Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, 530000, Guangxi Zhuang Autonomous Region, China.
Department of Breast Surgery, Guangxi Medical University Tumor Hospital, 71 Hedi Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, China.
Sci Rep. 2024 Jul 6;14(1):15561. doi: 10.1038/s41598-024-66658-x.
Breast cancer metastasis significantly impacts women's health globally. This study aimed to construct predictive models using clinical blood markers and ultrasound data to predict distant metastasis in breast cancer patients, ensuring clinical applicability, cost-effectiveness, relative non-invasiveness, and accessibility of these models. Analysis was conducted on data from 416 patients across two centers, focusing on clinical blood markers (tumor markers, liver and kidney function indicators, blood lipid markers, cardiovascular biomarkers) and maximum lesion diameter from ultrasound. Feature reduction was performed using Spearman correlation and LASSO regression. Two models were built using LightGBM: a clinical model (using clinical blood markers) and a combined model (incorporating clinical blood markers and ultrasound features), validated in training, internal test, and external validation (test1) cohorts. Feature importance analysis was conducted for both models, followed by univariate and multivariate regression analyses of these features. The AUC values of the clinical model in the training, internal test, and external validation (test1) cohorts were 0.950, 0.795, and 0.883, respectively. The combined model showed AUC values of 0.955, 0.835, and 0.918 in the training, internal test, and external validation (test1) cohorts, respectively. Clinical utility curve analysis indicated the combined model's superior net benefit in identifying breast cancer with distant metastasis across all cohorts. This suggests the combined model's superior discriminatory ability and strong generalization performance. Creatine kinase isoenzyme (CK-MB), CEA, CA153, albumin, creatine kinase, and maximum lesion diameter from ultrasound played significant roles in model prediction. CA153, CK-MB, lipoprotein (a), and maximum lesion diameter from ultrasound positively correlated with breast cancer distant metastasis, while indirect bilirubin and magnesium ions showed negative correlations. This study successfully utilized clinical blood markers and ultrasound data to develop AI models for predicting distant metastasis in breast cancer. The combined model, incorporating clinical blood markers and ultrasound features, exhibited higher accuracy, suggesting its potential clinical utility in predicting and identifying breast cancer distant metastasis. These findings highlight the potential prospects of developing cost-effective and accessible predictive tools in clinical oncology.
乳腺癌转移显著影响全球女性健康。本研究旨在构建使用临床血液标志物和超声数据预测乳腺癌患者远处转移的预测模型，确保这些模型具有临床适用性、成本效益、相对非侵入性和可及性。分析基于来自两个中心的 416 名患者的数据，重点关注临床血液标志物（肿瘤标志物、肝肾功能指标、血脂标志物、心血管生物标志物）和超声的最大病变直径。使用 Spearman 相关性和 LASSO 回归进行特征降维。使用 LightGBM 构建了两个模型：一个是临床模型（使用临床血液标志物），另一个是组合模型（包含临床血液标志物和超声特征），在训练、内部测试和外部验证（测试 1）队列中进行验证。对两个模型进行特征重要性分析，然后对这些特征进行单变量和多变量回归分析。临床模型在训练、内部测试和外部验证（测试 1）队列中的 AUC 值分别为 0.950、0.795 和 0.883。组合模型在训练、内部测试和外部验证（测试 1）队列中的 AUC 值分别为 0.955、0.835 和 0.918。临床实用曲线分析表明，在所有队列中，组合模型在识别具有远处转移的乳腺癌方面具有更高的净收益。这表明组合模型具有更好的判别能力和强大的泛化性能。肌酸激酶同工酶（CK-MB）、癌胚抗原（CEA）、CA153、白蛋白、肌酸激酶和超声的最大病变直径在模型预测中起重要作用。CA153、CK-MB、脂蛋白（a）和超声的最大病变直径与乳腺癌远处转移呈正相关，而间接胆红素和镁离子呈负相关。本研究成功利用临床血液标志物和超声数据开发了用于预测乳腺癌远处转移的人工智能模型。组合模型，结合临床血液标志物和超声特征，表现出更高的准确性，表明其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具有成本效益和可及性的预测工具的潜在前景。
