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<p>Papillary thyroid carcinoma (PTC) exhibits a high incidence and a strong propensity for lymph node metastasis (LNM). Accurate preoperative assessment of LNM is crucial for guiding surgical approaches, but conventional ultrasound exhibits suboptimal sensitivity. In this study, we developed a deep learning-based multimodal model to predict LNM in PTC patients. We retrospectively collected fine needle aspiration (FNA) liquid-based cytology specimens (<i>N</i> = 1095) and corresponding ultrasound images (<i>N</i> = 2190). A ResNet-101 architecture was trained using five-fold cross-validation and validated on external datasets from two independent centers. The multimodal model achieved strong predictive performance on the internal validation set (area under the curve, AUC: 0.891; accuracy: 0.821) and external validation set (AUC: 0.875; accuracy: 0.808). It outperformed models based solely on ultrasound or cytology images. Gradient-weighted class activation mapping revealed that nuclear features in FNA images were the most influential for LNM prediction. Our model achieved promising predictive performance and has the potential to guide clinical decision-making, potentially reducing unnecessary lymph node dissections in PTC patients.</p>

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Deep learning-based multimodal radiopathomics for preoperative prediction of lymph node metastasis in papillary thyroid carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月24日

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  • Qiuyu Cai,
  • Tianhong Gao,
  • Linyun Zhou,
  • Shengxuming Zhang,
  • Yi Chen,
  • Xianfa Xu,
  • Tian-An Jiang,
  • Jing Zhang,
  • Xiuming Zhang,
  • Zunlei Feng,
  • Qihan You

摘要

Papillary thyroid carcinoma (PTC) exhibits a high incidence and a strong propensity for lymph node metastasis (LNM). Accurate preoperative assessment of LNM is crucial for guiding surgical approaches, but conventional ultrasound exhibits suboptimal sensitivity. In this study, we developed a deep learning-based multimodal model to predict LNM in PTC patients. We retrospectively collected fine needle aspiration (FNA) liquid-based cytology specimens (N = 1095) and corresponding ultrasound images (N = 2190). A ResNet-101 architecture was trained using five-fold cross-validation and validated on external datasets from two independent centers. The multimodal model achieved strong predictive performance on the internal validation set (area under the curve, AUC: 0.891; accuracy: 0.821) and external validation set (AUC: 0.875; accuracy: 0.808). It outperformed models based solely on ultrasound or cytology images. Gradient-weighted class activation mapping revealed that nuclear features in FNA images were the most influential for LNM prediction. Our model achieved promising predictive performance and has the potential to guide clinical decision-making, potentially reducing unnecessary lymph node dissections in PTC patients.