Image Features for Automated Colorectal Polyp Classification Based on Clinical Prediction Models

摘要

Accurate endoscopic differentiation on resection of colorectal polyps (CRPs) (resect-discard or diagnose-leave strategies) increases cost-efficiency and reduces patient risk. We aim to develop a classification algorithm for automated differentiation of CRPs, by following the validated clinical Work-group serrAted polypS and Polyposis (WASP) classification scheme. Quantitative image features are investigated for each individual WASP criterion and classification is performed by conventional SVM. The technical WASP model results in areas under the curve of 0.87−0.95 and accuracies of 78−89%. Predicting polyp histology using model-based learning out-performs medical experts (accuracy, 87−93% vs 86 87%). Direct classification predicts more premalignant polyps−as being benign, compared to the automated WASP scheme. These errors do not occur when including ROC characteristics to the WASP model. The proposed WASP model is the first automated system, competing with medical expert classification.