Department of Breast Surgery, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China.
Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
BMC Womens Health. 2024 Aug 5;24(1):442. doi: 10.1186/s12905-024-03264-z.
Breast cancer has become the most prevalent malignant tumor in women, and the occurrence of distant metastasis signifies a poor prognosis. Utilizing predictive models to forecast distant metastasis in breast cancer presents a novel approach. This study aims to utilize readily available clinical data and advanced machine learning algorithms to establish an accurate clinical prediction model. The overall objective is to provide effective decision support for clinicians.
Data from 239 patients from two centers were analyzed, focusing on clinical blood biomarkers (tumor markers, liver and kidney function, lipid profile, cardiovascular markers). Spearman correlation and the least absolute shrinkage and selection operator regression were employed for feature dimension reduction. A predictive model was built using LightGBM and validated in training, testing, and external validation cohorts. Feature importance correlation analysis was conducted on the clinical model and the comprehensive model, followed by univariate and multivariate regression analysis of these features.
Through internal and external validation, we constructed a LightGBM model to predict de novo bone metastasis in newly diagnosed breast cancer patients. The area under the receiver operating characteristic curve values of this model in the training, internal validation test, and external validation test1 cohorts were 0.945, 0.892, and 0.908, respectively. Our validation results indicate that the model exhibits high sensitivity, specificity, and accuracy, making it the most accurate model for predicting bone metastasis in breast cancer patients. Carcinoembryonic Antigen, creatine kinase, albumin-globulin ratio, Apolipoprotein B, and Cancer Antigen 153 (CA153) play crucial roles in the model's predictions. Lipoprotein a, CA153, gamma-glutamyl transferase, α-Hydroxybutyrate dehydrogenase, alkaline phosphatase, and creatine kinase are positively correlated with breast cancer bone metastasis, while white blood cell ratio and total cholesterol are negatively correlated.
This study successfully utilized clinical blood biomarkers to construct an artificial intelligence model for predicting distant metastasis in breast cancer, demonstrating high accuracy. This suggests potential clinical utility in predicting and identifying distant metastasis in breast cancer. These findings underscore the potential prospect of developing economically efficient and readily accessible predictive tools in clinical oncology.
乳腺癌已成为女性最常见的恶性肿瘤，远处转移的发生预示着预后不良。利用预测模型预测乳腺癌远处转移是一种新的方法。本研究旨在利用易于获得的临床数据和先进的机器学习算法建立准确的临床预测模型。总体目标是为临床医生提供有效的决策支持。
分析了来自两个中心的 239 名患者的数据，重点是临床血液生物标志物（肿瘤标志物、肝肾功能、血脂谱、心血管标志物）。采用 Spearman 相关系数和最小绝对收缩和选择算子回归进行特征降维。使用 LightGBM 构建预测模型，并在训练、测试和外部验证队列中进行验证。对临床模型和综合模型进行特征重要性相关性分析，然后对这些特征进行单变量和多变量回归分析。
通过内部和外部验证，我们构建了一个 LightGBM 模型来预测新诊断的乳腺癌患者中骨转移的发生。该模型在训练、内部验证测试和外部验证测试 1 队列中的受试者工作特征曲线下面积值分别为 0.945、0.892 和 0.908。我们的验证结果表明，该模型具有较高的灵敏度、特异性和准确性，是预测乳腺癌患者骨转移最准确的模型。癌胚抗原、肌酸激酶、白蛋白-球蛋白比值、载脂蛋白 B 和癌抗原 153（CA153）在模型预测中起关键作用。脂蛋白 a、CA153、γ-谷氨酰转移酶、α-羟丁酸脱氢酶、碱性磷酸酶和肌酸激酶与乳腺癌骨转移呈正相关，而白细胞比值和总胆固醇与乳腺癌骨转移呈负相关。
本研究成功地利用临床血液生物标志物构建了人工智能模型来预测乳腺癌远处转移，具有较高的准确性。这表明在预测和识别乳腺癌远处转移方面具有潜在的临床应用价值。这些发现突显了在临床肿瘤学中开发经济高效且易于获取的预测工具的潜在前景。
