ArticleBMC medical informatics and decision making2012
Prediction of axillary lymph node metastasis in primary breast cancer patients using a decision tree-based model.
Article in BMC medical informatics and decision making, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- A non-invasive preoperative model for predicting sentinel lymph node metastasis in breast cancer using clinical data and MRI.BMC medical imaging · 2025Article
- Identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics.Scientific reports · 2024Article
- Assessment of the axilla in women with early-stage breast cancer undergoing primary surgery: a review.World journal of surgical oncology · 2024Review
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- Salivary metabolomics with machine learning for colorectal cancer detection.Cancer science · 2022Article
- Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study.Frontiers in cardiovascular medicine · 2022Article
- A meta-analysis of the diagnostic performance of machine learning-based MRI in the prediction of axillary lymph node metastasis in breast cancer patients.Insights into imaging · 2021Review
- Predictability of postoperative recurrence on hepatocellular carcinoma through data mining method.Molecular and clinical oncology · 2020Article
- Accurate and rapid screening model for potential diabetes mellitus.BMC medical informatics and decision making · 2019Article
- Establishment and Verification of a Bagged-Trees-Based Model for Prediction of Sentinel Lymph Node Metastasis for Early Breast Cancer Patients.Frontiers in oncology · 2019Article
- Urinary Polyamine Biomarker Panels with Machine-Learning Differentiated Colorectal Cancers, Benign Disease, and Healthy Controls.International journal of molecular sciences · 2018Article
- A Statistical Classifier to Support Diagnose Meningitis in Less Developed Areas of Brazil.Journal of medical systems · 2017Article
- A nomogram to predict the probability of axillary lymph node metastasis in early breast cancer patients with positive axillary ultrasound.Scientific reports · 2016Article
- Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT.Bone marrow transplantation · 2014Review
- Association of tumour stiffness on sonoelastography with axillary nodal status in T1 breast carcinoma patients.European radiology · 2013Article
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Authors and funding
12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe aim of this study was to develop a new data-mining model to predict axillary lymph node (AxLN) metastasis in primary breast cancer. To achieve this, we used a decision tree-based prediction method-the alternating decision tree (ADTree).
methodsClinical datasets for primary breast cancer patients who underwent sentinel lymph node biopsy or AxLN dissection without prior treatment were collected from three institutes (institute A, n = 148; institute B, n = 143; institute C, n = 174) and were used for variable selection, model training and external validation, respectively. The models were evaluated using area under the receiver operating characteristics (ROC) curve analysis to discriminate node-positive patients from node-negative patients.
resultsThe ADTree model selected 15 of 24 clinicopathological variables in the variable selection dataset. The resulting area under the ROC curve values were 0.770 [95% confidence interval (CI), 0.689-0.850] for the model training dataset and 0.772 (95% CI: 0.689-0.856) for the validation dataset, demonstrating high accuracy and generalization ability of the model. The bootstrap value of the validation dataset was 0.768 (95% CI: 0.763-0.774).
conclusionsOur prediction model showed high accuracy for predicting nodal metastasis in patients with breast cancer using commonly recorded clinical variables. Therefore, our model might help oncologists in the decision-making process for primary breast cancer patients before starting treatment.
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