Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Department of Clinical Medicine, The First Clinical Medical College of Nanchang University, Nanchang, China.
Front Public Health. 2022 Jul 6;10:922510. doi: 10.3389/fpubh.2022.922510. eCollection 2022.
Breast cancer (BC) was the most common malignant tumor in women, and breast infiltrating ductal carcinoma (IDC) accounted for about 80% of all BC cases. BC patients who had bone metastases (BM) were more likely to have poor prognosis and bad quality of life, and earlier attention to patients at a high risk of BM was important. This study aimed to develop a predictive model based on machine learning to predict risk of BM in patients with IDC. Six different machine learning algorithms, including Logistic regression (LR), Naive Bayes classifiers (NBC), Decision tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), and Extreme gradient boosting (XGB), were used to build prediction models. The XGB model offered the best predictive performance among these 6 models in internal and external validation sets (AUC: 0.888, accuracy: 0.803, sensitivity: 0.801, and specificity: 0.837). Finally, an XGB model-based web predictor was developed to predict risk of BM in IDC patients, which may help physicians make personalized clinical decisions and treatment plans for IDC patients.
乳腺癌（BC）是女性最常见的恶性肿瘤，乳腺浸润性导管癌（IDC）约占所有 BC 病例的 80%。发生骨转移（BM）的 BC 患者预后更差，生活质量更差，因此早期关注 BM 高危患者非常重要。本研究旨在基于机器学习开发预测 IDC 患者 BM 风险的预测模型。六种不同的机器学习算法，包括逻辑回归（LR）、朴素贝叶斯分类器（NBC）、决策树（DT）、随机森林（RF）、梯度提升机（GBM）和极端梯度提升（XGB），被用于构建预测模型。在内部和外部验证集中，XGB 模型在这 6 种模型中提供了最佳的预测性能（AUC：0.888，准确率：0.803，灵敏度：0.801，特异性：0.837）。最后，开发了一个基于 XGB 模型的网络预测器，用于预测 IDC 患者的 BM 风险，这可能有助于医生为 IDC 患者制定个性化的临床决策和治疗计划。
