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

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 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). Results: The 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%). Conclusions: We 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.