Abstract：
UNASSIGNED: The objective of the study was to use a deep learning model to differentiate between benign and malignant sentinel lymph nodes (SLNs) in patients with breast cancer compared to radiologists\' assessments.Seventy-nine women with breast cancer were enrolled and underwent lymphosonography and contrast-enhanced ultrasound (CEUS) examination after subcutaneous injection of ultrasound contrast agent around their tumor to identify SLNs. Google AutoML was used to develop image classification model. Grayscale and CEUS images acquired during the ultrasound examination were uploaded with a data distribution of 80% for training/20% for testing. The performance metric used was area under precision/recall curve (AuPRC). In addition, 3 radiologists assessed SLNs as normal or abnormal based on a clinical established classification. Two-hundred seventeen SLNs were divided in 2 for model development; model 1 included all SLNs and model 2 had an equal number of benign and malignant SLNs. Validation results model 1 AuPRC 0.84 (grayscale)/0.91 (CEUS) and model 2 AuPRC 0.91 (grayscale)/0.87 (CEUS). The comparison between artificial intelligence (AI) and readers\' showed statistical significant differences between all models and ultrasound modes; model 1 grayscale AI versus readers, P = 0.047, and model 1 CEUS AI versus readers, P < 0.001. Model 2 r grayscale AI versus readers, P = 0.032, and model 2 CEUS AI versus readers, P = 0.041.The interreader agreement overall result showed κ values of 0.20 for grayscale and 0.17 for CEUS.In conclusion, AutoML showed improved diagnostic performance in balance volume datasets. Radiologist performance was not influenced by the dataset\'s distribution.
摘要：
■该研究的目的是使用深度学习模型来区分乳腺癌患者的良性和恶性前哨淋巴结（SLN），与放射科医生的评估相比。纳入79例乳腺癌患者，在其肿瘤周围皮下注射超声造影剂以鉴定SLN后，进行了淋巴超声造影和超声造影（CEUS）检查。GoogleAutoML用于开发图像分类模型。在超声检查期间采集的灰度和CEUS图像被上传,其中80%的数据分布用于训练/20%用于测试。使用的性能度量是精确度/召回曲线下面积(AuPRC)。此外,3个放射科医师基于临床建立的分类将SLN评估为正常或异常。将两百十七个SLN分为2个用于模型开发；模型1包括所有SLN，模型2具有相同数量的良性和恶性SLN。验证结果模型1AuPRC0.84(灰度)/0.91(CEUS)和模型2AuPRC0.91(灰度)/0.87(CEUS)。人工智能（AI）和阅读器之间的比较表明，所有模型和超声模式之间存在统计学上的显着差异；模型1灰度AI与阅读器，P=0.047，模型1CEUSAI与读者，P<0.001。模型2r灰度AI与阅读器，P=0.032，模型2CEUSAI与读者，P=0.041。读者一致的总体结果显示，灰度的κ值为0.20，CEUS的κ值为0.17。总之,AutoML在平衡卷数据集中显示出改进的诊断性能。放射科医师的表现不受数据集分布的影响。
