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DOI: 10.1055/a-2350-9631
Value of green sign and chicken skin aspects for detecting malignancy of colorectal neoplasia in a prospective characterization study
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Abstract
Background and study aims Accurate endoscopic characterization of colorectal lesions is essential for predicting histology but is difficult even for experts. Simple criteria could help endoscopists to detect and predict malignancy. The aim of this study was to evaluate the value of the green sign and chicken skin aspects in detection of malignant colorectal neoplasia.
Patients and methods We prospectively characterized and evaluated the histology of all consecutive colorectal lesions detected during screening or referred for endoscopic resection (Pro-CONECCT study). We evaluated the diagnostic accuracy of the green sign and chicken skin aspects for detection of superficial and deep invasive lesions.
Results 461 patients with 803 colorectal lesions were included. The green sign had a negative predictive value of 89.6% (95% confidence interval [CI] 87.1%–91.8%) and 98.1% (95% CI 96.7%-99.0%) for superficial and deep invasive lesions, respectively. In contrast to chicken skin, the green sign showed additional value for detection of both lesion types compared with the CONECCT classification and chicken skin (adjusted odds ratio [OR] for superficial lesions 5.9; 95% CI 3.4–10.2; P <0.001), adjusted OR for deep lesions 9.0; 95% CI 3.9–21.1; P <0.001).
Conclusions The green sign may be associated with malignant colorectal neoplasia. Targeting these areas before precise analysis of the lesion could be a way of improving detection of focal malignancies and prediction of the most severe histology.
Keywords
Endoscopy Lower GI Tract - Polyps / adenomas / ... - Colorectal cancer - Tissue diagnosis - Diagnosis and imaging (inc chromoendoscopy, NBI, iSCAN, FICE, CLE...)Publikationsverlauf
Eingereicht: 01. März 2024
Angenommen nach Revision: 13. Juni 2024
Accepted Manuscript online:
24. Juni 2024
Artikel online veröffentlicht:
25. Juli 2024
© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).
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