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  • Title: Optical Diagnosis of Sessile Serrated Polyps: Bottleneck for the Optical Diagnosis Paradigm?
    Author: Vleugels JLA, IJspeert JEG, Hazewinkel Y, van der Vlugt M, Fockens P, Koens L, Dekker E.
    Journal: J Clin Gastroenterol; 2017; 51(5):426-432. PubMed ID: 27775962.
    Abstract:
    BACKGROUND: Optical diagnosis of diminutive (1 to 5 mm) polyps could result in a more cost-effective colonoscopy practice. Previous optical diagnosis studies did not incorporate the differentiation of sessile serrated polyps (SSPs). This study aimed to evaluate the impact of optical diagnosis of diminutive SSPs on the overall performance of endoscopic polyp differentiation in daily colonoscopy practice. METHODS: Endoscopy data were prospectively collected between 2011 and 2014 in a colonoscopy center. Each endoscopist reported a real-time optical diagnosis (SSP, adenoma or hyperplastic polyp) for all lesions in a structured colonoscopy reporting system, using narrow band imaging at their discretion. Study outcomes were accuracy of optical diagnosis, surveillance interval agreement and negative predictive value for diminutive rectosigmoid neoplastic histology based on the optical diagnosis of diminutive polyps compared to histopathology. RESULTS: Of 2853 removed diminutive polyps, 202 (7.1%) were histologically proven SSPs. Optical diagnosis of diminutive SSPs was accurate in 24.4%. Diminutive SSPs determined 6.9% of postpolypectomy surveillance assignments. Inaccurate optical diagnosis of diminutive SSPs led to lower surveillance interval agreement (78.1% vs. 53.3%, P<0.01) and pooled negative predictive value per polyp (84.3% vs. 50.0%; P<0.01) in patients with diminutive SSPs when compared to patients without diminutive SSPs. Accurate endoscopic identification of diminutive SSPs improved from 0% in 2011 to 47% in 2014 (P=0.02). CONCLUSIONS: Endoscopic characterization of diminutive SSPs is difficult, impairing overall performance of optical diagnosis in patients with diminutive SSPs. Future optical diagnosis studies should use validated trainings and classification algorithms that include differentiation of SSPs.
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