{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["8(12)"],"submitter":["Chen Y"],"pubmed_abstract":["Background
The ultrasonic diagnosis of lymph node lesions is usually based on a small number of subjective visual features from a single ultrasonic modality, which limits diagnostic accuracy. Therefore, our study aimed to propose a computerized method for using dual-mode ultrasound radiomics and the intrinsic imaging phenotypes for accurately differentiating benign, lymphomatous, and metastatic lymph nodes.Methods
A total of 543 lymph nodes from 538 patients were examined with both B-mode ultrasonography and elastography. The data set was randomly divided into a training set of 407 nodes and a validation set of 136 nodes. First, we extracted 430 radiomic features from dual-mode images. Then, we combined the least absolute shrinkage and selection operator with the analysis o"],"journal":["Annals of translational medicine"],"pagination":["742"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7333147"],"repository":["biostudies-literature"],"pubmed_title":["Dual-mode ultrasound radiomics and intrinsic imaging phenotypes for diagnosis of lymph node lesions."],"pmcid":["PMC7333147"],"pubmed_authors":["Jiang J","Chen M","Chen Y","Shi J","Zhang Q","Chang W"],"additional_accession":[]},"is_claimable":false,"name":"Dual-mode ultrasound radiomics and intrinsic imaging phenotypes for diagnosis of lymph node lesions.","description":"Background
The ultrasonic diagnosis of lymph node lesions is usually based on a small number of subjective visual features from a single ultrasonic modality, which limits diagnostic accuracy. Therefore, our study aimed to propose a computerized method for using dual-mode ultrasound radiomics and the intrinsic imaging phenotypes for accurately differentiating benign, lymphomatous, and metastatic lymph nodes.Methods
A total of 543 lymph nodes from 538 patients were examined with both B-mode ultrasonography and elastography. The data set was randomly divided into a training set of 407 nodes and a validation set of 136 nodes. First, we extracted 430 radiomic features from dual-mode images. Then, we combined the least absolute shrinkage and selection operator with the analysis o","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Jun","modification":"2025-04-19T23:02:51.449Z","creation":"2025-04-19T23:02:51.449Z"},"accession":"S-EPMC7333147","cross_references":{"pubmed":["32647667"],"doi":["10.21037/atm-19-4630"]}}