectal carcinoma (CRC) development is still common. This study aims to assess whether some lesions shown in the surveillance biopsies eluded our attention due to atypical histopathology. METHODS 91 patients (M 67/F 24, UC 59/CD 32) with surgically resected IBD-CRC were retrieved from 2 institutions in North America over 20 years. 40 patients with ≥1 colonoscopies performed >1 year prior to cancer diagnosis were regarded as “surveillanced”. Pathology records of CRC resections and all prior colonoscopic biopsies were reviewed. RESULTS 87.5% of these patients had ≥1 lesion detected in prior biopsies. Index lesion , the first lesion identified in all the biopsies, was detected in 65% of patients and in 86.8% of the regions where carcinoma developed later, in which 38.2% were conventional low-grade dysplasia (LGD), 29.4% indefinite for dysplasia (IND), 29.4% inflammatory polyp (IP), and 2.9% serrated lesions (SL). CONCLUSION In patients who had surveillance, certain lesions were detected by endoscopic biopsies in >80% of patients. >60% of index lesions were not overtly dysplastic, including IND, IP and SL, which may in part represent colitis-associated non-conventional dysplastic lesions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".