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. A non-destructive and efficient method for predicting the risk of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients is highly needed. Here, the authors develop a transfer learning radiomics model for preoperative prediction of LNM in patients with PTC in a multicenter scenario.
[1]Fudan Univ, Dept Elect Engn, Shanghai, Peoples R China.
[2]Key Lab Med Imaging Comp & Comp Assisted Interven, Shanghai, Peoples R China.
[3]Fudan Univ, MingGe Res, Sci Pk, Shanghai, Peoples R China.
[4]Fudan Univ, Shanghai Canc Ctr, Shanghai, Peoples R China.
[5]Shanghai Jiao Tong Univ, Ruijin Hosp, Shanghai, Peoples R China.
(8.0+极速模式)