Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video) - 23/12/11
Reprint requests: Shigeto Yoshida, MD, Department of Endoscopy, Hiroshima University Hospital, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8551, JapanRésumé |
Background |
Narrow-band imaging (NBI) classification of colorectal lesions is clinically useful in determining treatment options for colorectal tumors. There is a learning curve, however. Accurate NBI-based diagnosis requires training and experience. In addition, objective diagnosis is necessary. Thus, we developed a computerized system to automatically classify NBI magnifying colonoscopic images.
Objective |
To evaluate the utility and limitations of our automated NBI classification system.
Design |
Retrospective study.
Setting |
Department of endoscopy, university hospital.
Main outcome measurements |
Performance of our computer-based system for classification of NBI magnifying colonoscopy images in comparison to classification by two experienced endoscopists and to histologic findings.
Results |
For the 371 colorectal lesions depicted on validation images, the computer-aided classification system yielded a detection accuracy of 97.8% (363/371); sensitivity and specificity of types B-C3 lesions for a diagnosis of neoplastic lesion were 97.8% (317/324) and 97.9% (46/47), respectively. Diagnostic concordance between the computer-aided classification system and the two experienced endoscopists was 98.7% (366/371), with no significant difference between methods.
Limitations |
Retrospective, single-center in this initial report.
Conclusion |
Our new computer-aided system is reliable for predicting the histology of colorectal tumors by using NBI magnifying colonoscopy.
Le texte complet de cet article est disponible en PDF.Abbreviations : NBI, PIVI
Plan
| DISCLOSURE: All authors disclosed no financial relationships relevant to this publication. |
Vol 75 - N° 1
P. 179-185 - janvier 2012 Retour au numéro
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