背景:通过乳房X线检查进行乳腺癌筛查对于早期发现至关重要,然而,对乳房X线照相术服务的需求超过了放射科医生的能力。人工智能(AI)可以帮助评估乳房X线照相术上的微钙化。我们开发并测试了用于定位和表征微钙化的AI模型。
方法:三位放射科专家使用基于组织学的地面实况注释了乳房X线照片的数据集。数据集被分区用于训练,验证,和测试。三个神经网络(AlexNet,ResNet18和ResNet34)使用特定指标进行训练和评估,包括接收器工作特征曲线下面积(AUC),灵敏度,和特异性。在测试集(整个数据集的10%)上计算报告的度量。
结果:数据集包括1,000名21-73岁的患者和1,986次乳房X线照片(180密度A,220密度B,密度380℃,和220密度D),389个恶性和611个良性组的微钙化。AlexNet以0.98的灵敏度实现了最佳性能,0.89特异性,微钙化检测的AUC为0.98,灵敏度为0.85,0.89特异性,微钙化分类的AUC为0.94。对于微钙化检测,ResNet18和ResNet34实现了0.96和0.97的灵敏度,0.91和0.90特异性和0.98和0.98AUC,回顾性。对于微钙化分类,ResNet18和ResNet34表现出0.75和0.84的灵敏度,0.85和0.84特异性,和0.88和0.92AUC,分别。
结论:开发的AI模型可以准确地检测和表征乳房X线照相术中的微钙化。
结论:基于AI的系统有可能帮助放射科医生解释乳房X线照片上的微钙化。该研究强调了开发可能应用于乳腺癌筛查的可靠深度学习模型的重要性。
结论:•开发并测试了一种新颖的AI工具,以通过准确检测和表征微钙化来帮助放射科医生解释乳房X线照相术。•三个神经网络(AlexNet,ResNet18和ResNet34)接受了培训,已验证,并使用1,000名患者和1,986名乳房X线照片的注释数据集进行测试。•AI工具在乳房X线照相术中检测/定位和表征微钙化方面表现出很高的准确性,强调基于人工智能的系统的潜力,以协助放射科医生在乳房X线照片的解释。
Breast cancer screening through mammography is crucial for early detection, yet the demand for mammography services surpasses the capacity of radiologists. Artificial intelligence (AI) can assist in evaluating
microcalcifications on mammography. We developed and tested an AI model for localizing and characterizing
microcalcifications.
Three expert radiologists annotated a dataset of mammograms using histology-based ground truth. The dataset was partitioned for training, validation, and testing. Three neural networks (AlexNet, ResNet18, and ResNet34) were trained and evaluated using specific metrics including receiver operating characteristics area under the curve (AUC), sensitivity, and specificity. The reported metrics were computed on the test set (10% of the whole dataset).
The dataset included 1,000 patients aged 21-73 years and 1,986 mammograms (180 density A, 220 density B, 380 density C, and 220 density D), with 389 malignant and 611 benign groups of microcalcifications. AlexNet achieved the best performance with 0.98 sensitivity, 0.89 specificity of, and 0.98 AUC for microcalcifications detection and 0.85 sensitivity, 0.89 specificity, and 0.94 AUC of for microcalcifications classification. For
microcalcifications detection, ResNet18 and ResNet34 achieved 0.96 and 0.97 sensitivity, 0.91 and 0.90 specificity and 0.98 and 0.98 AUC, retrospectively. For microcalcifications classification, ResNet18 and ResNet34 exhibited 0.75 and 0.84 sensitivity, 0.85 and 0.84 specificity, and 0.88 and 0.92 AUC, respectively.
The developed AI models accurately detect and characterize
microcalcifications on mammography.
AI-based systems have the potential to assist radiologists in interpreting
microcalcifications on mammograms. The study highlights the importance of developing reliable deep learning models possibly applied to breast cancer screening.
• A novel AI tool was developed and tested to aid radiologists in the interpretation of mammography by accurately detecting and characterizing microcalcifications. • Three neural networks (AlexNet, ResNet18, and ResNet34) were trained, validated, and tested using an annotated dataset of 1,000 patients and 1,986 mammograms. • The AI tool demonstrated high accuracy in detecting/localizing and characterizing microcalcifications on mammography, highlighting the potential of AI-based systems to assist radiologists in the interpretation of mammograms.