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Comput Med Imaging Graph. 2018 Jul;67:9-20. doi: 10.1016/j.compmedimag.2018.02.007. Epub 2018 Apr 13.

Predictive features for early cancer detection in Barrett's esophagus using Volumetric Laser Endomicroscopy.

Author information

1
Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands; Department of Gastroenterology, Academic Medical Center, Postbus 22660, 1100 DD Amsterdam, The Netherlands. Electronic address: F.v.d.Sommen@tue.nl.
2
Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands. Electronic address: S.R.Klomp@student.tue.nl.
3
Department of Gastroenterology, Academic Medical Center, Postbus 22660, 1100 DD Amsterdam, The Netherlands. Electronic address: A.Swager@amc.uva.nl.
4
Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands. Electronic address: S.Zinger@tue.nl.
5
Department of Gastroenterology, Academic Medical Center, Postbus 22660, 1100 DD Amsterdam, The Netherlands; Department of Gastroenterology and Hepathology, Catharina Hospital, P.O. Box 1350, 5602ZA Eindhoven, The Netherlands. Electronic address: Wouter.Curvers@catharinaziekenhuis.nl.
6
Department of Gastroenterology, Academic Medical Center, Postbus 22660, 1100 DD Amsterdam, The Netherlands. Electronic address: support@elsevier.com.
7
Department of Gastroenterology and Hepathology, Catharina Hospital, P.O. Box 1350, 5602ZA Eindhoven, The Netherlands. Electronic address: Erik.Schoon@catharinaziekenhuis.nl.
8
Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands. Electronic address: P.H.N.de.With@tue.nl.

Abstract

The incidence of Barrett cancer is increasing rapidly and current screening protocols often miss the disease at an early, treatable stage. Volumetric Laser Endomicroscopy (VLE) is a promising new tool for finding this type of cancer early, capturing a full circumferential scan of Barrett's Esophagus (BE), up to 3-mm depth. However, the interpretation of these VLE scans can be complicated, due to the large amount of cross-sectional images and the subtle grayscale variations. Therefore, algorithms for automated analysis of VLE data can offer a valuable contribution to its overall interpretation. In this study, we broadly investigate the potential of Computer-Aided Detection (CADe) for the identification of early Barrett's cancer using VLE. We employ a histopathologically validated set of ex-vivo VLE images for evaluating and comparing a considerable set of widely-used image features and machine learning algorithms. In addition, we show that incorporating clinical knowledge in feature design, leads to a superior classification performance and additional benefits, such as low complexity and fast computation time. Furthermore, we identify an optimal tissue depth for classification of 0.5-1.0 mm, and propose an extension to the evaluated features that exploits this phenomenon, improving their predictive properties for cancer detection in VLE data. Finally, we compare the performance of the CADe methods with the classification accuracy of two VLE experts. With a maximum Area Under the Curve (AUC) in the range of 0.90-0.93 for the evaluated features and machine learning methods versus an AUC of 0.81 for the medical experts, our experiments show that computer-aided methods can achieve a considerably better performance than trained human observers in the analysis of VLE data.

KEYWORDS:

Barrett's Esophagus; Computer-aided detection and diagnosis; Endoscopy; Esophageal adenocarcinoma; Optical Coherence Tomography

[Indexed for MEDLINE]

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