Author
Listed:
- Jinhua Yu
(Fudan University
Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention)
- Yinhui Deng
(Fudan University
Fudan University Science Park)
- Tongtong Liu
(Fudan University)
- Jin Zhou
(Fudan University Shanghai Cancer Center)
- Xiaohong Jia
(Ruijin Hospital Affiliated to Shanghai Jiaotong University)
- Tianlei Xiao
(Fudan University)
- Shichong Zhou
(Fudan University Shanghai Cancer Center)
- Jiawei Li
(Fudan University Shanghai Cancer Center)
- Yi Guo
(Fudan University)
- Yuanyuan Wang
(Fudan University
Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention)
- Jianqiao Zhou
(Ruijin Hospital Affiliated to Shanghai Jiaotong University)
- Cai Chang
(Fudan University Shanghai Cancer Center)
Abstract
Non-invasive assessment of the risk of lymph node metastasis (LNM) in patients with papillary thyroid carcinoma (PTC) is of great value for the treatment option selection. The purpose of this paper is to develop a transfer learning radiomics (TLR) model for preoperative prediction of LNM in PTC patients in a multicenter, cross-machine, multi-operator scenario. Here we report the TLR model produces a stable LNM prediction. In the experiments of cross-validation and independent testing of the main cohort according to diagnostic time, machine, and operator, the TLR achieves an average area under the curve (AUC) of 0.90. In the other two independent cohorts, TLR also achieves 0.93 AUC, and this performance is statistically better than the other three methods according to Delong test. Decision curve analysis also proves that the TLR model brings more benefit to PTC patients than other methods.
Suggested Citation
Jinhua Yu & Yinhui Deng & Tongtong Liu & Jin Zhou & Xiaohong Jia & Tianlei Xiao & Shichong Zhou & Jiawei Li & Yi Guo & Yuanyuan Wang & Jianqiao Zhou & Cai Chang, 2020.
"Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics,"
Nature Communications, Nature, vol. 11(1), pages 1-10, December.
Handle:
RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18497-3
DOI: 10.1038/s41467-020-18497-3
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18497-3. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.