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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.
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1 Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China (ROR: https://ror.org/01vjw4z39) (GRID: grid.284723.8) (ISNI: 0000 0000 8877 7471); School of Medicine, South China University of Technology, Guangzhou, China (ROR: https://ror.org/0530pts50) (GRID: grid.79703.3a) (ISNI: 0000 0004 1764 3838); Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China (ROR: https://ror.org/01vjw4z39) (GRID: grid.284723.8) (ISNI: 0000 0000 8877 7471)
2 Bioinformatics Division, BNRIST and MOE Key Lab of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China (ROR: https://ror.org/03cve4549) (GRID: grid.12527.33) (ISNI: 0000 0001 0662 3178); China Mobile Research Institute, Beijing, China (ROR: https://ror.org/05gftfe97) (GRID: grid.495291.2) (ISNI: 0000 0004 0466 5552)
3 Department of Thoracic Surgery, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University, the First Affiliated Hospital of South University of Science and Technology of China, Shenzhen Institute of Respiratory Diseases), Shenzhen, China (ROR: https://ror.org/02xe5ns62) (GRID: grid.258164.c) (ISNI: 0000 0004 1790 3548); Department of Thoracic Surgery, the First Affiliated Hospital of Hainan Medical University, Hainan Province Clinical Medical Center of Respiratory Disease, Haikou, China (ROR: https://ror.org/004eeze55) (GRID: grid.443397.e) (ISNI: 0000 0004 0368 7493)
4 Bioinformatics Division, BNRIST and MOE Key Lab of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China (ROR: https://ror.org/03cve4549) (GRID: grid.12527.33) (ISNI: 0000 0001 0662 3178)
5 Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China (ROR: https://ror.org/01vjw4z39) (GRID: grid.284723.8) (ISNI: 0000 0000 8877 7471); Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China (ROR: https://ror.org/01vjw4z39) (GRID: grid.284723.8) (ISNI: 0000 0000 8877 7471)
6 Department of Thoracic Surgery, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University, the First Affiliated Hospital of South University of Science and Technology of China, Shenzhen Institute of Respiratory Diseases), Shenzhen, China (ROR: https://ror.org/02xe5ns62) (GRID: grid.258164.c) (ISNI: 0000 0004 1790 3548)
7 Department of Pathology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China (ROR: https://ror.org/01vjw4z39) (GRID: grid.284723.8) (ISNI: 0000 0000 8877 7471)
8 Clinical Trial Unit, Precision Medicine Institute, First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China (ROR: https://ror.org/037p24858) (GRID: grid.412615.5) (ISNI: 0000 0004 1803 6239)
9 Bioinformatics Division, BNRIST and MOE Key Lab of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China (ROR: https://ror.org/03cve4549) (GRID: grid.12527.33) (ISNI: 0000 0001 0662 3178); School of Medicine, Tsinghua University, Beijing, China (ROR: https://ror.org/03cve4549) (GRID: grid.12527.33) (ISNI: 0000 0001 0662 3178)