Deep learning radiomics of multimodal ultrasound for classifying metastatic cervical lymphadenopathy into primary cancer sites: a feasibility study.

Journal: Ultraschall in der Medizin (Stuttgart, Germany : 1980)

PMID: 38052240

Abstract

PURPOSE: To investigate the feasibility of deep learning radiomics (DLR) based on multimodal ultrasound to differentiate the primary cancer sites of metastatic cervical lymphadenopathy (CLA).

Authors

Yangyang Zhu

CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Zheling Meng

CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Hao Wu

Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.
Xiao Fan

Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou University, Lanzhou, 730030, China.
Wenhao Lv

Department of Gastroenterology, The No.4 People's Hospital of Hengshui City, Hengshui 053000, China.
Jie Tian

CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Kun Wang

CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Fang Nie

Department of Ultrasound, Lanzhou University Second Hospital, Lanzhou, Gansu 730030, China.

Keywords

Adult Aged Contrast Media Deep Learning Diagnosis, Differential Elasticity Imaging Techniques Feasibility Studies Female Head and Neck Neoplasms Humans Lung Neoplasms Lymphadenopathy Lymphatic Metastasis Male Middle Aged Multimodal Imaging Neck Radiomics Thyroid Neoplasms Ultrasonography

External Resources

View on PubMed Access via DOI PubMed (38052240)

Deep learning radiomics of multimodal ultrasound for classifying metastatic cervical lymphadenopathy into primary cancer sites: a feasibility study.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals