Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy

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.