Trial reportBMC cancer2023
Construction of a comprehensive predictive model for axillary lymph node metastasis in breast cancer: a retrospective study.
Trial report in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05826197 (A Study on the Construction of a Comprehensive Predictive Model for Axillary Lymph Node Metastasis in Breast Cancer), which is not on this map. Cited by 14 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Study on the Construction of a Comprehensive Predictive Model for Axillary Lymph Node Metastasis in Breast Cancer
Who cites it
14 citing papers in PubMed, 3 syntheses or guidelines pooled it, 16 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
- 18F-FDG PET/CT Semiquantitative and Radiomic Features for Assessing Pathologic Axillary Lymph Node Status in Clinical Stage I-III Breast Cancer Patients: A Systematic Review.Current oncology (Toronto, Ont.) · 2025Pooled it
- Bibliometric and visual analysis of radiomics for evaluating lymph node status in oncology.Frontiers in medicine · 2024Pooled it
- Diagnostic performance ofEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Influencing factors of axillary lymph node metastasis and prognosis in patients with T2 breast cancer.Gland surgery · 2025Article
- Construction and validation of a novel nomogram for prediction of lymph node metastasis in HER2-positive breast cancer: based on the optimal number of examined lymph nodes for accurate nodal staging.BMC women's health · 2025Article
- Lymph node metastasis in patients with hepatocellular carcinoma using machine learning: a population-based study.Frontiers in oncology · 2025Article
- Predictive value of ultrasound assessment of axillary and brachial artery parameters for lymph node metastasis in breast cancer patients.American journal of cancer research · 2025Article
- Radiomics in breast cancer: Current advances and future directions.Cell reports. Medicine · 2024Review
- Article
- Ultrasound radiomics signature for predicting central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma.Thyroid research · 2024Article
- Development and Validation of a Nomogram for Axillary Lymph Node Metastasis Risk in Breast Cancer.Journal of Cancer · 2024Article
- Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in invasive breast cancer.American journal of translational research · 2024Article
- Establishment of Prediction Model of Axillary Lymph Node Metastasis Before Operation for Early-Stage Breast Cancer.Cancer control : journal of the Moffitt Cancer CenterArticle
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
purposeThe accurate assessment of axillary lymph node metastasis (LNM) in early-stage breast cancer (BC) is of great importance. This study aimed to construct an integrated model based on clinicopathology, ultrasound, PET/CT, and PET radiomics for predicting axillary LNM in early stage of BC. MATERIALS AND
methods124 BC patients who underwent 18 F-fluorodeoxyglucose (18 F-FDG) PET/CT and whose diagnosis were confirmed by surgical pathology were retrospectively analyzed and included in this study. Ultrasound, PET and clinicopathological features of all patients were analyzed, and PET radiomics features were extracted to establish an ultrasound model (clinicopathology and ultrasound; model 1), a PET model (clinicopathology, ultrasound, and PET; model 2), and a comprehensive model (clinicopathology, ultrasound, PET, and radiomics; model 3), and the diagnostic efficacy of each model was evaluated and compared.
resultsThe T stage, US_BIRADS, US_LNM, and PET_LNM in the positive axillary LNM group was significantly higher than that of in the negative LNM group (P = 0.013, P = 0.049, P < 0.001, P < 0.001, respectively). Radiomics score for predicting LNM (RS_LNM) for the negative LNM and positive LNM were statistically significant difference (-1.090 ± 0.448 vs. -0.693 ± 0.344, t = -4.720, P < 0.001), and the AUC was 0.767 (95% CI: 0.674-0.861). The ROC curves showed that model 3 outperformed model 1 for the sensitivity (model 3 vs. model 1, 82.86% vs. 48.57%), and outperformed model 2 for the specificity (model 3 vs. model 2, 82.02% vs. 68.54%) in the prediction of LNM. The AUC of mode 1, model 2 and model 3 was 0.687, 0.826 and 0.874, and the Delong test showed the AUC of model 3 was significantly higher than that of model 1 and model 2 (P < 0.05). Decision curve analysis showed that model 3 resulted in a higher degree of net benefit for all the patients than model 1 and model 2.
conclusionThe use of a comprehensive model based on clinicopathology, ultrasound, PET/CT, and PET radiomics can effectively improve the diagnostic efficacy of axillary LNM in BC.
trial registrationThis study was registered at ClinicalTrials Gov (number NCT05826197) on 7th, May 2023.
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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.