Metastasis

Carcinoma

Pharmacokinetics

Thyroid carcinoma

Area under curve

Cohort

Computer science

Lymph node

Receiver operating characteristic

Radiology

Radiomics

Internal medicine

Area under the curve

Oncology

Medicine

Lymph node metastasis

Cancer

Machine learning

Thyroid

Nature Communications

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.

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