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Quantifying Early-Stage Lung Adenocarcinoma Progression with a Radiomic Trajectory

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Publicado en:NPJ Digital Medicine vol. 8, no. 1 (Dec 2025), p. 664-677
Autor principal: Qiu, Zhen-Bin
Otros Autores: Li, Jiaqi, Dou, Shihua, Meng, Qiuchen, Wang, Meng-Min, Li, Hong-Ji, Zhang, Chao, Xie, Hongsheng, Jiang, Ben-Yuan, Lin, Jun-Tao, Zhang, Jia-Tao, Xu, Fang-Ping, Yan, Jin-Hai, Wei, Lei, Wu, Yi-Long, Wang, Haibo, Yang, Lin, Zhang, Xuegong, Zhong, Wen-Zhao
Publicado:
Nature Publishing Group
Materias:
Medical diagnosis
Radiomics
Cancer
Patients
Deep learning
Medical prognosis
Tumors
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:2398-6352
DOI:10.1038/s41746-025-02031-0
Fuente:Health & Medical Collection