Optimized Radiomics Nomogram Based on Automated Breast Ultrasound System: A Potential Tool for Preoperative Prediction of Metastatic Lymph Node Burden in Breast Cancer

Abstract

Ning Li,1,* Chao Song,2 Xian Huang,3,* Hongjiang Zhang,1 Juan Su,4 Lichun Yang,5 Juhua He,6 Guihua Cui1 1Department of Ultrasound, Anning First People’s Hospital, Kunming City, People’s Republic of China; 2Department of Radiology, Anning First People’s Hospital, Kunming City, People’s Republic of China; 3Department of Ultrasound, Kunming City Maternal and Child Health Hospital, Kunming City, People’s Republic of China; 4Department of Ultrasound, Yulong People’s Hospital, Lijiang City, People’s Republic of China; 5Department of Ultrasound, Yunnan Cancer Hospital, Kunming City, People’s Republic of China; 6Department of Function Examination, Yunnan Provincial Hospital of Traditional Chinese Medicine, Kunming City, People’s Republic of China*These authors contributed equally to this workCorrespondence: Chao Song, Department of Radiology, Anning First People’s Hospital, Ganghe South Road, Anning City, Kunming City, Yunnan Province, 650302, People’s Republic of China, Tel + 86-13908848395, Email [email protected] Hongjiang Zhang, Department of Ultrasound, Anning First People’s Hospital, Ganghe South Road, Anning City, Kunming City, Yunnan Province, 650302, People’s Republic of China, Tel [phone], Email [email protected]: Axillary lymph node dissection (ALND) can be safely avoided in women with T1 or T2 primary invasive breast cancer (BC) and one to two metastatic sentinel lymph nodes (SLNs). However, cancellation of ALND based solely on SLN biopsy (SLNB) may lead to adverse outcomes. Therefore, preoperative assessment of LN tumor burden becomes a new focus for ALN status.Objective: This study aimed to develop and validate a nomogram incorporating the radiomics score (rad-score) based on automated breast ultrasound system (ABUS) and other clinicopathological features for evaluating the ALN status in patients with early-stage BC preoperatively.Methods: Totally 354 and 163 patients constituted the training and validation cohorts. They were divided into ALN low burden (< 3 metastatic LNs) and high burden (≥ 3 metastatic LNs) based on the histopathological diagnosis. The radiomics features of the segmented breast tumor in ABUS images were extracted and selected to generate the rad-score of each patient. These rad-scores, along with the ALN burden predictors identified from the clinicopathologic characteristics, were included in the multivariate analysis to establish a nomogram. It was further evaluated in the training and validation cohorts.Results: High ALN burdens accounted for 11.2% and 10.8% in the training and validation cohorts. The rad-score for each patient was developed based on 7 radiomics features extracted from the ABUS images. The radiomics nomogram was built with the rad-score, tumor size, US-reported LN status, and ABUS retraction phenomenon. It achieved better predictive efficacy than the nomogram without the rad-score and exhibited favorable discrimination, calibration and clinical utility in both cohorts.Conclusion: We developed an ABUS-based radiomics nomogram for the preoperative prediction of ALN burden in BC patients. It would be utilized for the identification of patients with low ALN burden if further validated, which contributed to appropriate axillary treatment and might avoid unnecessary ALND.Keywords: axillary lymph node, sentinel lymph node biopsy, invasive breast cancer, radiomics, nomogram, automated breast ultrasound system, tumor burden

Keywords

• axillary lymph node
• sentinel lymph node biopsy
• invasive breast cancer
• radiomics
• nomogram
• automated breast ultrasound system
• tumor burden;