Multi-Modal Ultrasound-Based Prognostic Model for Diffuse Large B-Cell Lymphoma with Predominantly Superficial Lymph Node Involvement

Objective: This study aimed to develop a multi-modal ultrasound-based model integrating radiomics, deep learning, and clinical features to predict progression-free survival (PFS) in diffuse large B-cell lymphoma (DLBCL) with superficial lymph node involvement. Methods: A total of 281 DLBCL patients with superficial lymph node involvement treated with standard regimens were retrospectively enrolled and assigned to training and test sets at a 7:3 ratio. Ultrasound radiomic features were extracted by PyRadiomics, and deep learning features were derived from DenseNet121 with transfer learning. After Pearson’s correlation filtering and least absolute shrinkage and selection operator (LASSO)-Cox regression for feature selection, five prognostic models were compared using C-index, time-dependent Area Under the Curve (AUC), risk stratification and decision curve analysis. Results: The combined model achieved the highest C-index of 0.811 in the test set, with 1-/2-/3-year PFS AUCs of 0.826, 0.812, and 0.810, respectively. Risk stratification showed significantly poorer PFS in the high-risk group (p < 0.01). A visualized nomogram was further developed as a preliminary reference for individualized prediction. Conclusions: This multi-modal combined predictive model and corresponding nomogram based on superficial lymph node features showed promising potential for practical and intuitive prognostic assessment and risk stratification in DLBCL.