Quantifying Early-Stage Lung Adenocarcinoma Progression with a Radiomic Trajectory

作者：Zhen‐Bin Qiu, Jiaqi Li, Shihua Dou, Qiuchen Meng, Meng-Min Wang, Hongji Li, Chao Zhang, Hongsheng Xie, Ben‐Yuan Jiang, Jun‐Tao Lin, Jia-Tao Zhang, Fangping Xu, Jinhai Yan, Lei Wei, Yi‐Long Wu, Haibo Wang, Lin Yang, Xuegong Zhang, Wen‐Zhao Zhong · 发表于：npj Digital Medicine · 年份：2025 · DOI：10.1038/s41746-025-02031-0 · 被引用次数：3 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment、Ferroptosis and cancer prognosis

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.