Quantifying Early-Stage Lung Adenocarcinoma Progression with a Radiomic Trajectory

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I whakaputaina i:	NPJ Digital Medicine vol. 8, no. 1 (Dec 2025), p. 664-677
Kaituhi matua:	Qiu, Zhen-Bin
Ētahi atu kaituhi:	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
I whakaputaina:	Nature Publishing Group
Ngā marau:	Medical diagnosis Radiomics Cancer Patients Deep learning Medical prognosis Tumors
Urunga tuihono:	Citation/Abstract Full Text Full Text - PDF
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Whakaahuatanga
Whakarāpopotonga:	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
Puna:	Health & Medical Collection