PURPOSE: Ultrasonography has the potential to accurately stage breast cancer with automated analysis to detect axillary lymph node metastasis. The aim of this study was to develop and test automated quantitative ultrasound image analysis of axillary lymph nodes for breast cancer staging. METHODS: Following an IRB-approved HIPAA compliant protocol, ultrasound images of 90 breast cancer patients presenting for lymph node assessment were retrospectively collected. There were 51 node-positive and 39 node-negative patients, yielding images of 223 lymph nodes (109 positive for metastasis and 114 negative for metastasis). The analysis was completely automated apart from the manual indication of the approximate center of each lymph node. Mathematical descriptors of the nodes, which served as image-based biomarkers, were computer-extracted and input to a classifier for the task of distinguishing between positive (i.e., metastatic) and negative lymph nodes. The performance of this task was assessed using receiver operating characteristic (ROC) analysis with evaluation by-node and by-patient using the area under the ROC curve (AUC) as the performance metric. RESULTS: The AUC was 0.85 (standard error 0.03) for by-node evaluation when distinguishing between positive and negative lymph nodes. The AUC was 0.87 (0.04) for patient-based prognosis, i.e., assessing whether patients were lymph node-positive or lymph node-negative. CONCLUSION: Based on these classification results, we conclude that mathematical descriptors of sonographically imaged lymph nodes may be useful as prognostic biomarkers in breast cancer staging and demonstrate potential for predicting patient lymph node status.
PURPOSE: Ultrasonography has the potential to accurately stage breast cancer with automated analysis to detect axillary lymph node metastasis. The aim of this study was to develop and test automated quantitative ultrasound image analysis of axillary lymph nodes for breast cancer staging. METHODS: Following an IRB-approved HIPAA compliant protocol, ultrasound images of 90 breast cancerpatients presenting for lymph node assessment were retrospectively collected. There were 51 node-positive and 39 node-negative patients, yielding images of 223 lymph nodes (109 positive for metastasis and 114 negative for metastasis). The analysis was completely automated apart from the manual indication of the approximate center of each lymph node. Mathematical descriptors of the nodes, which served as image-based biomarkers, were computer-extracted and input to a classifier for the task of distinguishing between positive (i.e., metastatic) and negative lymph nodes. The performance of this task was assessed using receiver operating characteristic (ROC) analysis with evaluation by-node and by-patient using the area under the ROC curve (AUC) as the performance metric. RESULTS: The AUC was 0.85 (standard error 0.03) for by-node evaluation when distinguishing between positive and negative lymph nodes. The AUC was 0.87 (0.04) for patient-based prognosis, i.e., assessing whether patients were lymph node-positive or lymph node-negative. CONCLUSION: Based on these classification results, we conclude that mathematical descriptors of sonographically imaged lymph nodes may be useful as prognostic biomarkers in breast cancer staging and demonstrate potential for predicting patient lymph node status.
Authors: E E Deurloo; P J Tanis; K G A Gilhuijs; S H Muller; R Kröger; J L Peterse; E J Th Rutgers; R Valdés Olmos; L J Schultze Kool Journal: Eur J Cancer Date: 2003-05 Impact factor: 9.162
Authors: Hiroyuki Abe; Robert A Schmidt; Rajshri N Shah; Akiko Shimauchi; Kirti Kulkarni; Charlene A Sennett; Gillian M Newstead Journal: AJR Am J Roentgenol Date: 2010-02 Impact factor: 3.959