A Novel Approach Using FDG-PET/CT-Based Radiomics to Assess Tumor Immune Phenotypes in Patients With Non-Small Cell Lung Cancer

PurposeTumor microenvironment immune types (TMITs) are closely related to the efficacy of immunotherapy. We aimed to assess the predictive ability of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT)-based radiomics of TMITs in treatment-naive patients with non...

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Autores principales:	Jianyuan Zhou, Sijuan Zou, Dong Kuang, Jianhua Yan, Jun Zhao, Xiaohua Zhu
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	radiomics tumor microenvironment immune types non-small cell lung cancer 18F-FDG PET/CT PD-L1 Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/4d29906b00d94fca8c97d1a756b644f6
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spelling	oai:doaj.org-article:4d29906b00d94fca8c97d1a756b644f62021-11-17T05:54:23ZA Novel Approach Using FDG-PET/CT-Based Radiomics to Assess Tumor Immune Phenotypes in Patients With Non-Small Cell Lung Cancer2234-943X10.3389/fonc.2021.769272https://doaj.org/article/4d29906b00d94fca8c97d1a756b644f62021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fonc.2021.769272/fullhttps://doaj.org/toc/2234-943XPurposeTumor microenvironment immune types (TMITs) are closely related to the efficacy of immunotherapy. We aimed to assess the predictive ability of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT)-based radiomics of TMITs in treatment-naive patients with non-small cell lung cancer (NSCLC).MethodsA retrospective analysis was performed in 103 patients with NSCLC who underwent 18F-FDG PET/CT scans. The patients were randomly assigned into a training set (n = 71) and a validation set (n = 32). Tumor specimens were analyzed by immunohistochemistry for the expression of programmed death-ligand 1 (PD-L1), programmed death-1 (PD-1), and CD8+ tumor-infiltrating lymphocytes (TILs) and categorized into four TMITs according to their expression of PD-L1 and CD8+ TILs. LIFEx package was used to extract radiomic features. The optimal features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm, and a radiomics signature score (rad-score) was developed. We constructed a combined model based on the clinical variables and radiomics signature and compared the predictive performance of models using receiver operating characteristic (ROC) curves.ResultsFour radiomic features (GLRLM_LRHGE, GLZLM_SZE, SUVmax, NGLDM_Contrast) were selected to build the rad-score. The rad-score showed a significant ability to discriminate between TMITs in both sets (p < 0.001, p < 0.019), with an area under the ROC curve (AUC) of 0.800 [95% CI (0.688–0.885)] in the training set and that of 0.794 [95% CI (0.615–0.916)] in the validation set, while the AUC values of clinical variables were 0.738 and 0.699, respectively. When clinical variables and radiomics signature were combined, the complex model showed better performance in predicting TMIT-I tumors, with the AUC values increased to 0.838 [95% CI (0.731–0.914)] in the training set and 0.811 [95% CI (0.634–0.927)] in the validation set.ConclusionThe FDG-PET/CT-based radiomic features showed good performance in predicting TMIT-I tumors in NSCLC, providing a promising approach for the choice of immunotherapy in a clinical setting.Jianyuan ZhouSijuan ZouDong KuangJianhua YanJun ZhaoJun ZhaoXiaohua ZhuFrontiers Media S.A.articleradiomicstumor microenvironment immune typesnon-small cell lung cancer18F-FDG PET/CTPD-L1Neoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENFrontiers in Oncology, Vol 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	radiomics tumor microenvironment immune types non-small cell lung cancer 18F-FDG PET/CT PD-L1 Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	radiomics tumor microenvironment immune types non-small cell lung cancer 18F-FDG PET/CT PD-L1 Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Jianyuan Zhou Sijuan Zou Dong Kuang Jianhua Yan Jun Zhao Jun Zhao Xiaohua Zhu A Novel Approach Using FDG-PET/CT-Based Radiomics to Assess Tumor Immune Phenotypes in Patients With Non-Small Cell Lung Cancer
description	PurposeTumor microenvironment immune types (TMITs) are closely related to the efficacy of immunotherapy. We aimed to assess the predictive ability of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT)-based radiomics of TMITs in treatment-naive patients with non-small cell lung cancer (NSCLC).MethodsA retrospective analysis was performed in 103 patients with NSCLC who underwent 18F-FDG PET/CT scans. The patients were randomly assigned into a training set (n = 71) and a validation set (n = 32). Tumor specimens were analyzed by immunohistochemistry for the expression of programmed death-ligand 1 (PD-L1), programmed death-1 (PD-1), and CD8+ tumor-infiltrating lymphocytes (TILs) and categorized into four TMITs according to their expression of PD-L1 and CD8+ TILs. LIFEx package was used to extract radiomic features. The optimal features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm, and a radiomics signature score (rad-score) was developed. We constructed a combined model based on the clinical variables and radiomics signature and compared the predictive performance of models using receiver operating characteristic (ROC) curves.ResultsFour radiomic features (GLRLM_LRHGE, GLZLM_SZE, SUVmax, NGLDM_Contrast) were selected to build the rad-score. The rad-score showed a significant ability to discriminate between TMITs in both sets (p < 0.001, p < 0.019), with an area under the ROC curve (AUC) of 0.800 [95% CI (0.688–0.885)] in the training set and that of 0.794 [95% CI (0.615–0.916)] in the validation set, while the AUC values of clinical variables were 0.738 and 0.699, respectively. When clinical variables and radiomics signature were combined, the complex model showed better performance in predicting TMIT-I tumors, with the AUC values increased to 0.838 [95% CI (0.731–0.914)] in the training set and 0.811 [95% CI (0.634–0.927)] in the validation set.ConclusionThe FDG-PET/CT-based radiomic features showed good performance in predicting TMIT-I tumors in NSCLC, providing a promising approach for the choice of immunotherapy in a clinical setting.
format	article
author	Jianyuan Zhou Sijuan Zou Dong Kuang Jianhua Yan Jun Zhao Jun Zhao Xiaohua Zhu
author_facet	Jianyuan Zhou Sijuan Zou Dong Kuang Jianhua Yan Jun Zhao Jun Zhao Xiaohua Zhu
author_sort	Jianyuan Zhou
title	A Novel Approach Using FDG-PET/CT-Based Radiomics to Assess Tumor Immune Phenotypes in Patients With Non-Small Cell Lung Cancer
title_short	A Novel Approach Using FDG-PET/CT-Based Radiomics to Assess Tumor Immune Phenotypes in Patients With Non-Small Cell Lung Cancer
title_full	A Novel Approach Using FDG-PET/CT-Based Radiomics to Assess Tumor Immune Phenotypes in Patients With Non-Small Cell Lung Cancer
title_fullStr	A Novel Approach Using FDG-PET/CT-Based Radiomics to Assess Tumor Immune Phenotypes in Patients With Non-Small Cell Lung Cancer
title_full_unstemmed	A Novel Approach Using FDG-PET/CT-Based Radiomics to Assess Tumor Immune Phenotypes in Patients With Non-Small Cell Lung Cancer
title_sort	novel approach using fdg-pet/ct-based radiomics to assess tumor immune phenotypes in patients with non-small cell lung cancer
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/4d29906b00d94fca8c97d1a756b644f6
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A Novel Approach Using FDG-PET/CT-Based Radiomics to Assess Tumor Immune Phenotypes in Patients With Non-Small Cell Lung Cancer

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