Multimodal model enhances qualitative diagnosis of hypervascular thyroid nodules: integrating radiomics and deep learning features based on B-mode and PDI images.

ABSTRACT:

Background

Facing challenges in differentiating benign/malignant hypervascular thyroid nodules due to overlapping ultrasound features and limited vascular characterization, this study developed multimodal machine learning models integrating B-mode and power Doppler imaging (PDI) features.

Methods

A retrospective cohort of 315 patients with pathologically confirmed hypervascular thyroid nodules (Adler grade 2/3) was divided into training (n=220) and test (n=95) sets. Multimodal ultrasound images were processed using a deep learning-based segmentation model and red-channel thresholding method, followed by radiomics feature extraction (1,910 features via PyRadiomics) and deep learning feature derivation (1,000 ResNet-derived features). Feature selection employed analysis of var

SUBMITTER: Wen W 

PROVIDER: S-EPMC12432950 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature