Automated Deep Learning Segmentation and CT Elastography Radiomics for Predicting Lymph Node Metastasis Near the Right Recurrent Laryngeal Nerve in Esophageal Cancer Preoperatively - Takeaways - MDSpire

Automated Deep Learning Segmentation and CT Elastography Radiomics for Predicting Lymph Node Metastasis Near the Right Recurrent Laryngeal Nerve in Esophageal Cancer Preoperatively

  • By

  • Chao Ji

  • Qingqing Li

  • Shumin Jiang

  • Lingling Wang

  • Sunkui Ke

  • Feng Wang

  • Hongbin Duan

  • Xiaomei Lin

  • Xi’e Xu

  • Xiaoli Huang

  • April 24, 2026

  • 0 min

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  • 1

    The study developed an automated imaging approach to predict lymph node metastasis near the right recurrent laryngeal nerve in esophageal cancer.

  • 2

    The method integrates nnU-Net automatic segmentation with CT-derived differential elasticity maps to enhance preoperative assessment.

  • 3

    Five significant radiomic features were identified, including a first-order entropy feature with an AUC of 0.814, indicating strong diagnostic performance.

  • 4

    The proposed workflow demonstrated clinical utility, providing valuable information for personalized treatment planning in esophageal cancer.

  • 5

    This approach addresses limitations of conventional CT by incorporating biomechanical properties, improving the accuracy of lymph node metastasis predictions.

Original Source(s)

Automated Deep Learning Segmentation and CT Elastography Radiomics for Predicting Lymph Node Metastasis Near the Right Recurrent Laryngeal Nerve in Esophageal Cancer Preoperatively

  • by Chao Ji, Qingqing Li, Shumin Jiang, Lingling Wang, Sunkui Ke, Feng Wang, Hongbin Duan, Xiaomei Lin, Xi’e Xu, Xiaoli Huang

  • April 24, 2026

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