Background and study aims Invasive cancer carries the risk of metastasis, and therefore, the ability to distinguish between invasive cancerous lesions and less-aggressive lesions is important. We evaluated a computer-aided diagnosis system that uses ultra-high (approximately × 400) magnification endocytoscopy (EC-CAD). Patients and methods We generated an image database from a consecutive series of 5843 endocytoscopy images of 375 lesions. For construction of a diagnostic algorithm, 5543 endocytoscopy images from 238 lesions were randomly extracted from the database for machine learning. We applied the obtained algorithm to 200 endocytoscopy images and calculated test characteristics for the diagnosis of invasive cancer. We defined a high-confidence diagnosis as having a ≥ 90 % probability of being correct. Results Of the 200 test images, 188 (94.0 %) were assessable with the EC-CAD system. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were 89.4 %, 98.9 %, 94.1 %, 98.8 %, and 90.1 %, respectively. High-confidence diagnosis had a sensitivity, specificity, accuracy, PPV, and NPV of 98.1 %, 100 %, 99.3 %, 100 %, and 98.8 %, respectively. Conclusion: EC-CAD may be a useful tool in diagnosing invasive colorectal cancer.
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