Prediction model of axillary lymph node status using automated breast ultrasound (ABUS) and ki-67 status in early-stage breast cancer

• Published in: BMC cancer Vol. 22; no. 1; pp. 1 - 11
• Main Authors: Wang, Qiucheng, Li, Bo, Liu, Zhao, Shang, Haitao, Jing, Hui, Shao, Hua, Chen, Kexin, Liang, Xiaoshuan, Cheng, Wen
• Format: Journal Article
• Language: English
• Published: London BioMed Central 28.08.2022; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Automated breast ultrasound; Automation; Axillary lymph node metastasis; Biomedical and Life Sciences; Biomedicine; Biopsy; Body mass index; Breast; Breast cancer; Cancer Research; Care and treatment; Chemotherapy; Development and progression; Dissection; Early-stage breast cancer; Gene amplification; Health Promotion and Disease Prevention; Ki-67; Ki-67 antigen; Lymph nodes; Lymphatic metastasis; Lymphatic system; Lymphedema; Medicine/Public Health; Metastases; Metastasis; Multivariate analysis; Normal distribution; Oncology; Patients; Prediction models; Prevention; Radiation therapy; Regression analysis; Retraction phenomenon; Risk factors; Statistical analysis; Surgery; Surgical Oncology; Testing; Ultrasonic imaging; Ultrasound; Variables; China; United States > US; Ki-67; Axillary lymph node metastasis; Retraction phenomenon; Early-stage breast cancer; Automated breast ultrasound
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-022-10034-3

Background Automated breast ultrasound (ABUS) is a useful choice in breast disease diagnosis. The axillary lymph node (ALN) status is crucial for predicting the clinical classification and deciding on the treatment of early-stage breast cancer (EBC) and could be the primary indicator of locoregional recurrence. We aimed to establish a prediction model using ABUS features of primary breast cancer to predict ALN status. Methods A total of 469 lesions were divided into the axillary lymph node metastasis (ALNM) group and the no ALNM (NALNM) group. Univariate analysis and multivariate analysis were used to analyze the difference of clinical factors and ABUS features between the two groups, and a predictive model of ALNM was established. Pathological results were as the gold standard. Results Ki-67, maximum diameter (MD), posterior feature shadowing or enhancement and hyperechoic halo were significant risk factors for ALNM in multivariate logistic regression analysis ( P  < 0.05). The four risk factors were used to build the predictive model, and it achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.791 (95% CI: 0.751, 0.831). The accuracy, sensitivity and specificity of the prediction model were 72.5%, 69.1% and 75.26%. The positive predictive value (PPV) and negative predictive value (NPV) were 66.08% and 79.93%, respectively. Distance to skin, MD, margin, shape, internal echo pattern, orientation, posterior features, and hyperechoic halo showed significant differences between stage I and stage II ( P  < 0.001). Conclusion ABUS features and Ki-67 can meaningfully predict ALNM in EBC and the prediction model may facilitate a more effective therapeutic schedule.