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Purpose: To test if the relative change in FDG-PET SUV max over the course of treatment was associated with disease progression and overall survival. Additionally, the prognostic values of other first-order PET-metric changes were investigated., Methods: The study included 38 patients with stage II-III NSCLC, who underwent concurrent chemoradiotherapy. Patients received two pre-treatment FDG-PET scans and four during-treatment scans at weekly intervals. SUV max was normalized to the start of treatment and analyzed using linear regression. Linear regression coefficients of other first order PET-metrics were grouped according to dissimilarity. Associations to patient outcome were analyzed using Cox hazard ratio., Results: Twenty-eight patients satisfied the criteria for analysis. All PET-metrics demonstrated a strong linear correlation with time during treatment [median R-range: -0.87: -0.97]. No strong associations (p > 0.10) were found for the relative slope of SUV max to patient outcomes. Other first-order metrics did correlate with outcome but the single imaging time-point maximizing the association of PET response with outcome varied per PET metric and outcome parameter., Conclusion: All investigated FDG PET metrics linearly decreased during treatment. Relative change in SUV max was not associated to patient outcome while several other first order PET-metrics were related to patient outcome. A single optimal imaging time-point could not be identified., (Copyright © 2021 Elsevier B.V. All rights reserved.)

In locally advanced lung cancer, established baseline clinical variables show limited prognostic accuracy and 18F-fluorodeoxyglucose positron emission tomography (FDG PET) radiomics features may increase accuracy for optimal treatment selection. Their robustness and added value relative to current clinical factors are unknown. Hence, we identify robust and independent PET radiomics features that may have complementary value in predicting survival endpoints. A 4D PET dataset (n = 70) was used for assessing the repeatability (Bland-Altman analysis) and independence of PET radiomics features (Spearman rank: |ρ|<0.5). Two 3D PET datasets combined (n = 252) were used for training and validation of an elastic net regularized generalized logistic regression model (GLM) based on a selection of clinical and robust independent PET radiomics features (GLMall). The fitted model performance was externally validated (n = 40). The performance of GLMall (measured with area under the receiver operating characteristic curve, AUC) was highest in predicting 2-year overall survival (0.66±0.07). No significant improvement was observed for GLMall compared to a model containing only PET radiomics features or only clinical variables for any clinical endpoint. External validation of GLMall led to AUC values no higher than 0.55 for any clinical endpoint. In this study, robust independent FDG PET radiomics features did not have complementary value in predicting survival endpoints in lung cancer patients. Improving risk stratification and clinical decision making based on clinical variables and PET radiomics features has still been proven difficult in locally advanced lung cancer patients., Competing Interests: The authors have declared that no competing interests exist.

Background: Most solid tumors contain inadequately oxygenated (i.e., hypoxic) regions, which tend to be more aggressive and treatment resistant. Hypoxia PET allows visualization of hypoxia and may enable treatment adaptation. However, hypoxia PET imaging is expensive, time-consuming and not widely available. We aimed to predict hypoxia levels in non-small cell lung cancer (NSCLC) using more easily available imaging modalities: FDG-PET/CT and dynamic contrast-enhanced CT (DCE-CT)., Material and Methods: For 34 NSCLC patients, included in two clinical trials, hypoxia HX4-PET/CT, planning FDG-PET/CT and DCE-CT scans were acquired before radiotherapy. Scans were non-rigidly registered to the planning CT. Tumor blood flow (BF) and blood volume (BV) were calculated by kinetic analysis of DCE-CT images. Within the gross tumor volume, independent clusters, i.e., supervoxels, were created based on FDG-PET/CT. For each supervoxel, tumor-to-background ratios (TBR) were calculated (median SUV/aorta SUV mean ) for HX4-PET/CT and supervoxel features (median, SD, entropy) for the other modalities. Two random forest models (cross-validated: 10 folds, five repeats) were trained to predict the hypoxia TBR; one based on CT, FDG, BF and BV, and one with only CT and FDG features. Patients were split in a training (trial NCT01024829) and independent test set (trial NCT01210378). For each patient, predicted, and observed hypoxic volumes (HV) (TBR > 1.2) were compared., Results: Fifteen patients (3291 supervoxels) were used for training and 19 patients (1502 supervoxels) for testing. The model with all features (RMSE training: 0.19 ± 0.01, test: 0.27) outperformed the model with only CT and FDG-PET features (RMSE training: 0.20 ± 0.01, test: 0.29). All tumors of the test set were correctly classified as normoxic or hypoxic (HV > 1 cm 3 ) by the best performing model., Conclusions: We created a data-driven methodology to predict hypoxia levels and hypoxia spatial patterns using CT, FDG-PET and DCE-CT features in NSCLC. The model correctly classifies all tumors, and could therefore, aid tumor hypoxia classification and patient stratification.