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||Comparison of Colposcopic Impression Based on Live Colposcopy and Evaluation of Static Digital Images.
||Liu AH, Gold MA, Schiffman M, Smith KM, Zuna RE, Dunn ST, Gage JC, Walker JL, Wentzensen N
||J Low Genit Tract Dis
||OBJECTIVE: The aim of the study was to evaluate the agreement and compare diagnostic accuracy of colposcopic impressions from live colposcopy versus evaluation of static digital images. MATERIALS AND METHODS: Live impressions and corresponding static images obtained during colposcopy of 690 women were independently compared. Diagnostic accuracy was calculated for colposcopic impressions from both methods, varying hypothetical thresholds for colposcopically directed cervical biopsies (acetowhitening or worse, low grade or worse, high grade or worse). Stratified analyses investigated the impact of referral cytology, human papillomavirus 16 infection, and age on colposcopic impression. RESULTS: Overall agreement between live and static colposcopic visualization was 43.0% (κ = 0.20; 95% CI = 0.14-0.26) over normal, acetowhitening, low-grade, and high-grade impressions. Classification of acetowhitening or worse impressions showed the highest agreement (92.2%; κ = 0.39; 95% CI = 0.21-0.57); both methods achieved more than 95% sensitivity for CIN 2+. Agreement between live and static colposcopic visualization was 69.3% for rating low-grade or worse impressions (κ = 0.23; 95% CI = 0.14-0.33) and 71% when rating high-grade impressions (κ = 0.33; 95% CI = 0.24-0.42). Live colposcopic impressions were more likely to be rated low grade or worse (p < .01; odds ratio = 3.5; 95% CI = 2.4-5.0), yielding higher sensitivity for CIN 2+ at this threshold than static image assessment (95.4% vs 79.8%, p < .01). Overall, colposcopic impressions were more likely rated high grade on live assessment among women referred with high-grade cytology (odds ratio = 3.3; 95% CI = 1.8-6.4), significantly improving the sensitivity for CIN 2+ (66.3% vs 48.5%, p < .01). CONCLUSIONS: Colposcopic impressions of acetowhitening or worse are highly sensitive for identifying cervical precancers and reproducible on static image-based pattern recognition.