Fusion model combining ultrasound-based radiomics and deep transfer learning with clinical parameters for preoperative prediction of pelvic lymph node metastasis in cervical cancer - Wang, Jihan; Bao, Shengxian; Huang, Tongtong; Cai, Yongzhi; Jin, Binbin; Wu, Ji (Frontiers in Oncology, 2025)
BackgroundTo 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.MethodsA 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 learning classifiers were evaluated, and the optimal model was used to construct a nomogram. Performance was assessed using area under the curve (AUC), calibration curves, and decision curve analysis (DCA).ResultsThe multilayer perceptron-based fusion model achieved a testing AUC of 0.753, outperforming standalone radiomics (AUC = 0.729) and DTL models (best AUC = 0.702; DenseNet121). Integration of clinical predictors (maximum tumor diameter and red blood cell count) further enhanced performance, yielding a nomogram with training/testing AUCs of 0.871 and 0.764, and a testing sensitivity and specificity of 58.1% and 84.4%,respectively. DCA demonstrated superior clinical utility for the nomogram across threshold probabilities (10%–50%).ConclusionsWe developed a multimodal fusion model integrating ultrasound-based radiomics, DTL, and clinical parameters for preoperative PLNM prediction in cervical cancer. The proposed nomogram provides a clinically applicable, cost-effective tool for preoperative PLNM prediction, particularly valuable for optimizing treatment decisions in resource-limited settings.