Deep Learning Analysis of Dual-Modality US Videos for the Characterization of Superficial Lymphadenopathy.
Authors
Affiliations (8)
Affiliations (8)
- Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou University, Street Address, Lanzhou 730030, China.
- Gansu Province Medical Engineering Research Center for Intelligence Ultrasound, Lanzhou, China.
- College of Future Information Technology, Fudan University, Shanghai, China.
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
- Department of Ultrasound, Huadong Hospital, Fudan University, Shanghai, China.
- Department of Ultrasound Medicine, The First People's Hospital of Chenzhou, Hunan, China.
- School of Life Sciences, Shanghai University, Shanghai, China.
- Department of Ultrasound, Sun Yat-sen University Cancer Center Gansu Hospital, Lanzhou, China.
Abstract
Purpose To develop and validate a deep learning (DL) model based on dual-modality US videos to differentiate benign from malignant superficial lymphadenopathy (LA) and stratify malignant subtypes. Materials and Methods This multicenter study included patients with pathologically confirmed LA from five centers (June 2019-December 2025). The Dual-Modality US Video Lymphadenopathy Diagnostic Network (DMUVL-DiagNet) integrated B-mode and color Doppler flow imaging videos with basic clinical information. A retrospective video dataset (<i>n</i> = 1616) was used for training and internal testing, while external testing used a prospective dual-modality US video set (<i>n</i> = 454) and a static US image set (<i>n</i> = 1956). Model performance was compared against clinical baseline and US-based DL models and an artificial intelligence (AI)-assisted reader study was conducted. Areas under the receiver operating characteristic curves (AUCs) were calculated to evaluate diagnostic performance. The study was prospectively registered at the Chinese Clinical Trial Registry (ChiCTR2400090592). Results Overall, 4026 patients (median age, 52.6 [IQR 41-62] years; 2175 males) were included. DMUVL-DiagNet consistently outperformed clinical and US-based models across all test sets, achieving AUCs of 0.91-0.95 for benign versus malignant classification and 0.87-0.91 for lymphoma versus metastasis classification. The model showed good generalizability (subgroup AUCs, 0.85-0.93) and surpassed the average performance of all radiologist experience groups in both tasks. Additionally, DMUVL-DiagNet assistance improved junior radiologist AUCs from 0.72 to 0.88 and from 0.58 to 0.77 for the two tasks, respectively (all <i>P</i> < .001). Conclusion DMUVL-DiagNet enabled accurate characterization of superficial LA and improved junior radiologist diagnostic performance. © The Authors 2026. Published by the Radiological Society of North America under a CC BY 4.0 license.