Computer-aided detection of early neoplastic lesions in Barrett’s esophagus
submitted 09. Juli 2015
accepted after revision 23. Februar 2016
21. April 2016 (online)
Background and study aims: Early neoplasia in Barrett’s esophagus is difficult to detect and often overlooked during Barrett’s surveillance. An automatic detection system could be beneficial, by assisting endoscopists with detection of early neoplastic lesions. The aim of this study was to assess the feasibility of a computer system to detect early neoplasia in Barrett’s esophagus.
Patients and methods: Based on 100 images from 44 patients with Barrett’s esophagus, a computer algorithm, which employed specific texture, color filters, and machine learning, was developed for the detection of early neoplastic lesions in Barrett’s esophagus. The evaluation by one endoscopist, who extensively imaged and endoscopically removed all early neoplastic lesions and was not blinded to the histological outcome, was considered the gold standard. For external validation, four international experts in Barrett’s neoplasia, who were blinded to the pathology results, reviewed all images.
Results: The system identified early neoplastic lesions on a per-image analysis with a sensitivity and specificity of 0.83. At the patient level, the system achieved a sensitivity and specificity of 0.86 and 0.87, respectively. A trade-off between the two performance metrics could be made by varying the percentage of training samples that showed neoplastic tissue.
Conclusion: The automated computer algorithm developed in this study was able to identify early neoplastic lesions with reasonable accuracy, suggesting that automated detection of early neoplasia in Barrett’s esophagus is feasible. Further research is required to improve the accuracy of the system and prepare it for real-time operation, before it can be applied in clinical practice.
- 1 Lagergren J, Lagergren P. Oesophageal cancer. BMJ 2010; 341: c6280
- 2 Dent J. Barrett’s esophagus: a historical perspective, an update on core practicalities and predictions on future evolutions of management. J Gastroenterol Hepatol 2011; 26: 11-30
- 3 Lepage C, Rachet B, Jooste V et al. Continuing rapid increase in esophageal adenocarcinoma in England and Wales. Am J Gastroenterol 2008; 103: 2694-2699
- 4 Behrens A, Pech O, Graupe F et al. Barrett’s adenocarcinoma of the esophagus: better outcomes through new methods of diagnosis and treatment. Dtsch Arztebl Int 2011; 108: 313-319
- 5 Phoa KN, Pouw RE, Bisschops R et al. Multimodality endoscopic eradication for neoplastic Barrett oesophagus: results of an European multicentre study (EURO-II). Gut 2015; DOI: 10.1136/gutjnl-2015-309298
- 6 Reid B, Blount P, Feng Z et al. Optimizing endoscopic biopsy detection of early cancers in Barrett’s high-grade dysplasia. Am J Gastroenterol 2000; 95: 3089-3096
- 7 Barbosa DJC, Roupar D, Lima CS. Multiscale texture descriptors for automatic small bowel tumors detection in capsule endoscopy. In: Olkkonen H, , ed. Discrete wavelet transforms – Biomedical applications. Rijeka, Croatia: Intech; 2011: 155-174
- 8 Li B, Meng MQ-H. Computer-aided detection of bleeding regions for capsule endoscopy images. IEEE Trans Biomed Eng 2009; 56: 1032-1039
- 9 Li B, Meng MQ-H. Texture analysis for ulcer detection in capsule endoscopy images. Image Vis Comput 2009; 27: 1336-1342
- 10 Kodogiannis VS, Boulougoura MG, Lygouras J et al. A neuro-fuzzy-based system for detecting abnormal patterns in wireless-capsule endoscopic images. Neurocomputing 2007; 70: 704-717
- 11 Karkanis SA, Iakovidis DK, Maroulis DE et al. Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE Trans Inf Technol Biomed 2003; 7: 141-152
- 12 Oh J, Hwang S, Lee J et al. Informative frame classification for endoscopy video. Med Image Anal 2007; 11: 110-127
- 13 van Vilsteren FGI, Herrero LA, Pouw RE et al. Radiofrequency ablation for the endoscopic eradication of esophageal squamous high grade intraepithelial neoplasia and mucosal squamous cell carcinoma. Endoscopy 2011; 43: 282-290
- 14 Schlemper RJ, Riddell RH, Kato Y et al. The Vienna classification of gastrointestinal epithelial neoplasia. Gut 2000; 47: 251-255
- 15 Kara MA, Curvers WL, Bergman JJ. Advanced endoscopic imaging in Barrett’s esophagus. Tech Gastrointest Endosc 2010; 12: 82-89
- 16 van der Sommen F, Zinger S, Schoon EJ et al. Supportive automatic annotation of early esophageal cancer using local gabor and color features. Neurocomputing 2014; 144: 92-106
- 17 Setio AAA, van der Sommen F, Zinger S et al. Evaluation and comparison of textural feature representation for the detection of early stage cancer in endoscopy. Proceedings of the 8th International Conference on Computer Vision Theory and Applications, 2013 February 21–24. Barcelona, Spain: 2013: 238-243