Improving Postpolypectomy Surveillance Decisions Using an Automated Analysis of Colonoscopy and Histopathology...

Postpolypectomy surveillance intervals derived from colonoscopy findings and histopathology should be guideline-based, yet these are frequently not adhered to, contributing to under- and oversurveillance. This motivates a more structured and automated approach to improve adherence.

We developed and evaluated an automated, implementable, guideline-aligned decision support tool that extracts colorectal polyp features from free-text colonoscopy and histopathology reports and produces auditable surveillance recommendations.

We extracted paired colonoscopy and histopathology reports from a National Health Service (NHS) trust and designed a 4-stage prompting pipeline using open-weight large language models to separate multispecimen histopathology reports into individual containers ("pots"), align endoscopic findings with specimens, and normalize each pot into a structured schema. The surveillance intervals were then computed by a deterministic rule engine encoding guideline rules. We evaluated extraction performance on 100 procedures (215 pots), assessed surveillance recommendation performance on a same-site cohort (n=100), and performed external validation on a cohort from a second trust (n=100). A separate retrospective audit compared automated recommendations and observed routine scheduling against a clinician-derived reference based on full electronic health record review in 127 patients.

Key extraction fields showed end-to-end accuracy (specimen type=0.995; location=0.981; diagnosis=0.981; excision completeness=0.949), with mean report-level pot comprehensiveness of 0.968 (SD 0.112). Overall surveillance recommendation accuracy was 0.96 (95% CI 0.90-0.98) at Guy's and St Thomas' NHS Foundation Trust (GSTT) and 0.93 (95% CI 0.86-0.97) at Sandwell and West Birmingham NHS Trust (SWB) and showed strong class-level performance (no follow-up F₁-score of 0.98 at GSTT and 0.97 at SWB; site check F₁-score of 0.93 at GSTT and 0.91 at SWB; 3-year follow-up F₁-score of 0.86 at GSTT and 0.80 at SWB; cancer alert F₁-score of 1.00 at GSTT and 1.00 at SWB). The comparison to current clinical practice showed that the tool would improve guideline adherence primarily by reducing potential undersurveillance.

This hybrid approach using open-weight large language models for structured extraction and guideline-encoded rules for decision-making can generate accurate, lightweight, and auditable postpolypectomy surveillance recommendations. Such a tool has the potential to improve safety netting and guideline adherence in NHS colonoscopy workflows.