Dataset Information

Predicting treatment outcomes using ¹⁸F-FDG PET biomarkers in patients with non-small-cell lung cancer receiving chemoimmunotherapy.

ABSTRACT:

Background

Predictive markers for treatment response and survival outcome have not been identified in patients with advanced non-small-cell lung cancer (NSCLC) receiving chemoimmunotherapy. We aimed to evaluate whether imaging biomarkers of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) and routinely assessed clinico-laboratory values were associated with clinical outcomes in patients with advanced NSCLC receiving pembrolizumab plus platinum-doublet chemotherapy as a first-line treatment.

Methods

We retrospectively enrolled 52 patients with advanced NSCLC who underwent baseline ¹⁸F-FDG PET/CT before treatment initiation. PET/CT parameters and clinico-laboratory variables, constituting the prognostic immunotherapy scoring system, were collected. Optimal cut-off values for PET/CT parameters were determined using the maximized log-rank test for progression-free survival (PFS). A multivariate prediction model was developed based on Cox models for PFS, and a scoring system was established based on hazard ratios of the predictive factors.

Results

During the median follow-up period of 16.7 months (95% confidence interval: 15.7-17.7 months), 43 (82.7%) and 31 (59.6%) patients experienced disease progression and death, respectively. Objective response was observed in 23 (44.2%) patients. In the multivariate analysis, maximum standardized uptake value, metabolic tumour volume_2.5, total lesion glycolysis_2.5, and bone marrow-to-liver uptake ratio from the PET/CT variables and neutrophil-to-lymphocyte ratio (NLR) from the clinico-laboratory variables were independently associated with PFS. The scoring system based on these independent predictive variables significantly predicted the treatment response, PFS, and overall survival.

Conclusion

PET/CT variables and NLR were useful biomarkers for predicting outcomes of patients with NSCLC receiving pembrolizumab and chemotherapy as a first-line treatment, suggesting their potential as effective markers for combined PD-1 blockade and chemotherapy.

SUBMITTER: Kim CG

PROVIDER: S-EPMC8753071 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Predicting treatment outcomes using <sup>18</sup>F-FDG PET biomarkers in patients with non-small-cell lung cancer receiving chemoimmunotherapy.

Kim Chang Gon CG Hwang Sang Hyun SH Kim Kyung Hwan KH Yoon Hong In HI Shim Hyo Sup HS Lee Ji Hyun JH Han Yejeong Y Ahn Beung-Chul BC Hong Min Hee MH Kim Hye Ryun HR Cho Byoung Chul BC Cho Arthur A Lim Sun Min SM

Therapeutic advances in medical oncology 20220109

<h4>Background</h4>Predictive markers for treatment response and survival outcome have not been identified in patients with advanced non-small-cell lung cancer (NSCLC) receiving chemoimmunotherapy. We aimed to evaluate whether imaging biomarkers of <sup>18</sup>F-fluorodeoxyglucose (<sup>18</sup>F-FDG) positron emission tomography/computed tomography (PET/CT) and routinely assessed clinico-laboratory values were associated with clinical outcomes in patients with advanced NSCLC receiving pembroli ...[more]

PMID: 35035536

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Project description:BackgroundIn recent years, immune checkpoint inhibitor (ICI) therapy has greatly changed the treatment prospects of patients with non-small cell lung cancer (NSCLC). Among the available ICI therapy strategies, programmed death-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors are the most widely used worldwide. At present, immunohistochemistry (IHC) is the main method to detect PD-L1 expression levels in clinical practice. However, given that IHC is invasive and cannot reflect the expression of PD-L1 dynamically and in real time, it is of great clinical significance to develop a new noninvasive, accurate radiomics method to evaluate PD-L1 expression levels and predict and filter patients who will benefit from immunotherapy. Therefore, the aim of our study was to assess the predictive power of pretherapy [18F]-fluorodeoxyglucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT)-based radiomics features for PD-L1 expression status in patients with NSCLC.MethodsA total of 334 patients with NSCLC who underwent [18F]FDG PET/CT imaging prior to treatment were analyzed retrospectively from September 2016 to July 2021. The LIFEx7.0.0 package was applied to extract 63 PET and 61 CT radiomics features. In the training group, the least absolute shrinkage and selection operator (LASSO) regression model was employed to select the most predictive radiomics features. We constructed and validated a radiomics model, clinical model and combined model. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used to evaluate the predictive performance of the three models in the training group and validation group. In addition, a radiomics nomogram to predict PD-L1 expression status was established based on the optimal predictive model.ResultsPatients were randomly assigned to a training group (n = 233) and a validation group (n = 101). Two radiomics features were selected to construct the radiomics signature model. Multivariate analysis showed that the clinical stage (odds ratio [OR] 1.579, 95% confidence interval [CI] 0.220-0.703, P < 0.001) was a significant predictor of different PD-L1 expression statuses. The AUC of the radiomics model was higher than that of the clinical model in the training group (0.706 vs. 0.638) and the validation group (0.761 vs. 0.640). The AUCs in the training group and validation group of the combined model were 0.718 and 0.769, respectively.ConclusionPET/CT-based radiomics features demonstrated strong potential in predicting PD-L1 expression status and thus could be used to preselect patients who may benefit from PD-1/PD-L1-based immunotherapy.

Project description:BackgroundIdentification of epidermal growth factor receptor (EGFR) mutation types is crucial before tyrosine kinase inhibitors (TKIs) treatment. Radiomics is a new strategy to noninvasively predict the genetic status of cancer. In this study, we aimed to develop a predictive model based on 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) radiomic features to identify the specific EGFR mutation subtypes.MethodsWe retrospectively studied 18F-FDG PET/CT images of 148 patients with isolated lung lesions, which were scanned in two hospitals with different CT scan setting (slice thickness: 3 and 5 mm, respectively). The tumor regions were manually segmented on PET/CT images, and 1,570 radiomic features (1,470 from CT and 100 from PET) were extracted from the tumor regions. Seven hundred and ninety-four radiomic features insensitive to different CT settings were first selected using the Mann white U test, and collinear features were further removed from them by recursively calculating the variation inflation factor. Then, multiple supervised machine learning models were applied to identify prognostic radiomic features through: (I) a multi-variate random forest to select features of high importance in discriminating different EGFR mutation status; (II) a logistic regression model to select features of the highest predictive value of the EGFR subtypes. The EGFR mutation predicting model was constructed from prognostic radiomic features using the popular Xgboost machine-learning algorithm and validated using 3-fold cross-validation. The performance of predicting model was analyzed using the receiver operating characteristic curve (ROC) and measured with the area under the curve (AUC).ResultsTwo sets of prognostic radiomic features were found for specific EGFR mutation subtypes: 5 radiomic features for EGFR exon 19 deletions, and 5 radiomic features for EGFR exon 21 L858R missense. The corresponding radiomic predictors achieved the prediction accuracies of 0.77 and 0.92 in terms of AUC, respectively. Combing these two predictors, the overall model for predicting EGFR mutation positivity was also constructed, and the AUC was 0.87.ConclusionsIn our study, we established predictive models based on radiomic analysis of 18F-FDG PET/CT images. And it achieved a satisfying prediction power in the identification of EGFR mutation status as well as the certain EGFR mutation subtypes in lung cancer.

Project description:BackgroundA considerable proportion of metastatic melanoma (mM) patients do not respond to immune checkpoint inhibitors (ICIs). There is a great need to develop noninvasive biomarkers to detect patients, who do not respond to ICIs early during the course of treatment. The aim of this study was to evaluate the role of early [18F]2fluoro-2-deoxy-D-glucose PET/CT (18F-FDG PET/CT) at week four (W4) and other possible prognostic biomarkers of survival in mM patients receiving ICIs.Patients and methods. In this prospective noninterventional clinical study, mM patients receiving ICIs regularly underwent 18F-FDG PET/CT: at baseline, at W4 after ICI initiation, at week sixteen and every 16 weeks thereafter. The tumor response to ICIs at W4 was assessed via modified European Organisation for Research and Treatment of Cancer (EORTC) criteria. Patients with progressive metabolic disease (PMD) were classified into the no clinical benefit group (no-CB), and those with other response types were classified into the clinical benefit group (CB). The primary end point was survival analysis on the basis of the W4 18F-FDG PET/CT response. The secondary endpoints were survival analysis on the basis of LDH, the number of metastatic localizations, and immune-related adverse events (irAEs). Kaplan-Meier analysis and univariate Cox regression analysis were used to assess the impact on survival.ResultsOverall, 71 patients were included. The median follow-up was 37.1 months (952% CI = 30.1-38.0). Three (4%) patients had only baseline scans due to rapid disease progression and death prior to W4 18F-FDG-PET/CT. Fifty-one (72%) patients were classified into the CB group, and 17 (24%) were classified into the no-CB group. There was a statistically significant difference in median overall survival (OS) between the CB group (median OS not reached [NR]; 95% CI = 17.8 months - NR) and the no-CB group (median OS 6.2 months; 95% CI = 4.6 months - NR; p = 0.003). Univariate Cox analysis showed HR of 0.4 (95% CI = 0.18 - 0.72; p = 0.004). median OS was also significantly longer in the group with normal serum LDH levels and the group with irAEs and cutaneous irAEs.ConclusionsEvaluation of mM patients with early 18F-FDG-PET/CT at W4, who were treated with ICIs, could serve as prognostic imaging biomarkers. Other recognized prognostic biomarkers were the serum LDH level and occurrence of cutaneous irAEs.

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Dataset Information

Predicting treatment outcomes using ¹⁸F-FDG PET biomarkers in patients with non-small-cell lung cancer receiving chemoimmunotherapy.

Background

Methods

Results

Conclusion

Publications

Predicting treatment outcomes using <sup>18</sup>F-FDG PET biomarkers in patients with non-small-cell lung cancer receiving chemoimmunotherapy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Predicting treatment outcomes using 18F-FDG PET biomarkers in patients with non-small-cell lung cancer receiving chemoimmunotherapy.

Background

Methods

Results

Conclusion

Publications

Predicting treatment outcomes using <sup>18</sup>F-FDG PET biomarkers in patients with non-small-cell lung cancer receiving chemoimmunotherapy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Predicting treatment outcomes using ¹⁸F-FDG PET biomarkers in patients with non-small-cell lung cancer receiving chemoimmunotherapy.