Computer-aided prediction of axillary lymph node status in breast cancer using tumor surrounding tissue features in ultrasound images. (July 2017)

• Record Type: Journal Article
• Authors: Moon, Woo Kyung; Lee, Yan-Wei; Huang, Yao-Sian; Lee, Su Hyun; Bae, Min Sun; Yi, Ann; Huang, Chiun-Sheng; Chang, Ruey-Feng
• Is Part Of: Computer methods and programs in biomedicine. Volume 146(2017)
• Page Start: 143
• Page End: 150
• Publication Date: 2017-07
• Subjects: Computer-aided prediction (CAP) system -- Breast cancer -- Tumor surrounding tissue -- Axillary lymph node (ALN) staging -- Image matting; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.06.001
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A computer-aided prediction system for predicting the axillary lymph node status in patients with breast cancer is proposed. A prediction system based on combined features of tumor surrounding tissue to predict axillary lymph node status is proposed. The tumor surrounding tissue would provide the useful information to predict the ALN status. Abstract: Background and objective: The presence or absence of axillary lymph node (ALN) metastasis is the most important prognostic factor for patients with early-stage breast cancer. In this study, a computer-aided prediction (CAP) system using the tumor surrounding tissue features in ultrasound (US) images was proposed to determine the ALN status in breast cancer. Methods: The US imaging database used in this study contained 114 cases of invasive breast cancer and 49 of them were ALN metastasis. After the tumor region segmentation by the level set method, image matting method was used to extract surrounding abnormal tissue of tumor from the acquired images. Then, 21 features composed of 2 intensity, 3 morphology, and 16 textural features are extracted from the surrounding tissue and processed by a logistic regression model. Finally, the prediction model is trained and tested from the selected features. Results: In the experiments, the textural feature set extracted from surrounding tissue showed higher performance than intensity and morphology feature sets ( Az, 0.7756 vs 0.7071 and 0.6431). The accuracy, sensitivity, specificity and the area index Az under the receiver operating characteristic (ROC) curve for the CAP system were 81.58% (93/114), 81.63% (40/49), 81.54% (53/65), and 0.8269 for using combined feature set. Conclusions: These results indicated that the proposed CAP system can be helpful to determine the ALN status in patients with breast cancer.