Assessing the Influence of B-US, CDFI, SE, and Patient Age on Predicting Molecular Subtypes in Breast Lesions Using Deep Learning Algorithms.

Journal: Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine

PMID: 38581195

Abstract

OBJECTIVES: Our study aims to investigate the impact of B-mode ultrasound (B-US) imaging, color Doppler flow imaging (CDFI), strain elastography (SE), and patient age on the prediction of molecular subtypes in breast lesions.

Authors

Weiyong Liu
Dongyue Wang

Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Le Liu

Department of Ultrasound, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Zhiguo Zhou

Keywords

Adult Age Factors Aged Aged, 80 and over Algorithms Breast Breast Neoplasms Deep Learning Elasticity Imaging Techniques Female Humans Middle Aged Predictive Value of Tests Reproducibility of Results Retrospective Studies Ultrasonography, Doppler, Color Ultrasonography, Mammary Young Adult

External Resources

View on PubMed Access via DOI PubMed (38581195)

Assessing the Influence of B-US, CDFI, SE, and Patient Age on Predicting Molecular Subtypes in Breast Lesions Using Deep Learning Algorithms.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals