PDF(Pubmed)
Abstract：
BACKGROUND: The most common route of breast cancer metastasis is through the mammary lymphatic network. An accurate assessment of the axillary lymph node (ALN) burden before surgery can avoid unnecessary axillary surgery, consequently preventing surgical complications. In this study, we aimed to develop a non-invasive prediction model incorporating breast specific gamma image (BSGI) features and ultrasonographic parameters to assess axillary lymph node status.
METHODS: Cohorts of breast cancer patients who underwent surgery between 2012 and 2021 were created (The training set included 1104 ultrasound images and 940 BSGI images from 235 patients, the test set included 568 ultrasound images and 296 BSGI images from 99 patients) for the development of the prediction model. six machine learning (ML) methods and recursive feature elimination were trained in the training set to create a strong prediction model. Based on the best-performing model, we created an online calculator that can make a linear predictor in patients easily accessible to clinicians. The receiver operating characteristic (ROC) and calibration curve are used to verify the model performance respectively and evaluate the clinical effectiveness of the model.
RESULTS: Six ultrasonographic parameters (transverse diameter of tumour, longitudinal diameter of tumour, lymphatic echogenicity, transverse diameter of lymph nodes, longitudinal diameter of lymph nodes, lymphatic color Doppler flow imaging grade) and one BSGI features (axillary mass status) were selected based on the best-performing model. In the test set, the support vector machines\' model showed the best predictive ability (AUC = 0.794, sensitivity = 0.641, specificity = 0.8, PPV = 0.676, NPV = 0.774 and accuracy = 0.737). An online calculator was established for clinicians to predict patients\' risk of ALN metastasis ( https://wuqian.shinyapps.io/shinybsgi/ ). The result in ROC showed the model could benefit from incorporating BSGI feature.
CONCLUSIONS: This study developed a non-invasive prediction model that incorporates variables using ML method and serves to clinically predict ALN metastasis and help in selection of the appropriate treatment option.
METHODS: Cohorts of breast cancer patients who underwent surgery between 2012 and 2021 were created (The training set included 1104 ultrasound images and 940 BSGI images from 235 patients, the test set included 568 ultrasound images and 296 BSGI images from 99 patients) for the development of the prediction model. six machine learning (ML) methods and recursive feature elimination were trained in the training set to create a strong prediction model. Based on the best-performing model, we created an online calculator that can make a linear predictor in patients easily accessible to clinicians. The receiver operating characteristic (ROC) and calibration curve are used to verify the model performance respectively and evaluate the clinical effectiveness of the model.
RESULTS: Six ultrasonographic parameters (transverse diameter of tumour, longitudinal diameter of tumour, lymphatic echogenicity, transverse diameter of lymph nodes, longitudinal diameter of lymph nodes, lymphatic color Doppler flow imaging grade) and one BSGI features (axillary mass status) were selected based on the best-performing model. In the test set, the support vector machines\' model showed the best predictive ability (AUC = 0.794, sensitivity = 0.641, specificity = 0.8, PPV = 0.676, NPV = 0.774 and accuracy = 0.737). An online calculator was established for clinicians to predict patients\' risk of ALN metastasis ( https://wuqian.shinyapps.io/shinybsgi/ ). The result in ROC showed the model could benefit from incorporating BSGI feature.
CONCLUSIONS: This study developed a non-invasive prediction model that incorporates variables using ML method and serves to clinically predict ALN metastasis and help in selection of the appropriate treatment option.
摘要：
背景：乳腺癌转移的最常见途径是通过乳腺淋巴网络。术前准确评估腋窝淋巴结（ALN）负担可以避免不必要的腋窝手术，从而防止手术并发症。在这项研究中,我们的目的是建立一种非侵入性预测模型,该模型结合了乳腺特异性伽马图像(BSGI)特征和超声参数,以评估腋窝淋巴结状态.
方法：创建了2012年至2021年接受手术的乳腺癌患者队列（训练集包括来自235名患者的1104张超声图像和940张BSGI图像，测试集包括来自99名患者的568张超声图像和296张BSGI图像），用于开发预测模型。在训练集中训练了六种机器学习（ML）方法和递归特征消除，以创建强大的预测模型。基于最佳性能模型，我们创建了一个在线计算器,该计算器可以使临床医生容易获得患者的线性预测因子.利用受试者工作特性(ROC)和校准曲线分别验证模型性能,评价模型的临床有效性。
结果：六个超声参数（肿瘤的横向直径，肿瘤的纵向直径，淋巴回声，淋巴结横径，淋巴结纵向直径，淋巴彩色多普勒血流显像分级）和一个BSGI特征（腋窝肿块状态）是根据表现最佳的模型选择的。在测试集中，支持向量机模型显示最佳预测能力(AUC=0.794,灵敏度=0.641,特异度=0.8,PPV=0.676,NPV=0.774,准确度=0.737).为临床医生建立了一个在线计算器来预测患者ALN转移的风险（https://wuqian。shinyapps.io/shinybsgi/)。ROC的结果表明，该模型可以从结合BSGI特征中受益。
结论：本研究开发了一种非侵入性预测模型，该模型使用ML方法纳入变量，用于临床预测ALN转移并帮助选择合适的治疗方案。
方法：创建了2012年至2021年接受手术的乳腺癌患者队列（训练集包括来自235名患者的1104张超声图像和940张BSGI图像，测试集包括来自99名患者的568张超声图像和296张BSGI图像），用于开发预测模型。在训练集中训练了六种机器学习（ML）方法和递归特征消除，以创建强大的预测模型。基于最佳性能模型，我们创建了一个在线计算器,该计算器可以使临床医生容易获得患者的线性预测因子.利用受试者工作特性(ROC)和校准曲线分别验证模型性能,评价模型的临床有效性。
结果：六个超声参数（肿瘤的横向直径，肿瘤的纵向直径，淋巴回声，淋巴结横径，淋巴结纵向直径，淋巴彩色多普勒血流显像分级）和一个BSGI特征（腋窝肿块状态）是根据表现最佳的模型选择的。在测试集中，支持向量机模型显示最佳预测能力(AUC=0.794,灵敏度=0.641,特异度=0.8,PPV=0.676,NPV=0.774,准确度=0.737).为临床医生建立了一个在线计算器来预测患者ALN转移的风险（https://wuqian。shinyapps.io/shinybsgi/)。ROC的结果表明，该模型可以从结合BSGI特征中受益。
结论：本研究开发了一种非侵入性预测模型，该模型使用ML方法纳入变量，用于临床预测ALN转移并帮助选择合适的治疗方案。
