Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics

Jinhua Yu, Yinhui Deng, Tongtong Liu, Jin Zhou, Xiaohong Jia, Tianlei Xiao, Shichong Zhou, Jiawei Li, Yi Guo, Yuanyuan Wang (), Jianqiao Zhou () and Cai Chang ()
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Jinhua Yu: Fudan University
Yinhui Deng: Fudan University
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
Jianqiao Zhou: Ruijin Hospital Affiliated to Shanghai Jiaotong University
Cai Chang: Fudan University Shanghai Cancer Center

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-18497-3

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