Computer-aided prediction of axillary lymph node status in breast cancer using tumor surrounding tissue features in ultrasound images
Journal
Computer Methods and Programs in Biomedicine
Journal Volume
146
Pages
143-150
Date Issued
2017
Author(s)
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. ? 2017 Elsevier B.V.
Subjects
Axillary lymph node (ALN) staging; Breast cancer; Computer-aided prediction (CAP) system; Image matting; Tumor surrounding tissue
SDGs
Other Subjects
Body fluids; Diseases; Forecasting; Image segmentation; Medical imaging; Numerical methods; Pathology; Regression analysis; Tumors; Ultrasonics; Axillary lymph nodes; Breast Cancer; Computer aided; Early-stage breast cancer; Image matting; Logistic Regression modeling; Receiver operating characteristic curves; Region segmentation; Tissue; adult; aged; Article; axillary lymph node; axillary lymph node metastasis; breast cancer; breast carcinoma; cancer morphology; cancer tissue; clinical evaluation; computer aided prediction; computer assisted diagnosis; controlled study; data processing; diagnostic accuracy; diagnostic test accuracy study; echomammography; echomammography device; histopathology; human; image analysis; image processing; image quality; image segmentation; lobular carcinoma; lymph node metastasis; major clinical study; prediction; predictive value; retrospective study; sensitivity and specificity; tumor volume; ultrasound transducer; axilla; breast tumor; diagnostic imaging; echography; female; lymph node; lymph node metastasis; pathology; Axilla; Breast Neoplasms; Diagnosis, Computer-Assisted; Female; Humans; Lymph Nodes; Lymphatic Metastasis; Sensitivity and Specificity; Ultrasonography
Publisher
Elsevier Ireland Ltd
Type
journal article