ArticleArchives of medical science : AMS2017
Application of artificial neural networks for predicting presence of non-sentinel lymph node metastases in breast cancer patients with positive sentinel lymph node biopsies.
Article in Archives of medical science : AMS, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Diagnostic Performance of Machine Learning-Based Radiomics of DCE-MRI in Predicting Axillary Lymph Node Metastasis in Breast Cancer: A Meta-Analysis.Frontiers in oncology · 2022Pooled it
- Clinical relevance of a degree of extracapsular extension in a sentinel lymph node in breast cancer patients: a single-centre study.Scientific reports · 2021Trial
- Effect of capsule invasion on recurrence and survival in axillary lymph node metastases of breast cancer.Revista da Associacao Medica Brasileira (1992) · 2026Article
- Artificial intelligence assisted ultrasound for the non-invasive prediction of axillary lymph node metastasis in breast cancer.BMC cancer · 2024Article
- Prediction of sentinel lymph node metastasis in breast cancer patients based on preoperative features: a deep machine learning approach.Scientific reports · 2024Article
- Iodine map histogram metrics in early-stage breast cancer: prediction of axillary lymph node metastasis status.Quantitative imaging in medicine and surgery · 2022Article
- Medullary breast cancer is a predominantly triple-negative breast cancer - histopathological analysis and comparison with invasive ductal breast cancer.Archives of medical science : AMS · 2022Article
- Development of a prediction model based on LASSO regression to evaluate the risk of non-sentinel lymph node metastasis in Chinese breast cancer patients with 1-2 positive sentinel lymph nodes.Scientific reports · 2021Article
- Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: A systematic review and meta-analysis.EClinicalMedicine · 2021Article
- Artificial neural network models to predict nodal status in clinically node-negative breast cancer.BMC cancer · 2019Article
- Association between circulating leptin concentration andArchives of medical science : AMS · 2019Review
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
introductionThe aim of this study was to present a new predictive tool for non-sentinel lymph node (nSLN) metastases. MATERIAL AND
methodsOne thousand five hundred eighty-three patients with early-stage breast cancer were subjected to sentinel lymph node biopsy (SLNB) between 2004 and 2012. Metastatic SLNs were found in 348 patients - the retrospective group. Selective axillary lymph node dissection (ALND) was performed in 94% of cases. Involvement of the nSLNs was identified in 32.1% of patients following ALND. The correlation between nSLN involvement and selected epidemiological data, primary tumor features and details of the diagnostic and therapeutic management was examined in metastatic SLN group. Multivariate analysis was performed using an artificial neural network to create a new nomogram. The new test was validated using the overall study population consisting of the prospective group (365 patients - SLNB between 01-07.2013).
resultsAccuracy of the new test was calculated using area under the receiver operating characteristics curve (AUC). We obtained AUC coefficient equal to 0.87 (95% confidence interval: 0.81-0.92). Sensitivity amounted to 69%, specificity to 86%, accuracy - 80% (retrospective group) and 77%, 46%, 66% (validation group), respectively. The Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram the calculated AUC value was 0.71, for Stanford - 0.68, for Tenon - 0.67.
conclusionsIn the analyzed group only the MSKCC nomogram and the new model showed AUC values exceeding the expected level of 0.70. Our nomogram performs well in prospective validation on patient series. The overall assessment of clinical usefulness of this test will be possible after testing it on different patient populations.
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