Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video).

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.