Barrett’s esopaghagus (BE) is a known precursor of esophageal adenocarcinoma (EAC). Patients with BE undergo regular surveillance to early detect stages of EAC. Volumetric laser endomicroscopy (VLE) is a novel technology incorporating a second-generation form of optical coherence tomography and is capable of imaging the inner tissue layers of the esophagus over a 6 cm length scan. However, interpretation of full VLE scans is still a challenge for human observers. In this work, we train an ensemble of deep convolutional neural networks to detect neoplasia in 45 BE patients, using a dataset of images acquired with VLE in a multi-center study. We achieve an area under the receiver operating characteristic curve (AUC) of 0.96 on the unseen test dataset and we compare our results with previous work done with VLE analysis, where only AUC of 0.90 was achieved via cross-validation on 18 BE patients. Our method for detecting neoplasia in BE patients facilitates future advances on patient treatment and provides clinicians with new assisting solutions to process and better understand VLE data.
Background: Optical diagnosis of colorectal polyps (CRPs) remains challenging. Imaging enhancement techniques such as narrow band imaging and blue light imaging (BLI) can improve optical diagnosis. We developed and prospectively validated a computer-aided diagnosis system (CADx) using high definition white light (HDWL) and BLI images, and compared it with the optical diagnosis of expert and novice endoscopists.
Methods: The CADx characterized CRPs by exploiting artificial neural networks. Six experts and thirteen novices optically diagnosed 60 CRPs based on intuition. After a washout period of four weeks, the same set of CRPs was permuted and optically diagnosed using BASIC (BLI Adenoma Serrated International Classification).
Results: The CADx had a diagnostic accuracy of 88.3% using HDWL images and 86.7% using BLI images. The overall diagnostic accuracy, combining HDWL and BLI (multimodal imaging), was 95.0% and significantly higher compared to experts (81.7%, p=0.031) and novices (66.5%, p<0.001). Sensitivity (95.6% vs. 61.1% and 55.4%) was also higher for CADx, while specificity was higher for experts compared to CADx and novices (94.1% vs 93.3% and 92.1%). For endoscopists, diagnostic accuracy did not increase using BASIC, neither for experts (Intuition 79.5% vs BASIC 81.7%, p=0.140) nor for novices (Intuition 66.7% vs BASIC 66.5%, p=0.953).
Conclusion: The CADx had a significantly higher diagnostic accuracy than experts and novices for the optical diagnosis of CRPs. Multimodal imaging, incorporating both HDWL and BLI, improved the diagnostic accuracy of the CADx. BASIC did not increase the diagnostic accuracy of endoscopists compared to intuitive optical diagnosis.
Barrett's esopaghagus (BE) is a known precursor of esophageal adenocarcinoma (EAC). Patients with BE undergo regular surveillance to early detect stages of EAC. Volumetric laser endomicroscopy (VLE) is a novel technology capable of imaging the inner tissue layers of the esophagus over a 6-cm length scan. However, interpretation of full VLE scans is still a challenge for human observers. In this work, we train an ensemble of deep convolutional neural networks to detect neoplasia in BE patients, using a dataset of images acquired with VLE in a multicenter study. We achieve values of AUC=$0.96$ on the unseen test dataset and we compare our results with previous work done with VLE analysis. Our method for detecting neoplasia in BE patients facilitates future advances on patient treatment and provides clinicians with new assisting solutions to process and better understand VLE data.
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