Construction of A Nomogram Prediction Model for PD-L1 Expression in Non-small Cell Lung Cancer Based on 18F-FDG PET/CT Metabolic Parameters / 中国肺癌杂志

BACKGROUND@#In recent years, immunotherapy represented by programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immunosuppressants has greatly changed the status of non-small cell lung cancer (NSCLC) treatment. PD-L1 has become an important biomarker for screening NSCLC immunotherapy beneficiaries, but how to easily and accurately detect whether PD-L1 is expressed in NSCLC patients is a difficult problem for clinicians. The aim of this study was to construct a Nomogram prediction model of PD-L1 expression in NSCLC patients based on 18F-fluorodeoxy glucose (18F-FDG) positron emission tomography/conputed tomography (PET/CT) metabolic parameters and to evaluate its predictive value.@*METHODS@#Retrospective collection of 18F-FDG PET/CT metabolic parameters, clinicopathological information and PD-L1 test results of 155 NSCLC patients from Inner Mongolia People's Hospital between September 2016 and July 2021. The patients were divided into the training group (n=117) and the internal validation group (n=38), and another 51 cases of NSCLC patients in our hospital between August 2021 and July 2022 were collected as the external validation group according to the same criteria. Then all of them were categorized according to the results of PD-L1 assay into PD-L1+ group and PD-L1- group. The metabolic parameters and clinicopathological information of patients in the training group were analyzed by univariate and binary Logistic regression, and a Nomogram prediction model was constructed based on the screened independent influencing factors. The effect of the model was evaluated by receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) in both the training group and the internal and external validation groups.@*RESULTS@#Binary Logistic regression analysis showed that metabolic tumor volume (MTV), gender and tumor diameter were independent influences on PD-L1 expression. Then a Nomogram prediction model was constructed based on the above independent influences. The ROC curve for the model in the training group shows an area under the curve (AUC) of 0.769 (95%CI: 0.683-0.856) with an optimal cutoff value of 0.538. The AUC was 0.775 (95%CI: 0.614-0.936) in the internal validation group and 0.752 (95%CI: 0.612-0.893) in the external validation group. The calibration curves were tested by the Hosmer-Lemeshow test and showed that the training group (χ2=0.040, P=0.979), the internal validation group (χ2=2.605, P=0.271), and the external validation group (χ2=0.396, P=0.820) were well calibrated. The DCA curves show that the model provides clinical benefit to patients over a wide range of thresholds (training group: 0.00-0.72, internal validation group: 0.00-0.87, external validation group: 0.00-0.66).@*CONCLUSIONS@#The Nomogram prediction model constructed on the basis of 18F-FDG PET/CT metabolic parameters has greater application value in predicting PD-L1 expression in NSCLC patients.

Imaging Characteristics of Stage I Non-Small Cell Lung Cancer on CT and FDG-PET: Relationship with Epidermal Growth Factor Receptor Protein Expression Status and Survival

OBJECTIVE: To identify CT and FDG-PET features associated with epidermal growth factor receptor (EGFR) protein overexpression, and to evaluate whether imaging features and EGFR-overexpression can help predict clinical outcome. MATERIALS AND METHODS: In 214 patients (M : F = 129 : 85; mean age, 63.2) who underwent curative resection of stage I non-small cell lung cancer, EGFR protein expression status was determined through immunohistochemical analysis. Imaging characteristics on CT and FDG-PET was assessed in relation to EGFR-overexpression. Imaging features and EGFR-overexpression were also evaluated for clinical outcome by using the Cox proportional hazards model. RESULTS: EGFR-overexpression was found in 51 patients (23.8%). It was significantly more frequent in tumors with an SUVmax > 5.0 (p 2.43 cm (p 5.0 (OR, 3.113; 95% CI, 1.375-7.049; p = 0.006) and diameter > 2.43 cm (OR, 2.799; 95% CI, 1.285-6.095; p = 0.010) were independent predictors of EGFR overexpression. Multivariate analysis showed that SUVmax > 4.0 (hazard ratio, 10.660; 95% CI, 1.370-82.966; p = 0.024), and the presence of cavitation within a tumor (hazard ratio, 3.122; 95% CI, 1.143-8.532; p = 0.026) were factors associated with poor prognosis. CONCLUSION: EGFR-overexpression is associated with high SUVmax, large tumor diameter, and small GGO proportion. CT and FDG-PET findings, which are closely related to EGFR overexpression, can be valuable in the prediction of clinical outcome.