A novel cross-attentive network for classifying cervical metastatic lymph nodes on B- and D-mode ultrasound images in oral squamous cell carcinoma.

Journal: Dento maxillo facial radiology
Published Date: Jul 1, 2026

Abstract

OBJECTIVES: This study proposes a deep convolutional neural network model that integrates B-mode and D-mode ultrasound images to classify metastatic lymph nodes in patients with oral squamous cell carcinoma. METHODS: A shared backbone network incorporating a cross-attention mechanism was employed to enhance feature-level interactions between dual-input ultrasound images. A total of 6 convolutional neural network architectures (VGG16, SqueezeNet, ResNet50, EfficientNet B3, ConvNext, and DenseNet121) were implemented within a shared backbone framework to investigate optimal performance. For each network, diagnostic performance was compared between dual-input and single-input ultrasound. In addition, model performance was evaluated against human observers with different levels of experience. RESULTS: The model using DenseNet121 as a shared backbone with an integrated cross-attention layer (LNM-Net) achieved the highest classification accuracy (85.3%) when utilizing dual-input images, surpassing the diagnostic performance of residents. The cross-attention module improved feature fusion, reducing false positives by suppressing modality-specific noise. CONCLUSIONS: LNM-Net demonstrates strong potential as a clinical decision-support tool for preoperative lymph node metastasis assessment in oral squamous cell carcinoma. Despite current limitations such as dataset size and cross-institutional variability, the model offers a promising supplementary aid, particularly in settings with limited radiological expertise. ADVANCES IN KNOWLEDGE: This study develops a novel cross-attentive network using dual-input B- and D-mode ultrasound images to classify metastatic lymph nodes in oral squamous cell carcinoma.

Authors

  • Yu-Ri Kim
    Department of Oral and Maxillofacial Radiology, Dental Research Institute, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Korea.
  • Ji Yong Han
    Interdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National University, Seoul, 08826, South Korea.
  • Su Yang
    Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Jong Woo Kim
    School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea.
  • Kyung-Hoe Huh
    4 Department of Oral and Maxillofacial Radiology, School of Dentistry, Seoul National University, Seoul, Korea.
  • Min-Suk Heo
    4 Department of Oral and Maxillofacial Radiology, School of Dentistry, Seoul National University, Seoul, Korea.
  • Sam-Sun Lee
    Department of Oral and Maxillofacial Radiology, School of Dentistry and Dental Research Institute, Seoul National University, Seoul, Korea.
  • Won-Jin Yi
    Department of Oral and Maxillofacial Radiology, School of Dentistry and Dental Research Institute, Seoul National University, Seoul, Korea. [email protected].
  • Jo-Eun Kim
    Department of Oral and Maxillofacial Radiology, Seoul National University Dental Hospital, Seoul, Korea.

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