TY - JOUR
T1 - Computer-aided prediction of polyp histology on white light colonoscopy using surface pattern analysis
AU - Sánchez-Montes, Cristina
AU - Sánchez, Francisco Javier
AU - Bernal, Jorge
AU - Córdova, Henry
AU - López-Cerón, María
AU - Cuatrecasas, Miriam
AU - Rodríguez De Miguel, Cristina
AU - García-Rodríguez, Ana
AU - Garcés-Durán, Rodrigo
AU - Pellisé, María
AU - Llach, Josep
AU - Fernández-Esparrach, Glòria
PY - 2019/1/1
Y1 - 2019/1/1
N2 - © 2019 Georg Thieme Verlag KG Stuttgart New York. Background This study aimed to evaluate a new computational histology prediction system based on colorectal polyp textural surface patterns using high definition white light images. Methods Textural elements (textons) were characterized according to their contrast with respect to the surface, shape, and number of bifurcations, assuming that dysplastic polyps are associated with highly contrasted, large tubular patterns with some degree of bifurcation. Computer-aided diagnosis (CAD) was compared with pathological diagnosis and the diagnosis made by endoscopists using Kudo and Narrow-Band Imaging International Colorectal Endoscopic classifications. Results Images of 225 polyps were evaluated (142 dysplastic and 83 nondysplastic). The CAD system correctly classified 205 polyps (91.1 %): 131/142 dysplastic (92.3 %) and 74/83 (89.2 %) nondysplastic. For the subgroup of 100 diminutive polyps (≤ 5 mm), CAD correctly classified 87 polyps (87.0 %): 43/50 (86.0 %) dysplastic and 44/50 (88.0 %) nondysplastic. There were no statistically significant differences in polyp histology prediction between the CAD system and endoscopist assessment. Conclusion A computer vision system based on the characterization of the polyp surface in white light accurately predicted colorectal polyp histology.
AB - © 2019 Georg Thieme Verlag KG Stuttgart New York. Background This study aimed to evaluate a new computational histology prediction system based on colorectal polyp textural surface patterns using high definition white light images. Methods Textural elements (textons) were characterized according to their contrast with respect to the surface, shape, and number of bifurcations, assuming that dysplastic polyps are associated with highly contrasted, large tubular patterns with some degree of bifurcation. Computer-aided diagnosis (CAD) was compared with pathological diagnosis and the diagnosis made by endoscopists using Kudo and Narrow-Band Imaging International Colorectal Endoscopic classifications. Results Images of 225 polyps were evaluated (142 dysplastic and 83 nondysplastic). The CAD system correctly classified 205 polyps (91.1 %): 131/142 dysplastic (92.3 %) and 74/83 (89.2 %) nondysplastic. For the subgroup of 100 diminutive polyps (≤ 5 mm), CAD correctly classified 87 polyps (87.0 %): 43/50 (86.0 %) dysplastic and 44/50 (88.0 %) nondysplastic. There were no statistically significant differences in polyp histology prediction between the CAD system and endoscopist assessment. Conclusion A computer vision system based on the characterization of the polyp surface in white light accurately predicted colorectal polyp histology.
U2 - 10.1055/a-0732-5250
DO - 10.1055/a-0732-5250
M3 - Article
C2 - 30360010
SN - 0013-726X
VL - 51
SP - 261
EP - 265
JO - Endoscopy
JF - Endoscopy
ER -