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Record W4408895066 · doi:10.1055/s-0045-1805464

Accuracy of diminutive colorectal polyp diagnosis when using pathology alone, computer aided characterisation alone or a combined approach

2025· article· en· W4408895066 on OpenAlexaff
Roupen Djinbachian, Heiko Pohl, DK Rex, Alan Barkun, C Hassan, Daniel von Renteln

Bibliographic record

VenueEndoscopy · 2025
Typearticle
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsMcGill UniversityCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineDiminutiveColorectal PolypRadiologyPathologyGeneral surgeryInternal medicineColorectal cancerColonoscopyCancer

Abstract

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Aims Multiple steps between polyp detection and obtaining a final pathology report can result in an incorrect diagnosis. These steps include resection and retrieval, during which specimens may fracture or be lost, followed by processing (embedding and sectioning) in the pathology laboratory, and final analysis, all of which can lead to errors and misdiagnoses. These steps have previously not been taken into account when estimating the true accuracy of pathology within an “intention-to-diagnose” framework. Intra-colonoscopy computer-aided characterization (CADx) has emerged as an alternative strategy and has the benefit of not requiring specimen retrieval and handling. We were interested in pragmatically comparing Pathology-based diagnosis to CADx from an “intention-to-diagnose” framework. Methods We conducted a post-hoc analysis of a prospective clinical study. Primary outcome was accuracy in polyp diagnosis when using CADx or pathology alone, taking into account the impact of non-resection, non-retrieval, and misdiagnosis when using pathology. Secondary outcome was the accuracy of a combined strategy where CADx is used for arbitration of cases that could not be diagnosed in pathology. For the pathology strategy, unresected polyps, unretrieved polyps, polyps not received in pathology, and diagnoses of “mucosal fold” were considered incorrect. In cases of “mucosal fold” diagnosis, 3 expert endoscopists (HP, DKR, CH) evaluated videos of these lesions to confirm whether a polyp was truly present. For the CADx strategy, accuracy was compared to pathology as gold standard when pathology was available and extrapolated to the polyps with no pathology diagnosis. Results A total of 467 diminutive polyps in 269 consecutive patients were included. Pathology, when taking into account cases where diagnosis could not be obtained (not resected, not retrieved, not received by pathology lab) and cases with inaccurate diagnosis (mucosal fold with 3 expert endoscopist video confirmation of the presence of polyp), had an estimated accuracy of 77.1% (95%CI 73.0-80.8). We found that CADx resulted in an accurate diagnosis in 74.7% (95%CI 69.7-79.3) when used alone. Inaccurate diagnoses in the pathology strategy were: 1.7% not resected due to losing sight of the polyp during endoscopy or other factors, resulting in no available histologic information; 6.4% retrieved despite attempts at retrieval; 1.1% were retrieved but not received by the pathology unit; and 13.7% were diagnosed as normal mucosa/mucosal folds. When using a combined approach of pathology with CADx for arbitration, the accuracy increased to 94.2%. Conclusions Our findings indicate that using pathology alone for diagnosis of colorectal polyps leads to unavailable or inaccurate diagnosis in 22.9% of cases with an overall accuracy of only 77%. Comparatively, CADx alone showed similar accuracy as diagnosis can be performed in all detected polyps. A combined approach allowed for the highest diagnostic accuracy. Publication History Article published online: 27 March 2025 © 2025. European Society of Gastrointestinal Endoscopy. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.030
GPT teacher head0.303
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
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