ArticleBreast cancer (Dove Medical Press)2023
Optimized Radiomics Nomogram Based on Automated Breast Ultrasound System: A Potential Tool for Preoperative Prediction of Metastatic Lymph Node Burden in Breast Cancer.
Article in Breast cancer (Dove Medical Press), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Integrating intratumoral and peritumoral ultrasound radiomics with clinicopathological features to predict axillary lymph node metastasis in early breast cancer.Gland surgery · 2026Article
- Super-resolution ultrasound radiomics for pre-FNA prediction of nondiagnostic (Bethesda I) thyroid nodules.Frontiers in endocrinology · 2026Article
- The application of super-resolution ultrasound radiomics models in predicting the failure of conservative treatment for ectopic pregnancy.Reproductive biology and endocrinology : RB&E · 2025Article
- Artificial intelligence-based automated breast ultrasound radiomics for breast tumor diagnosis and treatment: a narrative review.Frontiers in oncology · 2025Review
- Utilizing multiclassifier radiomics analysis of ultrasound to predict high axillary lymph node tumour burden in node-positive breast cancer patients: a multicentre study.Annals of medicine · 2024Article
- Predicting Non-Mass Breast Cancer Utilizing Ultrasound and Molybdenum Target X-Ray Characteristics.Journal of multidisciplinary healthcare · 2024Article
- Radiomics Analysis of Breast Lesions in Combination with Coronal Plane of ABVS and Strain Elastography.Breast cancer (Dove Medical Press) · 2023Article
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8 authors.
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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.
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