Predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma using deep learning.

ABSTRACT:

Background

The aim of this study is to design a deep learning (DL) model to preoperatively predict the occurrence of central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC).

Methods

This research collected preoperative ultrasound (US) images and clinical factors of 611 PTMC patients. The clinical factors were analyzed using multivariate regression. Then, a DL model based on US images and clinical factors was developed to preoperatively predict CLNM. The model's efficacy was evaluated using the receiver operating characteristic (ROC) curve, along with accuracy, sensitivity, specificity, and the F₁ score.

Results

The multivariate analysis indicated an independent correlation factors including age ≥55 (OR = 0.309, p

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10984175 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature