Woo Kyung Moon1, Yan-Wei Lee2, Yao-Sian Huang2, Su Hyun Lee1, Min Sun Bae1, Ann Yi3, Chiun-Sheng Huang4, Ruey-Feng Chang5. 1. Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul 110-744, Korea. 2. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan. 3. Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul 110-744, Korea; Seoul National University Hospital Healthcare System Gangnam Center, Seoul 135-984, Korea. 4. Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan. 5. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan; Graduate Institute of Network and Multimedia, National Taiwan University, Taipei, Taiwan. Electronic address: rfchang@csie.ntu.edu.tw.
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.
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.