Fusion model combining ultrasound-based radiomics and deep transfer learning with clinical parameters for preoperative prediction of pelvic lymph node metastasis in cervical cancer.

ABSTRACT:

Background

To develop and validate a multimodal fusion model integrating ultrasound-based radiomics, deep transfer learning (DTL), and clinical parameters for preoperative pelvic lymph node metastasis (PLNM) prediction in cervical cancer.

Methods

A retrospective cohort of 421 patients with surgically confirmed cervical cancer was divided into the training (70%, n = 294) and testing (30%, n = 127) sets. Ultrasound-based radiomics (1,561 handcrafted features) and 3 DTL architectures (DenseNet121, ResNet50, AlexNet) were employed for feature extraction. After redundancy reduction (Spearman correlation, least absolute shrinkage and selection operator regression) and principal component analysis, fused radiomics-DTL features were combined with clinical predictors. Eight machine l

SUBMITTER: Wang J 

PROVIDER: S-EPMC12658780 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature