연구성과 홍보

Comput Methods Programs Biomed. 2025 Sep:269:108915.

Title : Unraveling the tumor-microenvironment through a radiogenomic-based multiomic approach to predict outcomes of immunotherapy in non-small cell lung cancer

Authors : Dong Young Jeong1, Cheol Yong Joe2, Sang Min Lee3, Sehhoon Park4, Seung Hwan Moon5, Joon Young Choi5, Jonghoon Kim6, Se-Hoon Lee4, Ho Yun Lee7*

Affiliations :

1Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea.

2Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea; Department of Health Sciences and Technology, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea.

3Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.

4Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea.

5Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea.

6Department of Electronic and Computer Engineering, Sungkyunkwan University, Suwon, South Korea.

7Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea; Department of Health Sciences and Technology, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea.

DOI: 10.1016/j.cmpb.2025.108915

Abstract :

Background: The tumor microenvironment (TME) plays a critical role in influencing immune checkpoint inhibitor (ICI) therapy outcomes in advanced non-small cell lung cancer (NSCLC). This study aimed to develop a radiomics model reflecting an ICI-favorable TME based on whole transcriptome sequencing (WTS).

Methods: This multi-center retrospective cohort study included training (n = 120), internal validation (n = 319), and external validation (n = 150) cohorts of advanced NSCLC patients who received ICI as first- or second-line therapy. The radiomics model (rTME) was developed based on the TME score, which reflected ICI-favorable immune cell compositions. The model's performance was assessed using the C-index, and survival outcomes were also evaluated.

Results: In the training cohort, high rTME scores were associated with significantly prolonged progression-free survival (PFS) (median 4.1 vs. 2.9 months, p = 0.024) and overall survival (OS) (median 15.0 vs. 8.4 months, p = 0.030). Similar trends were observed in the internal validation cohort for PFS (median 3.3 vs. 2.1 months, p = 0.004) and OS (median 13.9 vs. 7.3 months, p = 0.004), as well as in the external validation cohort for OS (median 15.5 vs. 7.3 months, p = 0.008). Integrating clinical variables improved predictive accuracy in both the training and internal validation cohorts.

Conclusion: Our radiomics model, reflecting the ICI-favorable immune cell expression in the TME, showed a positive association with ICI outcomes in NSCLC patients. Integrating radiomics and clinical variables enhances prognostic accuracy, demonstrating the model's potential utility in guiding ICI therapy decisions.