ArticleScientific reports2016
A nomogram to predict the probability of axillary lymph node metastasis in early breast cancer patients with positive axillary ultrasound.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic value of multiple ultrasound diagnostic techniques for axillary lymph node metastases in breast cancer: A systematic analysis and network meta-analysis.Frontiers in oncology · 2022Pooled it
- Trial
- Integrating intratumoral and peritumoral ultrasound radiomics with clinicopathological features to predict axillary lymph node metastasis in early breast cancer.Gland surgery · 2026Article
- A study on predicting additional axillary lymph node positivity in breast cancer patients with positive sentinel lymph node to avoid axillary lymph node dissection.Gland surgery · 2026Article
- DeiT-MLP-mixer for the preoperative prediction of axillary lymph node involvement in breast cancer via ultrasound imaging.BMC medical imaging · 2026Article
- Multimodal nomogram for predicting axillary pathological complete response after neoadjuvant therapy in clinically node-positive breast cancer.Frontiers in oncology · 2026Article
- A non-invasive preoperative model for predicting sentinel lymph node metastasis in breast cancer using clinical data and MRI.BMC medical imaging · 2025Article
- A preoperative prediction model for ipsilateral axillary lymph node metastasis of breast cancer based on clinicopathological and ultrasonography features: a prospective cohort study.Frontiers in oncology · 2025Article
- Predicting Axillary Lymph Node Metastasis in Breast Cancer Using Ultrasound and Machine Learning with SHAP.Cancer management and research · 2025Article
- A nomogram model for predicting the risk of axillary lymph node metastasis in patients with early breast cancer and cN0 status.Oncology letters · 2024Article
- A non-invasive preoperative prediction model for predicting axillary lymph node metastasis in breast cancer based on a machine learning approach: combining ultrasonographic parameters and breast gamma specific imaging features.Radiation oncology (London, England) · 2024Article
- Assessment of the axilla in women with early-stage breast cancer undergoing primary surgery: a review.World journal of surgical oncology · 2024Review
- Article
- Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in invasive breast cancer.American journal of translational research · 2024Article
- Article
- A novel nomogram model of breast cancer-based imaging for predicting the status of axillary lymph nodes after neoadjuvant therapy.Scientific reports · 2023Article
- Development and validation of an ultrasonography and clinicopathological features-based nomogram for non-sentinel lymph node metastasis.Gland surgery · 2023Article
- Ultrasound and clinicopathological characteristics of breast cancer for predicting axillary lymph node metastasis.Clinical hemorheology and microcirculation · 2023Observational
- Prediction of axillary lymph node metastasis in early breast cancer patients with ultrasonic videos based deep learning.Frontiers in oncology · 2023Article
- Ultrasonography and clinicopathological features of breast cancer in predicting axillary lymph node metastases.BMC cancer · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Among patients with a preoperative positive axillary ultrasound, around 40% of them are pathologically proved to be free from axillary lymph node (ALN) metastasis. We aimed to develop and validate a model to predict the probability of ALN metastasis as a preoperative tool to support clinical decision-making. Clinicopathological features of 322 early breast cancer patients with positive axillary ultrasound findings were analyzed. Multivariate logistic regression analysis was performed to identify independent predictors of ALN metastasis. A model was created from the logistic regression analysis, comprising lymph node transverse diameter, cortex thickness, hilum status, clinical tumour size, histological grade and estrogen receptor, and it was subsequently validated in another 234 patients. Coefficient of determination (R(2)) and the area under the ROC curve (AUC) were calculated to be 0.9375 and 0.864, showing good calibration and discrimination of the model, respectively. The false-negative rates of the model were 0% and 5.3% for the predicted probability cut-off points of 7.1% and 13.8%, respectively. This means that omission of axillary surgery may be safe for patients with a predictive probability of less than 13.8%. After further validation in clinical practice, this model may support increasingly limited surgical approaches to the axilla in breast cancer.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.