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

Li, Jihui; Ge, Shushan; Sang, Shibiao; Hu, Chunhong; Deng, Shengming

doi:10.3389/fonc.2021.789014

Cited by 35 publications

(28 citation statements)

References 44 publications

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“…PET radiomics in pulmonary oncology gathered 107 articles [ 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 ,…”

Section: Resultsunclassified

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

et al. 2022

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Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.

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Section: Resultsunclassified

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

et al. 2022

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“…In our study, there were 36 patients (26%) expressing PD-L1 positively. Previous studies got different results between PD-L1 expression on TCs and SUVmax in patients with NSCLC (22,24,(29)(30)(31)(32)(33), ADC (23,34,35), and SCC (36,37). In NSCLC patients, Zhao et al (31) and Miyazawa et al (32) concluded that SUVmax is linked to PD-L1 high expression on TCs.…”

Section: Discussionmentioning

confidence: 98%

The predictive value of total-body PET/CT in non-small cell lung cancer for the PD-L1 high expression

Jin

et al. 2022

Front. Oncol.

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PurposeTotal-body positron emission tomography/computed tomography (PET/CT) provides faster scanning speed, higher image quality, and lower injected dose. To compensate for the shortcomings of the maximum standard uptake value (SUVmax), we aimed to normalize the values of PET parameters using liver and blood pool SUV (SUR-L and SUR-BP) to predict programmed cell death-ligand 1 (PD-L1) expression in non-small cell lung cancer (NSCLC) patients.Materials and methodsA total of 138 (104 adenocarcinoma and 34 squamous cell carcinoma) primary diagnosed NSCLC patients who underwent 18F-FDG-PET/CT imaging were analyzed retrospectively. Immunohistochemistry (IHC) analysis was performed for PD-L1 expression on tumor cells and tumor-infiltrating immune cells with 22C3 antibody. Positive PD-L1 expression was defined as tumor cells no less than 50% or tumor-infiltrating immune cells no less than 10%. The relationships between PD-L1 expression and PET parameters (SUVmax, SUR-L, and SUR-BP) and clinical variables were analyzed. Statistical analysis included χ2 test, receiver operating characteristic (ROC), and binary logistic regression.ResultsThere were 36 patients (26%) expressing PD-L1 positively. Gender, smoking history, Ki-67, and histologic subtype were related factors. SUVmax, SUR-L, and SUR-BP were significantly higher in the positive subset than those in the negative subset. Among them, the area under the curve (AUC) of SUR-L on the ROC curve was the biggest one. In NSCLC patients, the best cutoff value of SUR-L for PD-L1-positive expression was 4.84 (AUC = 0.702, P = 0.000, sensitivity = 83.3%, specificity = 54.9%). Multivariate analysis confirmed that age and SUR-L were correlated factors in adenocarcinoma (ADC) patients.ConclusionSUVmax, SUR-L, and SUR-BP had utility in predicting PD-L1 high expression, and SUR-L was the most reliable parameter. PET/CT can offer reference to screen patients for first-line atezolizumab therapy.

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“…Combining three different features resulted in the prediction of PD-L1 values between 1 and 49% with an AUC of 0.81 in the validation cohort. With differences relying mainly on dataset size, methodological choices and clinical endpoints, a variety of radiomics signatures were thus recently published, the majority focusing on the prediction of PD-L1/TMB expression [ 86 , 87 , 88 , 89 , 90 , 91 , 92 ] or even the assessment of TILs [ 93 ].…”

Section: Radiomics/deep-learning: the One To Unite Them All?mentioning

confidence: 99%

Multi-Omics Approaches for the Prediction of Clinical Endpoints after Immunotherapy in Non-Small Cell Lung Cancer: A Comprehensive Review

et al. 2022

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Immune checkpoint inhibitors (ICI) have revolutionized the management of locally advanced and advanced non-small lung cancer (NSCLC). With an improvement in the overall survival (OS) as both first- and second-line treatments, ICIs, and especially programmed-death 1 (PD-1) and programmed-death ligands 1 (PD-L1), changed the landscape of thoracic oncology. The PD-L1 level of expression is commonly accepted as the most used biomarker, with both prognostic and predictive values. However, even in a low expression level of PD-L1, response rates remain significant while a significant number of patients will experience hyperprogression or adverse events. The dentification of such subtypes is thus of paramount importance. While several studies focused mainly on the prediction of the PD-L1 expression status, others aimed directly at the development of prediction/prognostic models. The response to ICIs depends on a complex physiopathological cascade, intricating multiple mechanisms from the molecular to the macroscopic level. With the high-throughput extraction of features, omics approaches aim for the most comprehensive assessment of each patient. In this article, we will review the place of the different biomarkers (clinical, biological, genomics, transcriptomics, proteomics and radiomics), their clinical implementation and discuss the most recent trends projecting on the future steps in prediction modeling in NSCLC patients treated with ICI.

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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

Cited by 35 publications

References 44 publications

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

The predictive value of total-body PET/CT in non-small cell lung cancer for the PD-L1 high expression

Multi-Omics Approaches for the Prediction of Clinical Endpoints after Immunotherapy in Non-Small Cell Lung Cancer: A Comprehensive Review

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