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  • 2025년 11월호
    [Comput Methods Programs Biomed .] Unraveling the tumor-microenvironment through a radiogenomic-based multiomic approach to predict outcomes of immunotherapy in non-small cell lung cancer

    성균관의대 / 정동영, 이호윤*

  • 출처
    Comput Methods Programs Biomed .
  • 등재일
    2025 Sep:269:108915.
  • 저널이슈번호
  • 내용

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    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.

     

     

    Affiliations

    Dong Young Jeong 1, Cheol Yong Joe 2, Sang Min Lee 3, Sehhoon Park 4, Seung Hwan Moon 5, Joon Young Choi 5, Jonghoon Kim 6, Se-Hoon Lee 4, Ho Yun Lee 7
    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. Electronic address: hoyunlee96@gmail.com.

  • 키워드
    Immune checkpoint inhibitor; Non-small cell lung cancer; Prediction model; Radiomics; Transcriptomics.
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