■这项研究旨在开发一种利用临床血液标志物的人工智能模型,超声数据,和乳腺活检病理信息来预测乳腺癌患者的远处转移。
■利用了两个医疗中心的数据,临床血液标志物,超声数据,分别提取和选择乳腺活检病理信息。使用Spearman相关和LASSO回归进行特征降维。使用LR和LightGBM机器学习算法构建预测模型,并在内部和外部验证集上进行验证。对两个模型进行了特征相关性分析。
■LR模型在训练中获得了0.892、0.816和0.817的AUC值,内部验证,和外部验证队列,分别。LightGBM模型在相同的队列中获得了0.971、0.861和0.890的AUC值,分别。临床决策曲线分析显示,LightGBM模型在预测乳腺癌远处转移方面优于LR模型。鉴定的关键特征包括肌酸激酶同工酶(CK-MB)和α-羟基丁酸脱氢酶。
■这项研究使用临床血液标志物开发了一种人工智能模型,超声数据,和病理信息来识别乳腺癌患者的远处转移。LightGBM模型表现出优越的预测准确性和临床适用性,表明它是乳腺癌远处转移的早期诊断工具。
UNASSIGNED: This
study aims to develop an artificial intelligence model utilizing clinical blood markers, ultrasound data, and breast biopsy pathological information to predict the distant metastasis in breast cancer patients.
UNASSIGNED: Data from two medical centers were utilized, Clinical blood markers, ultrasound data, and breast biopsy pathological information were separately extracted and selected. Feature dimensionality reduction was performed using Spearman correlation and LASSO regression. Predictive models were constructed using
LR and LightGBM machine learning algorithms and validated on internal and external validation sets. Feature correlation analysis was conducted for both models.
UNASSIGNED: The
LR model achieved AUC values of 0.892, 0.816, and 0.817 for the training, internal validation, and external validation cohorts, respectively. The LightGBM model achieved AUC values of 0.971, 0.861, and 0.890 for the same cohorts, respectively. Clinical decision curve analysis showed a superior net benefit of the LightGBM model over the
LR model in predicting distant metastasis in breast cancer. Key features identified included creatine kinase isoenzyme (CK-MB) and alpha-hydroxybutyrate dehydrogenase.
UNASSIGNED: This
study developed an artificial intelligence model using clinical blood markers, ultrasound data, and pathological information to identify distant metastasis in breast cancer patients. The LightGBM model demonstrated superior predictive accuracy and clinical applicability, suggesting it as a promising tool for early diagnosis of distant metastasis in breast cancer.