ArticleBMC cancer2022
Prediction model of axillary lymph node status using automated breast ultrasound (ABUS) and ki-67 status in early-stage breast cancer.
Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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19 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Prediction of sentinel lymph node status in patients with early breast cancer using breast imaging as an alternative to surgical staging-a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- A novel model for assessing lymph node metastasis and the immune microenvironment in breast cancer by integrating digital pathology images and transcriptomics.Discover oncology · 2026Article
- Ultrasound-based multiregional radiomics nomogram for predicting recurrence in HER2-positive breast cancer.BMC medical imaging · 2026Article
- Noninvasive prediction of Ki67 proliferation index in breast cancer based on integrated photoacoustic imaging.BMC medical imaging · 2026Article
- Predictive value of ABVS and VTIQ parameters for axillary lymph node metastasis in breast cancer.Frontiers in cell and developmental biology · 2026Article
- Preoperative prediction of breast cancer Ki-67 status via multimodal ultrasound-clinical nomogram: a single-center study.Translational cancer research · 2025Article
- Impact of Chronic Stress and Anxiety on Interleukin-6, Hypoxia-Inducible Factor-1α and on Pathological and Immunohistochemical Parameters in Breast Cancer.Asian Pacific journal of cancer prevention : APJCP · 2025Article
- Ultrasound-based deep learning radiomics for enhanced axillary lymph node metastasis assessment: a multicenter study.The oncologist · 2025Article
- Prediction of axillary lymph node metastasis in T1 breast cancer using diffuse optical tomography, strain elastography and molecular markers.Quantitative imaging in medicine and surgery · 2025Article
- Prediction model of axillary lymph node status using an automated breast volume ultrasound radiomics nomogram in early breast cancer with negative axillary ultrasound.Frontiers in immunology · 2025Article
- Construction of a prediction model for axillary lymph node metastasis in breast cancer patients based on a multimodal fusion strategy of ultrasound and pathological images.Frontiers in oncology · 2025Article
- Diagnosis of Benign and Malignant Newly Developed Nodules on the Surgical Side After Breast Cancer Surgery Based on Machine Learning.The breast journal · 2025Article
- Role of the Clinical Features and MRI Parameters on Ki-67 Expression in Hepatocellular Carcinoma Patients: Development of a Predictive Nomogram.Journal of gastrointestinal cancer · 2024Article
- Revolutionizing breast cancer Ki-67 diagnosis: ultrasound radiomics and fully connected neural networks (FCNN) combination method.Breast cancer research and treatment · 2024Article
- A nomogram model for predicting the risk of axillary lymph node metastasis in patients with early breast cancer and cN0 status.Oncology letters · 2024Article
- Characteristics and risk factors of axillary lymph node metastasis of microinvasive breast cancer.Breast cancer research and treatment · 2024Article
- Pathological characteristics predicting sentinel lymph node metastasis in early breast cancer patients.Caspian journal of internal medicine · 2024Article
- Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in invasive breast cancer.American journal of translational research · 2024Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAutomated 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.
methodsA 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.
resultsKi-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).
conclusionABUS features and Ki-67 can meaningfully predict ALNM in EBC and the prediction model may facilitate a more effective therapeutic schedule.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.