Department of Electronic Engineering, Fudan University, Shanghai, China.
Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China.
Nat Commun. 2020 Sep 23;11(1):4807. doi: 10.1038/s41467-020-18497-3.
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.
非侵入性评估甲状腺乳头状癌（PTC）患者的淋巴结转移（LNM）风险对于治疗方案的选择具有重要价值。本文旨在开发一种转移学习放射组学（TLR）模型，用于在多中心、跨机器、多操作人员的场景下术前预测 PTC 患者的 LNM。报告称，TLR 模型能够稳定地预测 LNM。在根据诊断时间、机器和操作人员对主队列进行交叉验证和独立测试的实验中，TLR 的平均曲线下面积（AUC）为 0.90。在另外两个独立队列中，TLR 也实现了 0.93 AUC，根据 Delong 检验，这一性能明显优于其他三种方法。决策曲线分析也证明 TLR 模型比其他方法为 PTC 患者带来了更多的获益。
