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Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma

Cited 3 time in Web of Science Cited 3 time in Scopus
Authors
Park, Changhee; Na, Kwon Joong; Choi, Hongyoon; Ock, Chan-Young; Ha, Seunggyun; Kim, Miso; Park, Samina; Keam, Bhumsuk; Kim, Tae Min; Paeng, Jin Chul; Park, In Kyu; Kang, Chang Hyun; Kim, Dong-Wan; Cheon, Gi-Jeong; Kang, Keon Wook; Kim, Young Tae; Heo, Dae Seog
Issue Date
2020
Citation
Theranostics, Vol.10 No.23, pp.10838-10848
Keywords
Immunotherapytumor microenvironmentfluorodeoxyglucose positron emission tomographydeep learninggene expression profile
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.
ISSN
1838-7640
URI
https://hdl.handle.net/10371/171815
DOI
https://doi.org/10.7150/thno.50283
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College of Medicine/School of Medicine (의과대학/대학원)Internal Medicine (내과학전공)Journal Papers (저널논문_내과학전공)
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