2021
DOI: 10.3389/fonc.2021.623506
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Evaluating the Accuracy of Breast Cancer and Molecular Subtype Diagnosis by Ultrasound Image Deep Learning Model

Abstract: Background: Breast ultrasound is the first choice for breast tumor diagnosis in China, but the Breast Imaging Reporting and Data System (BI-RADS) categorization routinely used in the clinic often leads to unnecessary biopsy. Radiologists have no ability to predict molecular subtypes with important pathological information that can guide clinical treatment.Materials and Methods: This retrospective study collected breast ultrasound images from two hospitals and formed training, test and external test sets after … Show more

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Cited by 53 publications
(47 citation statements)
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“…One of the possible explanations for the findings may be that the TN subtypes demonstrated more necrosis, so the texture may be more features in the images. That results were consistent with some studies ( 22 , 23 ).…”
Section: Discussionsupporting
confidence: 94%
“…One of the possible explanations for the findings may be that the TN subtypes demonstrated more necrosis, so the texture may be more features in the images. That results were consistent with some studies ( 22 , 23 ).…”
Section: Discussionsupporting
confidence: 94%
“…The ResNet-50 and MobileNet pre-trained models showed 97.03% and 94.42% accuracy and took almost 114.57 and 192.4 min for training, respectively. Zhang et al [42] also proposed a deep learning model for breast cancer diagnosis using BU images. Their proposed models shows the accuracy of 92.86%.…”
Section: Discussionmentioning
confidence: 99%
“…The area under the curve (AUC) of 95% for their classifier was noted. In a subsequent study [17], an optimized deep learning model was constructed to classify the BU images. The dataset of 3739 images was used to validate their proposed model.…”
Section: Introductionmentioning
confidence: 99%
“…However, their predictive results simply comprised outputs of four separate binary tasks (including luminal A and non-luminal A set, luminal B and non-luminal B set, HER-2 positive and non-HER-2 positive set, and triple negative and non-triple negative set). Similarly, another study also only evaluated the performance of retrospectively collected greyscale US images for prediction of binary classification breast cancer molecular subtypes using a CNN approach [42]. Additionally, previous studies used radiomics, machine learning, or deep learning methods to decipher breast cancer molecular subtypes seen on magnetic resonance image (MRI) [43][44][45][46].…”
Section: Discussionmentioning
confidence: 99%