ArticleBMC medical informatics and decision making2023
The prediction of distant metastasis risk for male breast cancer patients based on an interpretable machine learning model.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Machine learning-based prediction of survival in breast cancer patients with lung metastasis: a SEER-based study.Translational cancer research · 2026Article
- Development and validation of an online prognostic risk stratification tool to assist decisions on perioperative chemotherapy for men with luminal early breast cancer.Breast cancer research : BCR · 2026Article
- Machine learning-based prognostic modeling and surgical value analysis of de novo metastatic invasive ductal carcinoma of the breast.Updates in surgery · 2026Article
- Hotspots and future trends of male breast cancer: a global perspective.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review.Cancer innovation · 2024Article
- Prediction models for postoperative recurrence of non-lactating mastitis based on machine learning.BMC medical informatics and decision making · 2024Article
- Predicting mortality and recurrence in colorectal cancer: Comparative assessment of predictive models.Heliyon · 2024Article
- Development and validation of AI models using LR and LightGBM for predicting distant metastasis in breast cancer: a dual-center study.Frontiers in oncology · 2024Article
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Abstract
objectivesThis research was designed to compare the ability of different machine learning (ML) models and nomogram to predict distant metastasis in male breast cancer (MBC) patients and to interpret the optimal ML model by SHapley Additive exPlanations (SHAP) framework.
methodsFour powerful ML models were developed using data from male breast cancer (MBC) patients in the SEER database between 2010 and 2015 and MBC patients from our hospital between 2010 and 2020. The area under curve (AUC) and Brier score were used to assess the capacity of different models. The Delong test was applied to compare the performance of the models. Univariable and multivariable analysis were conducted using logistic regression.
resultsOf 2351 patients were analyzed; 168 (7.1%) had distant metastasis (M1); 117 (5.0%) had bone metastasis, and 71 (3.0%) had lung metastasis. The median age at diagnosis is 68.0 years old. Most patients did not receive radiotherapy (1723, 73.3%) or chemotherapy (1447, 61.5%). The XGB model was the best ML model for predicting M1 in MBC patients. It showed the largest AUC value in the tenfold cross validation (AUC:0.884; SD:0.02), training (AUC:0.907; 95% CI: 0.899-0.917), testing (AUC:0.827; 95% CI: 0.802-0.857) and external validation (AUC:0.754; 95% CI: 0.739-0.771) sets. It also showed powerful ability in the prediction of bone metastasis (AUC: 0.880, 95% CI: 0.856-0.903 in the training set; AUC: 0.823, 95% CI:0.790-0.848 in the test set; AUC: 0.747, 95% CI: 0.727-0.764 in the external validation set) and lung metastasis (AUC: 0.906, 95% CI: 0.877-0.928 in training set; AUC: 0.859, 95% CI: 0.816-0.891 in the test set; AUC: 0.756, 95% CI: 0.732-0.777 in the external validation set). The AUC value of the XGB model was larger than that of nomogram in the training (0.907 vs 0.802) and external validation (0.754 vs 0.706) sets.
conclusionsThe XGB model is a better predictor of distant metastasis among MBC patients than other ML models and nomogram; furthermore, the XGB model is a powerful model for predicting bone and lung metastasis. Combining with SHAP values, it could help doctors intuitively understand the impact of each variable on outcome.
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