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Computer-aided detection of early neoplastic lesions in Barrett’s esophagus

van der Sommen, Fons ; Zinger, Svitlana ; Curvers, Wouter L. ; Bisschops, Raf ; Pech, Oliver ; Weusten, Bas L. A. M. ; Bergman, Jacques J. G. H. M. ; de With, Peter H. N. ; Schoon, Erik J.

Endoscopy, 2016-07, Vol.48 (7), p.617-624 [Periódico revisado por pares]

Stuttgart · New York: Georg Thieme Verlag KG

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Título:
Computer-aided detection of early neoplastic lesions in Barrett’s esophagus
Autor: van der Sommen, Fons ; Zinger, Svitlana ; Curvers, Wouter L. ; Bisschops, Raf ; Pech, Oliver ; Weusten, Bas L. A. M. ; Bergman, Jacques J. G. H. M. ; de With, Peter H. N. ; Schoon, Erik J.
Assuntos: Barrett Esophagus - complications ; Barrett Esophagus - diagnostic imaging ; Barrett Esophagus - pathology ; Esophageal Neoplasms - diagnostic imaging ; Esophageal Neoplasms - etiology ; Feasibility Studies ; Female ; Humans ; Image Processing, Computer-Assisted ; Male ; Middle Aged ; Original article ; Pattern Recognition, Automated ; ROC Curve ; Support Vector Machine
É parte de: Endoscopy, 2016-07, Vol.48 (7), p.617-624
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Descrição: 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.
Editor: Stuttgart · New York: Georg Thieme Verlag KG
Idioma: Inglês

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Computer-aided detection of early neoplastic lesions in Barrett’s esophagus

van der Sommen, Fons ; Zinger, Svitlana ; Curvers, Wouter L. ; Bisschops, Raf ; Pech, Oliver ; Weusten, Bas L. A. M. ; Bergman, Jacques J. G. H. M. ; de With, Peter H. N. ; Schoon, Erik J.

Stuttgart · New York: Georg Thieme Verlag KG

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