Development of a Deep Learning–Based Model for Diagnosing Breast Nodules With Ultrasound

Abstract

Objectives Artificial intelligence (AI) has been an important addition to medicine. We aimed to explore the use of deep learning (DL) to distinguish benign from malignant lesions with breast ultrasound (BUS). Methods The DL model was trained with BUS nodule data using a standard protocol (1271 malignant nodules, 1053 benign nodules, and 2144 images of the contralateral normal breast). The model was tested with 692 images of 256 breast nodules. We used the accuracy, precision, recall, harmonic mean of recall and precision, and mean average precision as the indices to assess the DL model. We used 100 BUS images to evaluate differences in diagnostic accuracy among the AI system, experts (>25 years of experience), and physicians with varying levels of experience. A receiver operating characteristic curve was generated to evaluate the accuracy for distinguishing between benign and malignant breast nodules. Results The DL model showed 73.3% sensitivity and 94.9% specificity for the diagnosis of benign versus malignant breast nodules (area under the curve, 0.943). No significant difference in diagnostic ability was found between the AI system and the expert group (P = .951), although the physicians with lower levels of experience showed significant differences from the AI and expert groups (P = .01 and .03, respectively). Conclusions Deep learning could distinguish between benign and malignant breast nodules with BUS. On BUS images, DL achieved diagnostic accuracy equivalent to that of expert physicians.

Establishment of a deep‐learning system to diagnose BI‐RADS4a or higher using breast ultrasound for clinical application

“…Although there have been many reports on the AI‐based diagnosis of breast ultrasound, most of the studies have focused on technical aspects, such as the algorithm used for deep learning depending on the purpose, while only a few studies have focused on clinical applications and utility of AI 10 . Among these technical considerations, the applications of deep‐learning techniques are mainly categorized into object detection, 11,12 segmentation, 13,14 image classification, 15,16 and image synthesis 17 . Regarding image classification, there are many reports on the distinction between benign and malignant lesions in static images 18–22 .…”

AI-enhanced breast imaging: Where are we and where are we heading?

“…The network was able to classify malignant lesions in a short time with an accuracy of 90% and therefore was proposed to work together with radiologists to improve breast cancer diagnosis. Several groups have investigated these AI models in multi-reader studies [37][38][39][40]. Becker et al [37] retrospectively evaluated the performance of a generic deep learning software for the classification of 637 breast lesions on US exams and compared it to radiologists with varying levels of expertise.…”

Development and validation of a transformer-based CAD model for improving the consistency of BI-RADS category 3–5 nodule classification among radiologists: a multiple center study

“…Among several commonly used breast examination techniques, ultrasonography is the most convenient and most economical modality with no radiation and relatively low cost. However, the quality of ultrasonography directly depends on operator expertise and experience, especially as it relates to scanning techniques, ability to detect lesions, and description and interpretation of images ( 3 ). The Breast Imaging Reporting and Data Systems on ultrasonography (BI-RADS) represents an attempt to not only normalize and standardize the terminology used to describe a series of appearances in ultrasound images but also to classify breast nodules from category 1 through category 6 depending on the probability of malignancy ( 4 ).…”