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<p>Determining tumor progression status is critical for early-stage lung adenocarcinoma (esLUAD) diagnosis and treatment, yet histopathology-based grading often overlooks heterogeneity within grades. We propose RadioTrace, a deep contrastive learning framework integrating radiomic and pathological information to learn a radiomic trajectory for quantifying esLUAD progression. Across four multi-institutional cohorts, RadioTrace well predicted tumor phenotypes including spread through air spaces (STAS) and lymph node metastasis (LNM). Survival analyses demonstrated it as an independent prognostic factor (log-rank test <i>p</i> <b>&lt;</b> 0.004 across all cohorts). Within the same pathological grade, it revealed significant survival heterogeneity (<i>p</i> <b>&lt;</b> 0.02 across all cohorts), underscoring the limitations of current grading criteria. Genomic and transcriptomic analyses confirmed associations with progression-related molecular features. Longitudinal analysis of patients with multiple CT follow-ups further showed consistency with continuous progression. These findings demonstrate that RadioTrace enables quantitative, interpretable assessment of esLUAD progression, providing insights beyond histopathology and assisting clinical decision-making.</p>

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Quantifying Early-Stage Lung Adenocarcinoma Progression with a Radiomic Trajectory

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月17日

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  • Zhen-Bin Qiu,
  • Jiaqi Li,
  • Shihua Dou,
  • Qiuchen Meng,
  • Meng-Min Wang,
  • Hong-Ji Li,
  • Chao Zhang,
  • Hongsheng Xie,
  • Ben-Yuan Jiang,
  • Jun-Tao Lin,
  • Jia-Tao Zhang,
  • Fang-Ping Xu,
  • Jin-Hai Yan,
  • Lei Wei,
  • Yi-Long Wu,
  • Haibo Wang,
  • Lin Yang,
  • Xuegong Zhang,
  • Wen-Zhao Zhong

摘要

Determining tumor progression status is critical for early-stage lung adenocarcinoma (esLUAD) diagnosis and treatment, yet histopathology-based grading often overlooks heterogeneity within grades. We propose RadioTrace, a deep contrastive learning framework integrating radiomic and pathological information to learn a radiomic trajectory for quantifying esLUAD progression. Across four multi-institutional cohorts, RadioTrace well predicted tumor phenotypes including spread through air spaces (STAS) and lymph node metastasis (LNM). Survival analyses demonstrated it as an independent prognostic factor (log-rank test p < 0.004 across all cohorts). Within the same pathological grade, it revealed significant survival heterogeneity (p < 0.02 across all cohorts), underscoring the limitations of current grading criteria. Genomic and transcriptomic analyses confirmed associations with progression-related molecular features. Longitudinal analysis of patients with multiple CT follow-ups further showed consistency with continuous progression. These findings demonstrate that RadioTrace enables quantitative, interpretable assessment of esLUAD progression, providing insights beyond histopathology and assisting clinical decision-making.