Application of deep learning to predict underestimation in ductal carcinoma in situ of the breast with ultrasound.

ABSTRACT:

Background

To develop an ultrasound-based deep learning model to predict postoperative upgrading of pure ductal carcinoma in situ (DCIS) diagnosed by core needle biopsy (CNB) before surgery.

Methods

Of the 360 patients with DCIS diagnosed by CNB and identified retrospectively, 180 had lesions upstaged to ductal carcinoma in situ with microinvasion (DCISM) or invasive ductal carcinoma (IDC) postoperatively. Ultrasound images obtained from the hospital database were divided into a training set (n=240) and validation set (n=120), with a ratio of 2:1 in chronological order. Four deep learning models, based on the ResNet and VggNet structures, were established to classify the ultrasound images into postoperative upgrade and pure DCIS. We obtained the area under the receiver opera

SUBMITTER: Qian L 

PROVIDER: S-EPMC7944276 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature