Predictive features for early cancer detection in Barrett's esophagus using Volumetric Laser Endomicroscopy. (July 2018) - van der Sommen, Fons; Klomp, Sander R.; Swager, Anne-Fré; Zinger, Svitlana; Curvers, Wouter L.; Bergman, Jacques J.G.H.M.; Schoon, Erik J.; de With, Peter H.N.
Graphical abstract: Highlights: First study on CAD for cancer detection in VLE images. CAD methods clearly outperform trained human experts. Simple clinically-inspired features outperform established alternatives. An optimal scan depth for cancer detection is identified. Exhaustive benchmark of widely-used methods for comparison. 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.