The correlation between PD-L1 expression and metabolic parameters of 18FDG PET/CT and the prognostic value of PD-L1 in non-small cell lung cancer

Correlation between PD-L1 expression and FDG-PET/CT visual assessments in non-small cell lung cancer resected specimens

PURPOSE

This retrospective study aimed to investigate the validity of fluorodeoxyglucose PET (FDG-PET) visual assessments to predict programmed death-ligand 1 (PD-L1) expression levels in patients with non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

One hundred and seven NSCLC patients who underwent FDG-PET/computed tomography (CT) scans and PD-L1 expression tests were retrospectively identified. Patients were divided into two groups according to PD-L1 expression: PD-L1 high group (PD-L1 tumor proportion score ≥50%) and PD-L1 low group (<50%). We compared clinicopathological characteristics and PET assessments [maximum standardized uptake value (SUVmax) and Deauville score] between the two groups based on PD-L1 expression.

RESULTS

High expression of PD-L1 was detected in 25 of 107 cases. In both univariable and multivariable analysis, there were significant differences in PET visual assessments in NSCLC (P < 0.05). Receiver operating characteristics for the PET visual assessments [area under the curve (AUC) = 0.712, 95% confidence interval (CI) 0.628-0.793] and SUVmax (AUC = 0.753, 95% CI 0.647-0.861) showed equivalent accuracy (P = 0.227). Based on histopathology, in adenocarcinoma patients, there were significant differences between PET visual assessments and PD-L1 expression (P < 0.05), while no significant differences were observed in squamous cell carcinoma patients. Based on epidermal growth factor receptor (EGFR) mutation analysis, in patients with EGFR wild type, there were significant differences between PET visual assessments and PD-L1 expression (P = 0.006), while in patients with EGFR mutations, there were no significant differences between PET visual assessments and PD-L1 expression.

CONCLUSION

Results of PET visual assessments correlated with PD-L1 expression in NSCLC.

Baseline 18F-FDG PET/CT parameters in predicting the efficacy of immunotherapy in non-small cell lung cancer

Objective

To analyse positron emission tomography/ computed tomography (PET/CT) imaging and clinical data from patients with non-small cell lung cancer (NSCLC), to identify characteristics of survival beneficiaries of immune checkpoint inhibitors (ICIs) treatment and to establish a survival prediction model.

Methods

A retrospective analysis was conducted on PET/CT imaging and clinical parameters of 155 NSCLC patients who underwent baseline PET/CT examination at the Department of Nuclear Medicine, Hebei General Hospital. The Kaplan-Meier curve was employed to compare progression-free survival (PFS) and overall survival (OS) between the ICIs and non-ICIs group and to assess the impact of variables on PFS and OS in the ICIs group. Multivariate Cox proportional hazards regression analysis was conducted with parameters significantly associated with survival in univariate analysis.

Results

Significant differences were observed in PFS (χ2  = 11.910, p = 0.0006) and OS (χ2  = 8.343, p = 0.0039). Independent predictors of PFS in the ICIs group included smoking history[hazard ratio (HR) = 2.522, 95% confidence interval (CI): 1.044 ~ 6.091, p = 0.0398], SUVmax of the primary lesion(HR = 0.2376, 95%CI: 0.1018 ~ 0.5548, p = 0.0009), MTVp (HR = 0.0755, 95%CI: 0.0284 ~ 0.2003, p < 0.001), and TLGp (HR = 0.1820, 95%CI: 0.0754 ~ 0.4395, p = 0.0002). These were also independent predictors of OS in the ICIs group[HR(95%CI) were 2.729 (1.125 ~ 6.619), 0.2636 (0.1143 ~ 0.6079), 0.0715 (0.0268 ~ 0.1907), 0.2102 (0.0885 ~ 0.4992), both p < 0.05)]. Age was an additional independent predictor of OS (HR = 0.4140, 95%CI: 0.1748 ~ 0.9801, p = 0.0449).

Conclusion

Smoking history, primary lesion SUVmax, MTVp, and TLGp were independent predictors of PFS, whilst age, smoking history, SUVmax, MTVp, and TLGp were independent predictors of OS in the ICIs group. Patients without a history of smoking and with SUVmax ≤19.2, MTVp ≤20.745cm3, TLGp ≤158.62 g, and age ≤ 60 years benefited more from ICI treatment.

Outcome prediction based on [18F]FDG PET/CT in patients with pleural mesothelioma treated with ipilimumab and nivolumab +/- UV1 telomerase vaccine

PURPOSE

The introduction of immunotherapy in pleural mesothelioma (PM) has highlighted the need for effective outcome predictors. This study explores the role of [18F]FDG PET/CT in predicting outcomes in PM treated with immunotherapy.

METHODS

Patients from the NIPU trial, receiving ipilimumab and nivolumab +/- telomerase vaccine in second-line, were included. [18F]FDG PET/CT was obtained at baseline (n = 100) and at week-5 (n = 76). Metabolic tumour volume (MTV) and peak standardised uptake value (SUVpeak) were evaluated in relation to survival outcomes. Wilcoxon rank-sum test was used to assess differences in MTV, total lesion glycolysis (TLG), maximum standardised uptake value (SUVmax) and SUVpeak between patients exhibiting an objective response, defined as either partial response or complete response according to the modified Response Criteria in Solid Tumours (mRECIST) and immune RECIST (iRECIST), and non-responders, defined as either stable disease or progressive disease as their best overall response.

RESULTS

Univariate Cox regression revealed significant associations of MTV with OS (HR 1.36, CI: 1.14, 1.62, p < 0.001) and PFS (HR 1.18, CI: 1.03, 1.34, p = 0.02), while multivariate analysis showed a significant association with OS only (HR 1.35, CI: 1.09, 1.68, p = 0.007). While SUVpeak was not significantly associated with OS or PFS in univariate analyses, it was significantly associated with OS in multivariate analysis (HR 0.43, CI: 0.23, 0.80, p = 0.008). Objective responders had significant reductions in TLG, SUVmax and SUVpeak at week-5.

CONCLUSION

MTV provides prognostic value in PM treated with immunotherapy. High SUVpeak was not associated with inferior outcomes, which could be attributed to the distinct mechanisms of immunotherapy. Early reductions in PET metrics correlated with treatment response.

STUDY REGISTRATION

The NIPU trial (NCT04300244) is registered at clinicaltrials.gov. https://classic.

CLINICALTRIALS

gov/ct2/show/NCT04300244?cond=Pleural+Mesothelioma&cntry=NO&draw=2&rank=4.

Clinical factors associated with high PD-L1 expression in patients with early-stage non-small cell lung cancer

BACKGROUND

Superior outcomes have been obtained for neoadjuvant treatment with immune checkpoint inhibitors (ICI) plus chemotherapy over neoadjuvant chemotherapy alone, especially in patients with high programmed cell death ligand 1 (PD-L1) expression. However, it is not always possible to obtain sufficient tumor specimens for biomarker testing before surgery. In this study, we explored clinical factors that can predict high PD-L1 expression.

METHODS

We retrospectively enrolled 340 lung cancer patients who received pulmonary resection between 2014 and 2023 and who had PD-L1 expression data. Chi-squared tests and logistic regression analyses were used to identify clinical factors associated with high PD-L1 status.

RESULTS

Univariable and multivariable analyses revealed that smoking, high maximum standardized uptake value (SUVmax) of 18F-fluorodeoxyglucose positron emission computed tomography (18F-FDG PET/CT), and high plasma fibrinogen are independent predictors of high PD-L1 expression. A predictive score for high PD-L1 expression (ranging from 0 to 3) was developed based on these parameters. Notably, only 5% of patients with a score of 0 exhibited high PD-L1 expression, whereas this proportion increased to 53% for patients with a score of 3.

CONCLUSION

These results showed that plasma fibrinogen, smoking history, and SUVmax are predictors of high PD-L1 expression, providing a basis for identifying patients expected to benefit from neoadjuvant ICI treatment.

Deep learning-based image analysis predicts PD-L1 status from 18F-FDG PET/CT images in non-small-cell lung cancer

Background

There is still a lack of clinically validated biomarkers to screen lung cancer patients suitable for programmed dead cell-1 (PD-1)/programmed dead cell receptor-1 (PD-L1) immunotherapy. Detection of PD-L1 expression is invasively operated, and some PD-L1-negative patients can also benefit from immunotherapy; thus, the joint modeling of both deep learning images and clinical features was used to improve the prediction performance of PD-L1 expression in non-small cell lung cancer (NSCLC).

Methods

Retrospective collection of 101 patients diagnosed with pathology in our hospital who underwent 18F FDG PET/CT scans, with lung cancer tissue Tumor Propulsion Score (TPS) ≥1% as a positive expression. Lesions were extracted after preprocessing PET/CT images, and using deep learning 3D DenseNet121 to learn lesions in PET, CT, and PET/CT images, 1,024 fully connected features were extracted; clinical features (age, gender, smoking/no smoking history, lesion diameter, lesion volume, maximum standard uptake value of lesions [SUVmax], mean standard uptake value of lesions [SUVmean], total lesion glycolysis [TLG]) were combined for joint modeling based on the structured data Category Embedding Model.

Results

Area under a receiver operating characteristic (ROC) curve (AUC) and accuracy of predicting PD-L1 positive for PET, CT, and PET/CT test groups were 0.814 ± 0.0152, 0.7212 ± 0.0861, and 0.90 ± 0.0605, 0.806 ± 0.023, 0.70 ± 0.074, and 0.950 ± 0.0250, respectively. After joint clinical feature modeling, the AUC and accuracy of predicting PD-L1 positive for PET/CT were 0.96 ± 0.00905 and 0.950 ± 0.0250, respectively.

Conclusion

This study combines the features of 18F-FDG PET/CT images with clinical features using deep learning to predict the expression of PD-L1 in NSCLC, suggesting that 18F-FDG PET/CT images can be conducted as biomarkers for PD-L1 expression.

Re: The correlation between PD-L1 expression and metabolic parameters of 18FDG PET/CT and the prognostic value of PD-L1 in non-small cell lung cancer

We read with great interest the recent study conducted by Xu et al1. In this study, the authors investigated the correlation of programmed death ligand-1 (PD-L1) expression with metabolic parameters measured by 18F-fluorodeoxyglucose positron emission computed tomography (¹⁸F-FDG PET/CT) and its prognostic value in patients with surgically resected non-small-cell lung cancer (NSCLC). The results of this study1 found that in NSCLC patients, the maximum standardized uptake value (SUVmax) measured by ¹⁸F-FDG PET/CT was a predictor of PD-L1 expression, and both PD-L1 and TLG were prognostic factors for the prognosis of NSCLC patient. Undoubtedly, the novel findings of this study help to further refine the risk stratification of NSCLC patients. However, after reading this article carefully, we would like to share the following points.