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Abstract：
BACKGROUND: The estrogen receptor (ER) serves as a pivotal indicator for assessing endocrine therapy efficacy and breast cancer prognosis. Invasive biopsy is a conventional approach for appraising ER expression levels, but it bears disadvantages due to tumor heterogeneity. To address the issue, a deep learning model leveraging mammography images was developed in this study for accurate evaluation of ER status in patients with breast cancer.
OBJECTIVE: To predict the ER status in breast cancer patients with a newly developed deep learning model leveraging mammography images.
METHODS: Datasets comprising preoperative mammography images, ER expression levels, and clinical data spanning from October 2016 to October 2021 were retrospectively collected from 358 patients diagnosed with invasive ductal carcinoma. Following collection, these datasets were divided into a training dataset (n = 257) and a testing dataset (n = 101). Subsequently, a deep learning prediction model, referred to as IP-SE-DResNet model, was developed utilizing two deep residual networks along with the Squeeze-and-Excitation attention mechanism. This model was tailored to forecast the ER status in breast cancer patients utilizing mammography images from both craniocaudal view and mediolateral oblique view. Performance measurements including prediction accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curves (AUCs) were employed to assess the effectiveness of the model.
RESULTS: In the training dataset, the AUCs for the IP-SE-DResNet model utilizing mammography images from the craniocaudal view, mediolateral oblique view, and the combined images from both views, were 0.849 (95% CIs: 0.809-0.868), 0.858 (95% CIs: 0.813-0.872), and 0.895 (95% CIs: 0.866-0.913), respectively. Correspondingly, the AUCs for these three image categories in the testing dataset were 0.835 (95% CIs: 0.790-0.887), 0.746 (95% CIs: 0.793-0.889), and 0.886 (95% CIs: 0.809-0.934), respectively. A comprehensive comparison between performance measurements underscored a substantial enhancement achieved by the proposed IP-SE-DResNet model in contrast to a traditional radiomics model employing the naive Bayesian classifier. For the latter, the AUCs stood at only 0.614 (95% CIs: 0.594-0.638) in the training dataset and 0.613 (95% CIs: 0.587-0.654) in the testing dataset, both utilizing a combination of mammography images from the craniocaudal and mediolateral oblique views.
CONCLUSIONS: The proposed IP-SE-DResNet model presents a potent and non-invasive approach for predicting ER status in breast cancer patients, potentially enhancing the efficiency and diagnostic precision of radiologists.
OBJECTIVE: To predict the ER status in breast cancer patients with a newly developed deep learning model leveraging mammography images.
METHODS: Datasets comprising preoperative mammography images, ER expression levels, and clinical data spanning from October 2016 to October 2021 were retrospectively collected from 358 patients diagnosed with invasive ductal carcinoma. Following collection, these datasets were divided into a training dataset (n = 257) and a testing dataset (n = 101). Subsequently, a deep learning prediction model, referred to as IP-SE-DResNet model, was developed utilizing two deep residual networks along with the Squeeze-and-Excitation attention mechanism. This model was tailored to forecast the ER status in breast cancer patients utilizing mammography images from both craniocaudal view and mediolateral oblique view. Performance measurements including prediction accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curves (AUCs) were employed to assess the effectiveness of the model.
RESULTS: In the training dataset, the AUCs for the IP-SE-DResNet model utilizing mammography images from the craniocaudal view, mediolateral oblique view, and the combined images from both views, were 0.849 (95% CIs: 0.809-0.868), 0.858 (95% CIs: 0.813-0.872), and 0.895 (95% CIs: 0.866-0.913), respectively. Correspondingly, the AUCs for these three image categories in the testing dataset were 0.835 (95% CIs: 0.790-0.887), 0.746 (95% CIs: 0.793-0.889), and 0.886 (95% CIs: 0.809-0.934), respectively. A comprehensive comparison between performance measurements underscored a substantial enhancement achieved by the proposed IP-SE-DResNet model in contrast to a traditional radiomics model employing the naive Bayesian classifier. For the latter, the AUCs stood at only 0.614 (95% CIs: 0.594-0.638) in the training dataset and 0.613 (95% CIs: 0.587-0.654) in the testing dataset, both utilizing a combination of mammography images from the craniocaudal and mediolateral oblique views.
CONCLUSIONS: The proposed IP-SE-DResNet model presents a potent and non-invasive approach for predicting ER status in breast cancer patients, potentially enhancing the efficiency and diagnostic precision of radiologists.
摘要：
背景：雌激素受体（ER）是评估内分泌治疗疗效和乳腺癌预后的关键指标。侵入性活检是评估ER表达水平的常规方法，但由于肿瘤异质性，它具有缺点。为了解决这个问题，本研究开发了一种利用乳腺X线摄影图像的深度学习模型,用于准确评估乳腺癌患者的ER状态.
目的：利用新开发的深度学习模型利用乳房X线摄影图像预测乳腺癌患者的ER状态。
方法：包含术前乳房X线照相术图像的数据集，ER表达水平,回顾性收集了358例诊断为浸润性导管癌的患者2016年10月至2021年10月的临床数据.收集之后，这些数据集分为训练数据集(n=257)和测试数据集(n=101).随后，深度学习预测模型，称为IP-SE-DResNet模型，是利用两个深度残差网络以及挤压和激励注意机制开发的。该模型旨在利用颅尾视图和中侧斜视图的乳房X线摄影图像来预测乳腺癌患者的ER状态。性能测量，包括预测准确性，灵敏度,特异性，和受试者工作特征曲线下面积（AUC）用于评估模型的有效性。
结果：在训练数据集中，IP-SE-DResNet模型的AUC利用来自头尾视图的乳房X线照相术图像，中外侧斜视图,和来自两个视图的组合图像，为0.849（95%CI：0.809-0.868），0.858（95%CI：0.813-0.872），和0.895（95%CIs：0.866-0.913），分别。相应地,测试数据集中这三个图像类别的AUC为0.835(95%CIs：0.790-0.887)，0.746（95%CIs：0.793-0.889），和0.886（95%CIs：0.809-0.934），分别。性能测量之间的综合比较强调了与采用朴素贝叶斯分类器的传统影像组学模型相比，所提出的IP-SE-DResNet模型实现了实质性增强。对于后者,训练数据集中的AUC仅为0.614(95%CIs：0.594-0.638)，测试数据集中为0.613(95%CIs：0.587-0.654)，两者都利用了来自头尾和中外侧倾斜视图的乳房X线照相术图像的组合。
结论：提出的IP-SE-DResNet模型为预测乳腺癌患者的ER状态提供了一种有效且非侵入性的方法，有可能提高放射科医生的效率和诊断精度。
目的：利用新开发的深度学习模型利用乳房X线摄影图像预测乳腺癌患者的ER状态。
方法：包含术前乳房X线照相术图像的数据集，ER表达水平,回顾性收集了358例诊断为浸润性导管癌的患者2016年10月至2021年10月的临床数据.收集之后，这些数据集分为训练数据集(n=257)和测试数据集(n=101).随后，深度学习预测模型，称为IP-SE-DResNet模型，是利用两个深度残差网络以及挤压和激励注意机制开发的。该模型旨在利用颅尾视图和中侧斜视图的乳房X线摄影图像来预测乳腺癌患者的ER状态。性能测量，包括预测准确性，灵敏度,特异性，和受试者工作特征曲线下面积（AUC）用于评估模型的有效性。
结果：在训练数据集中，IP-SE-DResNet模型的AUC利用来自头尾视图的乳房X线照相术图像，中外侧斜视图,和来自两个视图的组合图像，为0.849（95%CI：0.809-0.868），0.858（95%CI：0.813-0.872），和0.895（95%CIs：0.866-0.913），分别。相应地,测试数据集中这三个图像类别的AUC为0.835(95%CIs：0.790-0.887)，0.746（95%CIs：0.793-0.889），和0.886（95%CIs：0.809-0.934），分别。性能测量之间的综合比较强调了与采用朴素贝叶斯分类器的传统影像组学模型相比，所提出的IP-SE-DResNet模型实现了实质性增强。对于后者,训练数据集中的AUC仅为0.614(95%CIs：0.594-0.638)，测试数据集中为0.613(95%CIs：0.587-0.654)，两者都利用了来自头尾和中外侧倾斜视图的乳房X线照相术图像的组合。
结论：提出的IP-SE-DResNet模型为预测乳腺癌患者的ER状态提供了一种有效且非侵入性的方法，有可能提高放射科医生的效率和诊断精度。
