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

Abstract

Background 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.

Quantitative ultrasound radiomics analysis to evaluate lymph nodes in patients with cancer: a systematic review

“…All 47 studies are summarized in Supplementary Table 1 [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48] and were divided based on the tumor of origin: breast cancer, head and neck cancer, and cervical cancer. Most of the papers were published between 2019 and 2022 (n = 19 articles), and the analyses were conducted using MATLAB, R software, SPSS, OriginPro, a papillary thyroid carcinoma (PTC), cervical LN metastasis (LNM) prediction system (custom-built, n = 3), MIPAV, and Mazda.…”

“…Feature extraction was mainly performed with PyRadiomics, ITK-SNAP, LifeX, MedCalc, ImageJ, PASW and Prism. The values of the area under the curve (AUC), sensitivity, specificity, and accuracy are summarized in ▶ Table 1 [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48].…”

“…Overall, the ALN metastasis prediction studies show promising results, under consideration of validation/testing values: all the studies were successful in terms of AUC values. In fact, the lower AUC value is reported by Gao et al [4] at 0.733, while eight studies report an AUC value over 0.800 [3,5,8,11,12,33,39,43] and three over 0.900 [6,7,11].…”

“…The vast majority of breast cancer-related studies used US-based radiomics analysis, either alone or combined with clinicopathological characteristics, to predict axillary lymph node (ALN) secondary involvement (n = 13 articles) [2,3,4,5,6,7,8,11,12,33,39,41,43]. Interestingly Zheng et al [6], Sun et al [7], and Tang et al [41] exploited the advantages of deep learning radiomics (DLR), reaching excellent results in predicting ALN status.…”

“…Interestingly Zheng et al [6], Sun et al [7], and Tang et al [41] exploited the advantages of deep learning radiomics (DLR), reaching excellent results in predicting ALN status. Chen et al [12] and Li et al [33] focused on automated breast US-based analysis, building nomograms that incorporated the Rad-score with the retraction phenomenon and showing fine discriminatory power. Lastly, Zhang et al [34] and Gu et al [39] developed radiomics nomograms to predict treatment response and LNM status after presurgical neoadjuvant chemotherapy, obtaining good performance scores.…”