Deep learning predicts cervical lymph node metastasis in clinically node-negative papillary thyroid carcinoma.

ABSTRACT:

Objectives

Precise determination of cervical lymph node metastasis (CLNM) involvement in patients with early-stage thyroid cancer is fairly significant for identifying appropriate cervical treatment options. However, it is almost impossible to directly judge lymph node metastasis based on the imaging information of early-stage thyroid cancer patients with clinically negative lymph nodes.

Methods

Preoperative US images (BMUS and CDFI) of 1031 clinically node negative PTC patients definitively diagnosed on pathology from two independent hospitals were divided into training set, validation set, internal test set, and external test set. An ensemble deep learning model based on ResNet-50 was built integrating clinical variables, BMUS, and CDFI images using a bagging classifier to

SUBMITTER: Zhou LQ 

PROVIDER: S-EPMC10733258 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature