Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics

PurposeIn the present study, we aimed to evaluate the expression of programmed death-ligand 1 (PD-L1) in patients with non-small cell lung cancer (NSCLC) by radiomic features of 18F-FDG PET/CT and clinicopathological characteristics.MethodsA total 255 NSCLC patients (training cohort: n = 170; valida...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Jihui Li [verfasserIn] Shushan Ge [verfasserIn] Shibiao Sang [verfasserIn] Chunhong Hu [verfasserIn] Shengming Deng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	non-small cell lung cancer PD-L1 immunotherapy radiomics 18F-FDG PET/CT clinicopathological

Übergeordnetes Werk:	In: Frontiers in Oncology - Frontiers Media S.A., 2012, 11(2021)
Übergeordnetes Werk:	volume:11 ; year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fonc.2021.789014

Katalog-ID:	DOAJ01103744X

Internformat


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520			\|a PurposeIn the present study, we aimed to evaluate the expression of programmed death-ligand 1 (PD-L1) in patients with non-small cell lung cancer (NSCLC) by radiomic features of 18F-FDG PET/CT and clinicopathological characteristics.MethodsA total 255 NSCLC patients (training cohort: n = 170; validation cohort: n = 85) were retrospectively enrolled in the present study. A total of 80 radiomic features were extracted from pretreatment 18F-FDG PET/CT images. Clinicopathologic features were compared between the two cohorts. The least absolute shrinkage and selection operator (LASSO) regression was used to select the most useful prognostic features in the training cohort. Radiomics signature and clinicopathologic risk factors were incorporated to develop a prediction model by using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the prognostic factors.ResultsA total of 80 radiomic features were extracted in the training dataset. In the univariate analysis, the expression of PD-L1 in lung tumors was significantly correlated with the radiomic signature, histologic type, Ki-67, SUVmax, MTV, and TLG (p< 0.05, respectively). However, the expression of PD-L1 was not correlated with age, TNM stage, and history of smoking (p> 0.05). Moreover, the prediction model for PD-L1 expression level over 1% and 50% that combined the radiomic signature and clinicopathologic features resulted in an area under the curve (AUC) of 0.762 and 0.814, respectively.ConclusionsA prediction model based on PET/CT images and clinicopathological characteristics provided a novel strategy for clinicians to screen the NSCLC patients who could benefit from the anti-PD-L1 immunotherapy.
650		4	\|a non-small cell lung cancer
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650		4	\|a radiomics
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653		0	\|a Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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allfields	10.3389/fonc.2021.789014 doi (DE-627)DOAJ01103744X (DE-599)DOAJ6ca9815ec5654ae09d89fae69f4be6d8 DE-627 ger DE-627 rakwb eng RC254-282 Jihui Li verfasserin aut Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier PurposeIn the present study, we aimed to evaluate the expression of programmed death-ligand 1 (PD-L1) in patients with non-small cell lung cancer (NSCLC) by radiomic features of 18F-FDG PET/CT and clinicopathological characteristics.MethodsA total 255 NSCLC patients (training cohort: n = 170; validation cohort: n = 85) were retrospectively enrolled in the present study. A total of 80 radiomic features were extracted from pretreatment 18F-FDG PET/CT images. Clinicopathologic features were compared between the two cohorts. The least absolute shrinkage and selection operator (LASSO) regression was used to select the most useful prognostic features in the training cohort. Radiomics signature and clinicopathologic risk factors were incorporated to develop a prediction model by using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the prognostic factors.ResultsA total of 80 radiomic features were extracted in the training dataset. In the univariate analysis, the expression of PD-L1 in lung tumors was significantly correlated with the radiomic signature, histologic type, Ki-67, SUVmax, MTV, and TLG (p< 0.05, respectively). However, the expression of PD-L1 was not correlated with age, TNM stage, and history of smoking (p> 0.05). Moreover, the prediction model for PD-L1 expression level over 1% and 50% that combined the radiomic signature and clinicopathologic features resulted in an area under the curve (AUC) of 0.762 and 0.814, respectively.ConclusionsA prediction model based on PET/CT images and clinicopathological characteristics provided a novel strategy for clinicians to screen the NSCLC patients who could benefit from the anti-PD-L1 immunotherapy. non-small cell lung cancer PD-L1 immunotherapy radiomics 18F-FDG PET/CT clinicopathological Neoplasms. Tumors. Oncology. Including cancer and carcinogens Shushan Ge verfasserin aut Shibiao Sang verfasserin aut Chunhong Hu verfasserin aut Shengming Deng verfasserin aut Shengming Deng verfasserin aut Shengming Deng verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 11(2021) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:11 year:2021 https://doi.org/10.3389/fonc.2021.789014 kostenfrei https://doaj.org/article/6ca9815ec5654ae09d89fae69f4be6d8 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2021.789014/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021
spelling	10.3389/fonc.2021.789014 doi (DE-627)DOAJ01103744X (DE-599)DOAJ6ca9815ec5654ae09d89fae69f4be6d8 DE-627 ger DE-627 rakwb eng RC254-282 Jihui Li verfasserin aut Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier PurposeIn the present study, we aimed to evaluate the expression of programmed death-ligand 1 (PD-L1) in patients with non-small cell lung cancer (NSCLC) by radiomic features of 18F-FDG PET/CT and clinicopathological characteristics.MethodsA total 255 NSCLC patients (training cohort: n = 170; validation cohort: n = 85) were retrospectively enrolled in the present study. A total of 80 radiomic features were extracted from pretreatment 18F-FDG PET/CT images. Clinicopathologic features were compared between the two cohorts. The least absolute shrinkage and selection operator (LASSO) regression was used to select the most useful prognostic features in the training cohort. Radiomics signature and clinicopathologic risk factors were incorporated to develop a prediction model by using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the prognostic factors.ResultsA total of 80 radiomic features were extracted in the training dataset. In the univariate analysis, the expression of PD-L1 in lung tumors was significantly correlated with the radiomic signature, histologic type, Ki-67, SUVmax, MTV, and TLG (p< 0.05, respectively). However, the expression of PD-L1 was not correlated with age, TNM stage, and history of smoking (p> 0.05). Moreover, the prediction model for PD-L1 expression level over 1% and 50% that combined the radiomic signature and clinicopathologic features resulted in an area under the curve (AUC) of 0.762 and 0.814, respectively.ConclusionsA prediction model based on PET/CT images and clinicopathological characteristics provided a novel strategy for clinicians to screen the NSCLC patients who could benefit from the anti-PD-L1 immunotherapy. non-small cell lung cancer PD-L1 immunotherapy radiomics 18F-FDG PET/CT clinicopathological Neoplasms. Tumors. Oncology. Including cancer and carcinogens Shushan Ge verfasserin aut Shibiao Sang verfasserin aut Chunhong Hu verfasserin aut Shengming Deng verfasserin aut Shengming Deng verfasserin aut Shengming Deng verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 11(2021) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:11 year:2021 https://doi.org/10.3389/fonc.2021.789014 kostenfrei https://doaj.org/article/6ca9815ec5654ae09d89fae69f4be6d8 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2021.789014/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021
allfields_unstemmed	10.3389/fonc.2021.789014 doi (DE-627)DOAJ01103744X (DE-599)DOAJ6ca9815ec5654ae09d89fae69f4be6d8 DE-627 ger DE-627 rakwb eng RC254-282 Jihui Li verfasserin aut Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier PurposeIn the present study, we aimed to evaluate the expression of programmed death-ligand 1 (PD-L1) in patients with non-small cell lung cancer (NSCLC) by radiomic features of 18F-FDG PET/CT and clinicopathological characteristics.MethodsA total 255 NSCLC patients (training cohort: n = 170; validation cohort: n = 85) were retrospectively enrolled in the present study. A total of 80 radiomic features were extracted from pretreatment 18F-FDG PET/CT images. Clinicopathologic features were compared between the two cohorts. The least absolute shrinkage and selection operator (LASSO) regression was used to select the most useful prognostic features in the training cohort. Radiomics signature and clinicopathologic risk factors were incorporated to develop a prediction model by using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the prognostic factors.ResultsA total of 80 radiomic features were extracted in the training dataset. In the univariate analysis, the expression of PD-L1 in lung tumors was significantly correlated with the radiomic signature, histologic type, Ki-67, SUVmax, MTV, and TLG (p< 0.05, respectively). However, the expression of PD-L1 was not correlated with age, TNM stage, and history of smoking (p> 0.05). Moreover, the prediction model for PD-L1 expression level over 1% and 50% that combined the radiomic signature and clinicopathologic features resulted in an area under the curve (AUC) of 0.762 and 0.814, respectively.ConclusionsA prediction model based on PET/CT images and clinicopathological characteristics provided a novel strategy for clinicians to screen the NSCLC patients who could benefit from the anti-PD-L1 immunotherapy. non-small cell lung cancer PD-L1 immunotherapy radiomics 18F-FDG PET/CT clinicopathological Neoplasms. Tumors. Oncology. Including cancer and carcinogens Shushan Ge verfasserin aut Shibiao Sang verfasserin aut Chunhong Hu verfasserin aut Shengming Deng verfasserin aut Shengming Deng verfasserin aut Shengming Deng verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 11(2021) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:11 year:2021 https://doi.org/10.3389/fonc.2021.789014 kostenfrei https://doaj.org/article/6ca9815ec5654ae09d89fae69f4be6d8 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2021.789014/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021
allfieldsGer	10.3389/fonc.2021.789014 doi (DE-627)DOAJ01103744X (DE-599)DOAJ6ca9815ec5654ae09d89fae69f4be6d8 DE-627 ger DE-627 rakwb eng RC254-282 Jihui Li verfasserin aut Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier PurposeIn the present study, we aimed to evaluate the expression of programmed death-ligand 1 (PD-L1) in patients with non-small cell lung cancer (NSCLC) by radiomic features of 18F-FDG PET/CT and clinicopathological characteristics.MethodsA total 255 NSCLC patients (training cohort: n = 170; validation cohort: n = 85) were retrospectively enrolled in the present study. A total of 80 radiomic features were extracted from pretreatment 18F-FDG PET/CT images. Clinicopathologic features were compared between the two cohorts. The least absolute shrinkage and selection operator (LASSO) regression was used to select the most useful prognostic features in the training cohort. Radiomics signature and clinicopathologic risk factors were incorporated to develop a prediction model by using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the prognostic factors.ResultsA total of 80 radiomic features were extracted in the training dataset. In the univariate analysis, the expression of PD-L1 in lung tumors was significantly correlated with the radiomic signature, histologic type, Ki-67, SUVmax, MTV, and TLG (p< 0.05, respectively). However, the expression of PD-L1 was not correlated with age, TNM stage, and history of smoking (p> 0.05). Moreover, the prediction model for PD-L1 expression level over 1% and 50% that combined the radiomic signature and clinicopathologic features resulted in an area under the curve (AUC) of 0.762 and 0.814, respectively.ConclusionsA prediction model based on PET/CT images and clinicopathological characteristics provided a novel strategy for clinicians to screen the NSCLC patients who could benefit from the anti-PD-L1 immunotherapy. non-small cell lung cancer PD-L1 immunotherapy radiomics 18F-FDG PET/CT clinicopathological Neoplasms. Tumors. Oncology. Including cancer and carcinogens Shushan Ge verfasserin aut Shibiao Sang verfasserin aut Chunhong Hu verfasserin aut Shengming Deng verfasserin aut Shengming Deng verfasserin aut Shengming Deng verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 11(2021) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:11 year:2021 https://doi.org/10.3389/fonc.2021.789014 kostenfrei https://doaj.org/article/6ca9815ec5654ae09d89fae69f4be6d8 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2021.789014/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021
allfieldsSound	10.3389/fonc.2021.789014 doi (DE-627)DOAJ01103744X (DE-599)DOAJ6ca9815ec5654ae09d89fae69f4be6d8 DE-627 ger DE-627 rakwb eng RC254-282 Jihui Li verfasserin aut Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier PurposeIn the present study, we aimed to evaluate the expression of programmed death-ligand 1 (PD-L1) in patients with non-small cell lung cancer (NSCLC) by radiomic features of 18F-FDG PET/CT and clinicopathological characteristics.MethodsA total 255 NSCLC patients (training cohort: n = 170; validation cohort: n = 85) were retrospectively enrolled in the present study. A total of 80 radiomic features were extracted from pretreatment 18F-FDG PET/CT images. Clinicopathologic features were compared between the two cohorts. The least absolute shrinkage and selection operator (LASSO) regression was used to select the most useful prognostic features in the training cohort. Radiomics signature and clinicopathologic risk factors were incorporated to develop a prediction model by using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the prognostic factors.ResultsA total of 80 radiomic features were extracted in the training dataset. In the univariate analysis, the expression of PD-L1 in lung tumors was significantly correlated with the radiomic signature, histologic type, Ki-67, SUVmax, MTV, and TLG (p< 0.05, respectively). However, the expression of PD-L1 was not correlated with age, TNM stage, and history of smoking (p> 0.05). Moreover, the prediction model for PD-L1 expression level over 1% and 50% that combined the radiomic signature and clinicopathologic features resulted in an area under the curve (AUC) of 0.762 and 0.814, respectively.ConclusionsA prediction model based on PET/CT images and clinicopathological characteristics provided a novel strategy for clinicians to screen the NSCLC patients who could benefit from the anti-PD-L1 immunotherapy. non-small cell lung cancer PD-L1 immunotherapy radiomics 18F-FDG PET/CT clinicopathological Neoplasms. Tumors. Oncology. Including cancer and carcinogens Shushan Ge verfasserin aut Shibiao Sang verfasserin aut Chunhong Hu verfasserin aut Shengming Deng verfasserin aut Shengming Deng verfasserin aut Shengming Deng verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 11(2021) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:11 year:2021 https://doi.org/10.3389/fonc.2021.789014 kostenfrei https://doaj.org/article/6ca9815ec5654ae09d89fae69f4be6d8 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2021.789014/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021
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Moreover, the prediction model for PD-L1 expression level over 1% and 50% that combined the radiomic signature and clinicopathologic features resulted in an area under the curve (AUC) of 0.762 and 0.814, respectively.ConclusionsA prediction model based on PET/CT images and clinicopathological characteristics provided a novel strategy for clinicians to screen the NSCLC patients who could benefit from the anti-PD-L1 immunotherapy.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">non-small cell lung cancer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">PD-L1 immunotherapy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">radiomics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">18F-FDG PET/CT</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">clinicopathological</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neoplasms. 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callnumber-first	R - Medicine
author	Jihui Li
spellingShingle	Jihui Li misc RC254-282 misc non-small cell lung cancer misc PD-L1 immunotherapy misc radiomics misc 18F-FDG PET/CT misc clinicopathological misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics
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topic_title	RC254-282 Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics non-small cell lung cancer PD-L1 immunotherapy radiomics 18F-FDG PET/CT clinicopathological
topic	misc RC254-282 misc non-small cell lung cancer misc PD-L1 immunotherapy misc radiomics misc 18F-FDG PET/CT misc clinicopathological misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc non-small cell lung cancer misc PD-L1 immunotherapy misc radiomics misc 18F-FDG PET/CT misc clinicopathological misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_browse	misc RC254-282 misc non-small cell lung cancer misc PD-L1 immunotherapy misc radiomics misc 18F-FDG PET/CT misc clinicopathological misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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title	Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics
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title_full	Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics
author_sort	Jihui Li
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author_browse	Jihui Li Shushan Ge Shibiao Sang Chunhong Hu Shengming Deng
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title_sort	evaluation of pd-l1 expression level in patients with non-small cell lung cancer by 18f-fdg pet/ct radiomics and clinicopathological characteristics
callnumber	RC254-282
title_auth	Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics
abstract	PurposeIn the present study, we aimed to evaluate the expression of programmed death-ligand 1 (PD-L1) in patients with non-small cell lung cancer (NSCLC) by radiomic features of 18F-FDG PET/CT and clinicopathological characteristics.MethodsA total 255 NSCLC patients (training cohort: n = 170; validation cohort: n = 85) were retrospectively enrolled in the present study. A total of 80 radiomic features were extracted from pretreatment 18F-FDG PET/CT images. Clinicopathologic features were compared between the two cohorts. The least absolute shrinkage and selection operator (LASSO) regression was used to select the most useful prognostic features in the training cohort. Radiomics signature and clinicopathologic risk factors were incorporated to develop a prediction model by using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the prognostic factors.ResultsA total of 80 radiomic features were extracted in the training dataset. In the univariate analysis, the expression of PD-L1 in lung tumors was significantly correlated with the radiomic signature, histologic type, Ki-67, SUVmax, MTV, and TLG (p< 0.05, respectively). However, the expression of PD-L1 was not correlated with age, TNM stage, and history of smoking (p> 0.05). Moreover, the prediction model for PD-L1 expression level over 1% and 50% that combined the radiomic signature and clinicopathologic features resulted in an area under the curve (AUC) of 0.762 and 0.814, respectively.ConclusionsA prediction model based on PET/CT images and clinicopathological characteristics provided a novel strategy for clinicians to screen the NSCLC patients who could benefit from the anti-PD-L1 immunotherapy.
abstractGer	PurposeIn the present study, we aimed to evaluate the expression of programmed death-ligand 1 (PD-L1) in patients with non-small cell lung cancer (NSCLC) by radiomic features of 18F-FDG PET/CT and clinicopathological characteristics.MethodsA total 255 NSCLC patients (training cohort: n = 170; validation cohort: n = 85) were retrospectively enrolled in the present study. A total of 80 radiomic features were extracted from pretreatment 18F-FDG PET/CT images. Clinicopathologic features were compared between the two cohorts. The least absolute shrinkage and selection operator (LASSO) regression was used to select the most useful prognostic features in the training cohort. Radiomics signature and clinicopathologic risk factors were incorporated to develop a prediction model by using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the prognostic factors.ResultsA total of 80 radiomic features were extracted in the training dataset. In the univariate analysis, the expression of PD-L1 in lung tumors was significantly correlated with the radiomic signature, histologic type, Ki-67, SUVmax, MTV, and TLG (p< 0.05, respectively). However, the expression of PD-L1 was not correlated with age, TNM stage, and history of smoking (p> 0.05). Moreover, the prediction model for PD-L1 expression level over 1% and 50% that combined the radiomic signature and clinicopathologic features resulted in an area under the curve (AUC) of 0.762 and 0.814, respectively.ConclusionsA prediction model based on PET/CT images and clinicopathological characteristics provided a novel strategy for clinicians to screen the NSCLC patients who could benefit from the anti-PD-L1 immunotherapy.
abstract_unstemmed	PurposeIn the present study, we aimed to evaluate the expression of programmed death-ligand 1 (PD-L1) in patients with non-small cell lung cancer (NSCLC) by radiomic features of 18F-FDG PET/CT and clinicopathological characteristics.MethodsA total 255 NSCLC patients (training cohort: n = 170; validation cohort: n = 85) were retrospectively enrolled in the present study. A total of 80 radiomic features were extracted from pretreatment 18F-FDG PET/CT images. Clinicopathologic features were compared between the two cohorts. The least absolute shrinkage and selection operator (LASSO) regression was used to select the most useful prognostic features in the training cohort. Radiomics signature and clinicopathologic risk factors were incorporated to develop a prediction model by using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the prognostic factors.ResultsA total of 80 radiomic features were extracted in the training dataset. In the univariate analysis, the expression of PD-L1 in lung tumors was significantly correlated with the radiomic signature, histologic type, Ki-67, SUVmax, MTV, and TLG (p< 0.05, respectively). However, the expression of PD-L1 was not correlated with age, TNM stage, and history of smoking (p> 0.05). Moreover, the prediction model for PD-L1 expression level over 1% and 50% that combined the radiomic signature and clinicopathologic features resulted in an area under the curve (AUC) of 0.762 and 0.814, respectively.ConclusionsA prediction model based on PET/CT images and clinicopathological characteristics provided a novel strategy for clinicians to screen the NSCLC patients who could benefit from the anti-PD-L1 immunotherapy.
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title_short	Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics
url	https://doi.org/10.3389/fonc.2021.789014 https://doaj.org/article/6ca9815ec5654ae09d89fae69f4be6d8 https://www.frontiersin.org/articles/10.3389/fonc.2021.789014/full https://doaj.org/toc/2234-943X
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author2	Shushan Ge Shibiao Sang Chunhong Hu Shengming Deng
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Moreover, the prediction model for PD-L1 expression level over 1% and 50% that combined the radiomic signature and clinicopathologic features resulted in an area under the curve (AUC) of 0.762 and 0.814, respectively.ConclusionsA prediction model based on PET/CT images and clinicopathological characteristics provided a novel strategy for clinicians to screen the NSCLC patients who could benefit from the anti-PD-L1 immunotherapy.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">non-small cell lung cancer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">PD-L1 immunotherapy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">radiomics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">18F-FDG PET/CT</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">clinicopathological</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neoplasms. 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Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics

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