ArticleScientific reports2022
Construction and validation of a risk prediction model for clinical axillary lymph node metastasis in T1-2 breast cancer.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- A comparative analysis of three graph neural network models for predicting axillary lymph node metastasis in early-stage breast cancer.Scientific reports · 2025Article
- Characteristics and risk factors of axillary lymph node metastasis of microinvasive breast cancer.Breast cancer research and treatment · 2024Article
- Identifying Key Genes Involved in Axillary Lymph Node Metastasis in Breast Cancer Using Advanced RNA-Seq Analysis: A Methodological Approach with GLMQL and MAS.International journal of molecular sciences · 2024Article
- Incorporating ultrasound-based lymph node staging significantly improves the performance of a clinical nomogram for predicting preoperative axillary lymph node metastasis in breast cancer.Biomolecules & biomedicine · 2023Article
- 3D reconstruction based novel methods are more effective than traditional clinical assessment in breast cancer axillary lymph node metastasis prediction.Scientific reports · 2022Article
- Is it time to retire sentinel lymph node biopsy and use multi-omics prediction models?Annals of translational medicine · 2022Article
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
The current diagnostic technologies for assessing the axillary lymph node metastasis (ALNM) status accurately in breast cancer (BC) remain unsatisfactory. Here, we developed a diagnostic model for evaluating the ALNM status using a combination of mRNAs and the T stage of the primary tumor as a novel biomarker. We collected relevant information on T1-2 BC from public databases. An ALNM prediction model was developed by logistic regression based on the screened signatures and then internally and externally validated. Calibration curves and the area under the curve (AUC) were employed as performance metrics. The prognostic value and tumor immune infiltration of the model were also determined. An optimal diagnostic model was created using a combination of 11 mRNAs and T stage of the primary tumor and showed high discrimination, with AUCs of 0.828 and 0.746 in the training sets. AUCs of 0.671 and 0.783 were achieved in the internal validation cohorts. The mean external AUC value was 0.686 and ranged between 0.644 and 0.742. Moreover, the new model has good specificity in T1 and hormone receptor-negative/human epidermal growth factor receptor 2- negative (HR-/HER2-) BC and good sensitivity in T2 BC. In addition, the risk of ALNM and 11 mRNAs were correlated with the infiltration of M2 macrophages, as well as the prognosis of BC. This novel prediction model is a useful tool to identify the risk of ALNM in T1-2 BC patients, particularly given that it can be used to adjust surgical options in the future.
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