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<p>Accurate assessment of lymph node metastasis (LNM) in T1/2-stage esophageal squamous cell carcinoma (ESCC) is critical for treatment planning but remains challenging due to diagnostic inaccuracies and unclear metastatic mechanisms. This study aimed to predict LNM in T1/2-stage ESCC using machine learning-based radiomics and elucidate its biological underpinnings. We retrospectively analyzed 374 surgically treated ESCC patients from two centers, employing six machine-learning algorithms to derive an optimal radiomics score. Key pathways and genes linked to LNM were investigated via bioinformatics and experimental validation. The decision tree (DT)-based radiomics model demonstrated superior predictive performance, with AUCs of 0.933 (training), 0.887 (validation), and 0.845 (test). Bioinformatics analysis implicated tumor-lymphatic invasion pathways, with EFNA1 emerging as a potential key regulator. These findings highlight the clinical utility of radiomics for LNM prediction in early-stage ESCC and provide insights into its molecular mechanisms.</p>

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Unraveling the power of radiomics: prediction and exploration of lymph node metastasis in stage T1/2 esophageal squamous cell carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月16日

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  • Yu Zhang,
  • Long Liu,
  • Mengyu Han,
  • Linrui Li,
  • Qibing Wu,
  • Xin Wang

摘要

Accurate assessment of lymph node metastasis (LNM) in T1/2-stage esophageal squamous cell carcinoma (ESCC) is critical for treatment planning but remains challenging due to diagnostic inaccuracies and unclear metastatic mechanisms. This study aimed to predict LNM in T1/2-stage ESCC using machine learning-based radiomics and elucidate its biological underpinnings. We retrospectively analyzed 374 surgically treated ESCC patients from two centers, employing six machine-learning algorithms to derive an optimal radiomics score. Key pathways and genes linked to LNM were investigated via bioinformatics and experimental validation. The decision tree (DT)-based radiomics model demonstrated superior predictive performance, with AUCs of 0.933 (training), 0.887 (validation), and 0.845 (test). Bioinformatics analysis implicated tumor-lymphatic invasion pathways, with EFNA1 emerging as a potential key regulator. These findings highlight the clinical utility of radiomics for LNM prediction in early-stage ESCC and provide insights into its molecular mechanisms.