PDF(Pubmed)
Abstract：
OBJECTIVE: The authors aimed to establish an artificial intelligence (AI)-based method for preoperative diagnosis of breast lesions from contrast enhanced mammography (CEM) and to explore its biological mechanism.
METHODS: This retrospective study includes 1430 eligible patients who underwent CEM examination from June 2017 to July 2022 and were divided into a construction set (n=1101), an internal test set (n=196), and a pooled external test set (n=133). The AI model adopted RefineNet as a backbone network, and an attention sub-network, named convolutional block attention module (CBAM), was built upon the backbone for adaptive feature refinement. An XGBoost classifier was used to integrate the refined deep learning features with clinical characteristics to differentiate benign and malignant breast lesions. The authors further retrained the AI model to distinguish in situ and invasive carcinoma among breast cancer candidates. RNA-sequencing data from 12 patients were used to explore the underlying biological basis of the AI prediction.
RESULTS: The AI model achieved an area under the curve of 0.932 in diagnosing benign and malignant breast lesions in the pooled external test set, better than the best-performing deep learning model, radiomics model, and radiologists. Moreover, the AI model has also achieved satisfactory results (an area under the curve from 0.788 to 0.824) for the diagnosis of in situ and invasive carcinoma in the test sets. Further, the biological basis exploration revealed that the high-risk group was associated with the pathways such as extracellular matrix organization.
CONCLUSIONS: The AI model based on CEM and clinical characteristics had good predictive performance in the diagnosis of breast lesions.
METHODS: This retrospective study includes 1430 eligible patients who underwent CEM examination from June 2017 to July 2022 and were divided into a construction set (n=1101), an internal test set (n=196), and a pooled external test set (n=133). The AI model adopted RefineNet as a backbone network, and an attention sub-network, named convolutional block attention module (CBAM), was built upon the backbone for adaptive feature refinement. An XGBoost classifier was used to integrate the refined deep learning features with clinical characteristics to differentiate benign and malignant breast lesions. The authors further retrained the AI model to distinguish in situ and invasive carcinoma among breast cancer candidates. RNA-sequencing data from 12 patients were used to explore the underlying biological basis of the AI prediction.
RESULTS: The AI model achieved an area under the curve of 0.932 in diagnosing benign and malignant breast lesions in the pooled external test set, better than the best-performing deep learning model, radiomics model, and radiologists. Moreover, the AI model has also achieved satisfactory results (an area under the curve from 0.788 to 0.824) for the diagnosis of in situ and invasive carcinoma in the test sets. Further, the biological basis exploration revealed that the high-risk group was associated with the pathways such as extracellular matrix organization.
CONCLUSIONS: The AI model based on CEM and clinical characteristics had good predictive performance in the diagnosis of breast lesions.
摘要：
目的：作者旨在建立一种基于人工智能（AI）的乳腺造影（CEM）术前诊断乳腺病变的方法，并探讨其生物学机制。
方法：这项回顾性研究包括2017年6月至2022年7月接受CEM检查的1430名符合条件的患者，并分为一组构建组（n=1101）。内部测试集（n=196），和一个汇集的外部测试集(n=133)。AI模型采用RefineNet作为骨干网络，和一个注意力子网络，命名为卷积块注意模块(CBAM)，建立在自适应特征细化的主干上。使用XGBoost分类器将精细的深度学习特征与临床特征整合以区分良性和恶性乳腺病变。作者进一步重新训练了AI模型，以区分乳腺癌候选人中的原位癌和浸润性癌。来自12名患者的RNA测序数据用于探索AI预测的潜在生物学基础。
结果：AI模型在合并的外部测试集中诊断良性和恶性乳腺病变的曲线下面积为0.932，比表现最好的深度学习模型更好，影像组学模型，和放射科医生。此外，AI模型在测试集中诊断原位癌和浸润性癌方面也取得了令人满意的结果(曲线下面积为0.788~0.824).Further,生物学基础探索显示，高危人群与细胞外基质组织等途径有关。
结论：基于CEM和临床特征的AI模型在乳腺病变诊断中具有良好的预测性能。
方法：这项回顾性研究包括2017年6月至2022年7月接受CEM检查的1430名符合条件的患者，并分为一组构建组（n=1101）。内部测试集（n=196），和一个汇集的外部测试集(n=133)。AI模型采用RefineNet作为骨干网络，和一个注意力子网络，命名为卷积块注意模块(CBAM)，建立在自适应特征细化的主干上。使用XGBoost分类器将精细的深度学习特征与临床特征整合以区分良性和恶性乳腺病变。作者进一步重新训练了AI模型，以区分乳腺癌候选人中的原位癌和浸润性癌。来自12名患者的RNA测序数据用于探索AI预测的潜在生物学基础。
结果：AI模型在合并的外部测试集中诊断良性和恶性乳腺病变的曲线下面积为0.932，比表现最好的深度学习模型更好，影像组学模型，和放射科医生。此外，AI模型在测试集中诊断原位癌和浸润性癌方面也取得了令人满意的结果(曲线下面积为0.788~0.824).Further,生物学基础探索显示，高危人群与细胞外基质组织等途径有关。
结论：基于CEM和临床特征的AI模型在乳腺病变诊断中具有良好的预测性能。
