Quantitative vascular feature-based multimodality prediction model for multi-origin malignant cervical lymphadenopathy.

ABSTRACT:

Background

The precise prediction of multi-origin malignant cervical lymphadenopathy is limited by the low inter-reader reproducibility of imaging interpretation, and a quantitative method to improve this aspect is lacking. This study aimed to develop and validate an artificial intelligence framework integrating quantitative vascular features for assessing cervical lymphadenopathy and explore its utility among radiologists.

Methods

For this retrospective study, a total of 21,298 ultrasound images of 10,649 cervical lymph nodes (LNs) from 10,386 patients and 2366 images of 1183 LNs from 1151 patients at the Sun Yat-sen University Cancer Center between January 2011 and July 2022 were used for model development and internal testing, respectively. For external model testing, we

SUBMITTER: Li C 

PROVIDER: S-EPMC11870188 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature