Accuracy of diminutive colorectal polyp diagnosis when using pathology alone, computer aided characterisation alone or a combined approach
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».