Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional...
Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study
About this item
Full title
Author / Creator
Liu, Shunli , Sun, Weikai , Yang, Shifeng , Duan, Lisha , Huang, Chencui , Xu, Jingxu , Hou, Feng , Hao, Dapeng , Yu, Tengbo and Wang, Hexiang
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
Formats
Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Subjects
More information
Scope and Contents
Contents
Objectives
To evaluate the performance of a deep learning radiomic nomogram (DLRN) model at predicting tumor relapse in patients with soft tissue sarcomas (STS) who underwent surgical resection.
Methods
In total, 282 patients who underwent MRI and resection for STS at three independent centers were retrospectively enrolled. In addition, 11...
Alternative Titles
Full title
Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2623198067
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2623198067
Other Identifiers
ISSN
0938-7994
E-ISSN
1432-1084
DOI
10.1007/s00330-021-08221-0
