글로벌 연구동향
핵의학
- 2020년 11월호
[Theranostics.] Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma서울대병원 / 박창희, 나권중, 최홍윤*, 옥찬영*
- 출처
- Theranostics.
- 등재일
- 2020 Aug 29
- 저널이슈번호
- 10(23):10838-10848. doi: 10.7150/thno.50283. eCollection 2020.
- 내용
Abstract
Rationale: The clinical application of biomarkers reflecting tumor immune microenvironment is hurdled by the invasiveness of obtaining tissues despite its importance in immunotherapy. We developed a deep learning-based biomarker which noninvasively estimates a tumor immune profile with fluorodeoxyglucose positron emission tomography (FDG-PET) in lung adenocarcinoma (LUAD). Methods: A deep learning model to predict cytolytic activity score (CytAct) using semi-automatically segmented tumors on FDG-PET trained by a publicly available dataset paired with tissue RNA sequencing (n = 93). This model was validated in two independent cohorts of LUAD: SNUH (n = 43) and The Cancer Genome Atlas (TCGA) cohort (n = 16). The model was applied to the immune checkpoint blockade (ICB) cohort, which consists of patients with metastatic LUAD who underwent ICB treatment (n = 29). Results: The predicted CytAct showed a positive correlation with CytAct of RNA sequencing in validation cohorts (Spearman rho = 0.32, p = 0.04 in SNUH cohort; spearman rho = 0.47, p = 0.07 in TCGA cohort). In ICB cohort, the higher predicted CytAct of individual lesion was associated with more decrement in tumor size after ICB treatment (Spearman rho = -0.54, p < 0.001). Higher minimum predicted CytAct in each patient associated with significantly prolonged progression free survival and overall survival (Hazard ratio 0.25, p = 0.001 and 0.18, p = 0.004, respectively). In patients with multiple lesions, ICB responders had significantly lower variance of predicted CytActs (p = 0.005). Conclusion: The deep learning model that predicts CytAct using FDG-PET of LUAD was validated in independent cohorts. Our approach may be used to noninvasively assess an immune profile and predict outcomes of LUAD patients treated with ICB.Affiliations
Changhee Park 1 , Kwon Joong Na 2 , Hongyoon Choi 3 , Chan-Young Ock 1 , Seunggyun Ha 4 , Miso Kim 1 , Samina Park 2 , Bhumsuk Keam 1 5 , Tae Min Kim 1 5 , Jin Chul Paeng 3 , In Kyu Park 2 , Chang Hyun Kang 2 , Dong-Wan Kim 1 5 , Gi-Jeong Cheon 3 5 , Keon Wook Kang 3 5 , Young Tae Kim 2 5 , Dae Seog Heo 1 5
1 Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
2 Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, Republic of Korea.
3 Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
4 Division of Nuclear Medicine, Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
5 Cancer Research Institute, Seoul National University, Seoul, Republic of Korea.
- 키워드
- Immunotherapy; deep learning; fluorodeoxyglucose positron emission tomography; gene expression profile; tumor microenvironment.
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