Breast cancer risk prediction model based on improved local ternary pattern algorithm / 中国医学影像技术

ABSTRACT

Objective To explore the value of new and fused conventional texture features extracted from mammograms using improved local ternary patterns (LTP) in predicting risk of breast cancer.Methods Mammograms were segmented.Based on improved LTP,the new and conventional texture features were extracted from segmented mammograms of bilateral breasts.Then the features of bilateral breasts were merged.The high dimensional characteristics were reduced with principal component analysis (PCA).Finally,the new texture features were classified with k-nearest neighbor (KNN),and the fusion features were clustered with logistic alternating decision tree (LADTree) algorithm.Results The area under ROC curve (AUC) of new texture features for predicting breast cancer was 0.732 4 ±0.042 8,and the sensitivity,specificity and prediction accuracy was 72.04％ (67/93),74.51％ (76/102) and 73.33％ (143/195),respectively.Furthermore,AUC of fusion features was 0.865 5± 0.014 8,the sensitivity,specificity and prediction accuracy was 84.95％ (79/93),88.23％ (90/102) and 86.67％ (169/195),respectively.Conclusion The new texture features based on improved LTP have high prediction accuracy for breast cancer,and the prediction efficacy can be improved after fusion with conventional features.