Computer-aided detection of early Barrett's neoplasia using volumetric laser endomicroscopy.
OBJECTIVE
Volumetric laser endomicroscopy (VLE) is an advanced imaging system that provides a near-microscopic resolution scan of the esophageal wall layers up to 3-mm deep. VLE has the potential to improve detection of early neoplasia in Barrett's esophagus (BE). However, interpretation of VLE images is complex because of the large amount of data that need to be interpreted in real time. The aim of this study was to investigate the feasibility of a computer algorithm to identify early BE neoplasia on ex vivo VLE images.


METHODS
We used 60 VLE images from a database of high-quality ex vivo VLE-histology correlations, obtained from BE patients ± neoplasia (30 nondysplastic BE [NDBE] and 30 high-grade dysplasia/early adenocarcinoma images). VLE features from a recently developed clinical VLE prediction score for BE neoplasia served as input for the algorithm: (1) higher VLE surface than subsurface signal and (2) lack of layering. With this input, novel clinically inspired algorithm features were developed, based on signal intensity statistics and grayscale correlations. For comparison, generic image analysis methods were examined for their performance to detect neoplasia. For classification of the images in the NDBE or neoplastic group, several machine learning methods were evaluated. Leave-1-out cross-validation was used for algorithm validation.


RESULTS
Three novel clinically inspired algorithm features were developed. The feature "layering and signal decay statistics" showed the optimal performance compared with the other clinically features ("layering" and "signal intensity distribution") and generic image analyses methods, with an area under the receiver operating characteristic curve (AUC) of .95. Corresponding sensitivity and specificity were 90% and 93%, respectively. In addition, the algorithm showed a better performance than the clinical VLE prediction score (AUC .81).


CONCLUSIONS
This is the first study in which a computer algorithm for BE neoplasia was developed based on VLE images with direct histologic correlates. The algorithm showed good performance to detect BE neoplasia in ex vivo VLE images compared with the performance of a recently developed clinical VLE prediction score. This study suggests that an automatic detection algorithm has the potential to assist endoscopists in detecting early neoplasia on VLE. Future studies on in vivo VLE scans are needed to further validate the algorithm.
Department of Gastroenterology and Hepatology, Academic Medical Center, Amsterdam, the Netherlands.
Anne-Fré
AF
Swager
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
Fons
F
van der Sommen
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
Sander R
SR
Klomp
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
Sveta
S
Zinger
Department of Pathology, Academic Medical Center, Amsterdam, the Netherlands.
Sybren L
SL
Meijer
Department of Gastroenterology and Hepatology, Catharina Hospital, Eindhoven, the Netherlands.
Erik J
EJ
Schoon
Department of Gastroenterology and Hepatology, Academic Medical Center, Amsterdam, the Netherlands.
Jacques J G H M
JJGHM
Bergman
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
Peter H
PH
de With
Department of Gastroenterology and Hepatology, Catharina Hospital, Eindhoven, the Netherlands.
Wouter L
WL
Curvers
D000230:Adenocarcinoma / Q000175:diagnosis / Q000473:pathology*; D000368:Aged; D000465:Algorithms; D001471:Barrett Esophagus / Q000175:diagnosis / Q000473:pathology*; D016022:Case-Control Studies; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004938:Esophageal Neoplasms / Q000175:diagnosis / Q000473:pathology*; D004945:Esophagoscopy / Q000379:methods*; D004947:Esophagus / Q000473:pathology*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D000069550:Machine Learning; D008297:Male; D018613:Microscopy, Confocal / Q000379:methods*; D008875:Middle Aged; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine