Surgeon assessment of significant rectal polyps using white light endoscopy alone and in comparison to fluorescence-augmented AI lesion classification.

Autor: Hardy NP; UCD Centre for Precision Surgery, University College Dublin, Dublin, Ireland., Moynihan A; UCD Centre for Precision Surgery, University College Dublin, Dublin, Ireland., Dalli J; UCD Centre for Precision Surgery, University College Dublin, Dublin, Ireland., Epperlein JP; IBM Research Europe, Dublin, Ireland., McEntee PD; UCD Centre for Precision Surgery, University College Dublin, Dublin, Ireland., Boland PA; UCD Centre for Precision Surgery, University College Dublin, Dublin, Ireland., Neary PM; Department of Surgery, University Hospital Waterford, University College Cork, Cork, Ireland., Cahill RA; UCD Centre for Precision Surgery, University College Dublin, Dublin, Ireland. ronan.cahill@ucd.ie.; Department of Surgery, Mater Misericordiae University Hospital, 47 Eccles Street, Dublin 7, Ireland. ronan.cahill@ucd.ie.
Jazyk: angličtina
Zdroj: Langenbeck's archives of surgery [Langenbecks Arch Surg] 2024 Jun 01; Vol. 409 (1), pp. 170. Date of Electronic Publication: 2024 Jun 01.
DOI: 10.1007/s00423-024-03364-2
Abstrakt: Purpose: Perioperative decision making for large (> 2 cm) rectal polyps with ambiguous features is complex. The most common intraprocedural assessment is clinician judgement alone while radiological and endoscopic biopsy can provide periprocedural detail. Fluorescence-augmented machine learning (FA-ML) methods may optimise local treatment strategy.
Methods: Surgeons of varying grades, all performing colonoscopies independently, were asked to visually judge endoscopic videos of large benign and early-stage malignant (potentially suitable for local excision) rectal lesions on an interactive video platform (Mindstamp) with results compared with and between final pathology, radiology and a novel FA-ML classifier. Statistical analyses of data used Fleiss Multi-rater Kappa scoring, Spearman Coefficient and Frequency tables.
Results: Thirty-two surgeons judged 14 ambiguous polyp videos (7 benign, 7 malignant). In all cancers, initial endoscopic biopsy had yielded false-negative results. Five of each lesion type had had a pre-excision MRI with a 60% false-positive malignancy prediction in benign lesions and a 60% over-staging and 40% equivocal rate in cancers. Average clinical visual cancer judgement accuracy was 49% (with only 'fair' inter-rater agreement), many reporting uncertainty and higher reported decision confidence did not correspond to higher accuracy. This compared to 86% ML accuracy. Size was misjudged visually by a mean of 20% with polyp size underestimated in 4/6 and overestimated in 2/6. Subjective narratives regarding decision-making requested for 7/14 lesions revealed wide rationale variation between participants.
Conclusion: Current available clinical means of ambiguous rectal lesion assessment is suboptimal with wide inter-observer variation. Fluorescence based AI augmentation may advance this field via objective, explainable ML methods.
(© 2024. The Author(s).)
Databáze: MEDLINE
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