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Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma
DC Field | Value | Language |
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dc.contributor.author | Park, Changhee | - |
dc.contributor.author | Na, Kwon Joong | - |
dc.contributor.author | Choi, Hongyoon | - |
dc.contributor.author | Ock, Chan-Young | - |
dc.contributor.author | Ha, Seunggyun | - |
dc.contributor.author | Kim, Miso | - |
dc.contributor.author | Park, Samina | - |
dc.contributor.author | Keam, Bhumsuk | - |
dc.contributor.author | Kim, Tae Min | - |
dc.contributor.author | Paeng, Jin Chul | - |
dc.contributor.author | Park, In Kyu | - |
dc.contributor.author | Kang, Chang Hyun | - |
dc.contributor.author | Kim, Dong-Wan | - |
dc.contributor.author | Cheon, Gi-Jeong | - |
dc.contributor.author | Kang, Keon Wook | - |
dc.contributor.author | Kim, Young Tae | - |
dc.contributor.author | Heo, Dae Seog | - |
dc.date.accessioned | 2021-01-31T08:06:50Z | - |
dc.date.available | 2021-01-31T08:06:50Z | - |
dc.date.created | 2020-10-06 | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Theranostics, Vol.10 No.23, pp.10838-10848 | - |
dc.identifier.issn | 1838-7640 | - |
dc.identifier.other | 112732 | - |
dc.identifier.uri | https://hdl.handle.net/10371/171815 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.publisher | Ivyspring International Publisher | - |
dc.title | Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma | - |
dc.type | Article | - |
dc.contributor.AlternativeAuthor | 천기정 | - |
dc.contributor.AlternativeAuthor | 박새미나 | - |
dc.contributor.AlternativeAuthor | 김동완 | - |
dc.contributor.AlternativeAuthor | 강창현 | - |
dc.contributor.AlternativeAuthor | 박인규 | - |
dc.contributor.AlternativeAuthor | 강건욱 | - |
dc.contributor.AlternativeAuthor | 김영태 | - |
dc.contributor.AlternativeAuthor | 허대석 | - |
dc.identifier.doi | 10.7150/thno.50283 | - |
dc.citation.journaltitle | Theranostics | - |
dc.identifier.wosid | 000566786000015 | - |
dc.identifier.scopusid | 2-s2.0-85090759296 | - |
dc.citation.endpage | 10848 | - |
dc.citation.number | 23 | - |
dc.citation.startpage | 10838 | - |
dc.citation.volume | 10 | - |
dc.identifier.sci | 000566786000015 | - |
dc.description.isOpenAccess | Y | - |
dc.contributor.affiliatedAuthor | Park, Samina | - |
dc.contributor.affiliatedAuthor | Paeng, Jin Chul | - |
dc.contributor.affiliatedAuthor | Park, In Kyu | - |
dc.contributor.affiliatedAuthor | Kang, Chang Hyun | - |
dc.contributor.affiliatedAuthor | Kim, Dong-Wan | - |
dc.contributor.affiliatedAuthor | Cheon, Gi-Jeong | - |
dc.contributor.affiliatedAuthor | Kang, Keon Wook | - |
dc.contributor.affiliatedAuthor | Kim, Young Tae | - |
dc.contributor.affiliatedAuthor | Heo, Dae Seog | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordPlus | CANCER | - |
dc.subject.keywordPlus | HETEROGENEITY | - |
dc.subject.keywordPlus | RESISTANCE | - |
dc.subject.keywordPlus | EXPRESSION | - |
dc.subject.keywordPlus | METASTASES | - |
dc.subject.keywordPlus | CRITERIA | - |
dc.subject.keywordAuthor | Immunotherapy | - |
dc.subject.keywordAuthor | tumor microenvironment | - |
dc.subject.keywordAuthor | fluorodeoxyglucose positron emission tomography | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | gene expression profile | - |
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