背景:手术期间,通过活检确定的导管原位癌(DCIS)的风险通常会增加。因此,术前确认DCIS分级对于临床决策是必要的.
目的:训练基于超声(US)的三分类深度学习(DL)模型,结合临床数据,乳房X线照相术(MG),US,和核心针活检(CNB)病理来预测低级别DCIS,中高档DCIS,并升级DCIS。
方法:回顾性收集2013年5月至2022年6月(N1)733例经活检证实的DCIS患者的资料。和其他数据(N2)通过活检证实为低级别DCIS。将病变随机分为训练(n=471),验证(n=142),和测试(n=141)设置以建立DCIS-Net。DCIS-Net上的信息,临床(年龄和体征),美国(大小,钙化,type,乳腺影像报告和数据系统[BI-RADS]),MG(微钙化,BI-RADS),和CNB病理学(核级别,建筑特征,和免疫组织化学)被收集。进行了Logistic回归和随机森林分析,以开发多模态DCIS-Net来计算特异性,灵敏度,准确度,接收机工作特性曲线,和曲线下面积(AUC)。
结果:在N1的测试集中,三分类任务中多模态DCIS-Net的准确性和AUC分别为0.752-0.766和0.859-0.907,分别。区分DCIS和升级DCIS的准确性和AUC分别为0.751-0.780和0.829-0.861。在N2测试集中,区分低等级DCIS和升级低等级DCIS的准确性和AUC分别为0.769-0.987和0.818-0.939。在多模态DCIS-Net中,DL的功能重要性从1到5排名。
结论:通过开发DCIS-Net并将其与多模态信息集成,诊断低等级DCIS,中高档DCIS,和升级DCIS是可能的。它还可以用于区分DCIS与升级DCIS以及低等级DCIS与升级低等级DCIS,这可以为DCIS临床工作流程铺平道路。
BACKGROUND: The risk of ductal carcinoma in situ (DCIS) identified by biopsy often increases during surgery. Therefore, confirming the DCIS grade preoperatively is necessary for clinical decision-making.
OBJECTIVE: To train a three-classification deep learning (DL) model based on ultrasound (US), combining clinical data, mammography (MG), US, and core needle biopsy (CNB) pathology to predict low-grade DCIS, intermediate-to-high-grade DCIS, and upstaged DCIS.
METHODS: Data of 733 patients with 754 DCIS cases confirmed by biopsy were retrospectively collected from May 2013 to June 2022 (N1), and other data (N2) were confirmed by biopsy as low-grade DCIS. The lesions were randomly divided into training (n=471), validation (n=142), and test (n = 141) sets to establish the DCIS-Net. Information on the DCIS-Net, clinical (age and sign), US (size, calcifications, type, breast imaging reporting and data system [BI-RADS]), MG (microcalcifications, BI-RADS), and CNB pathology (nuclear grade, architectural features, and immunohistochemistry) were collected. Logistic regression and random forest analyses were conducted to develop Multimodal DCIS-Net to calculate the specificity, sensitivity, accuracy, receiver operating characteristic curve, and area under the curve (AUC).
RESULTS: In the test set of N1, the accuracy and AUC of the multimodal DCIS-Net were 0.752-0.766 and 0.859-0.907 in the three-classification task, respectively. The accuracy and AUC for discriminating DCIS from upstaged DCIS were 0.751-0.780 and 0.829-0.861, respectively. In the test set of N2, the accuracy and AUC of discriminating low-grade DCIS from upstaged low-grade DCIS were 0.769-0.987 and 0.818-0.939, respectively. DL was ranked from one to five in the importance of features in the multimodal-DCIS-Net.
CONCLUSIONS: By developing the DCIS-Net and integrating it with multimodal information, diagnosing low-grade DCIS, intermediate-to high-grade DCIS, and upstaged DCIS is possible. It can also be used to distinguish DCIS from upstaged DCIS and low-grade DCIS from upstaged low-grade DCIS, which could pave the way for the DCIS clinical workflow.