AUTHOR=Zhao Feng , Cai Changjing , Liu Menghan , Xiao Jidong 

TITLE=Identification of the lymph node metastasis-related automated breast volume scanning features for predicting axillary lymph node tumor burden of invasive breast cancer via a clinical prediction model

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2022.881761

DOI=10.3389/fendo.2022.881761

ISSN=1664-2392

ABSTRACT=Breast cancer has become the malignant tumor with the highest incidence in females. Axillary lymph node dissection (ALND) is an effective method of maintaining regional control, however, it is associated with a significant risk of complications. Meanwhile, whether the patients need ALND or not is according to sentinel lymph node biopsy (SLNB). However, the false negative results of SLNB had been reported. Automated breast volume scanning (ABVS) is a routine examination in breast cancer. A real-world cohort contained 245 patients who underwent ABVS examination of the patients with breast cancer were enrolled, including 251 tumor lesions. The ABVS manifestations were analyzed with the SLNB results and the ALND results for selecting the lymph node metastasis related ABVS features. Finally, a nomogram was used to construct a breast cancer axillary lymph node tumor burden prediction model. Breast cancer patients with a molecular subtype of luminal B-type, a maximum lesion diameter of ≥ 5 cm, tumor invasion of the Cooper's ligament, and tumor invasion of the nipple had heavy lymph node tumor burden. Molecular classification, tumor size, and Cooper's ligament status were used to construct a clinical prediction model of axillary lymph node tumor burden. The consistency indexes (AUC) of the training cohort and the validation cohort were 0.743 and 0.711, respectively, which was close to SLNB (0.768). The best cut-off value of ABVS nomogram is 81.146 points. After in combination with ABVS features and SLNB, the AUC of prediction model is 0.889, and the best cut-off value is 178.965 points. The calibration curve showed that the constructed nomogram clinical prediction model and the real-results were highly consistent. The clinical prediction model constructed using molecular classification, tumor size, and cooper's ligament status can effectively predict the probability of heavy axillary lymph node tumor burden, which can be the significant supplement to the SLNB. Therefore, this model may be used for individual decision-making in the diagnosis and treatments of breast cancer.