Dataset Information

Assessment of pulmonary 18F-FDG-PET uptake and cytokine profiles in non-small cell lung cancer patients treated with radiotherapy and erlotinib.

ABSTRACT: Purpose:To investigate effects of radiotherapy (RT) and erlotinib on pulmonary glucose uptake using 2-deoxy-2-(18F)fluoro-D-glucose (18F-FDG) positron emission tomography (PET) during and after treatment of non-small cell lung cancer (NSCLC) and to identify associations between serum cytokine levels and lung glucose uptake. Material and methods:Twenty-seven patients with advanced NSCLC, receiving RT alone or concomitant RT and erlotinib therapy, were examined by 18F-FDG PET before, during, and after treatment. A total of 57 18F-FDG PET scans were analyzed. Pulmonary 18F-FDG uptake and radiotherapy dose mapping were used to acquire dose-response curves for each patient, where subsequent linear regression gave a glucose uptake level in the un-irradiated parts of the lungs (SUV0) and a response slope (ΔSUV). Serum cytokine levels at corresponding time points were assessed using a multiplex bioassay. Correlations between the most robust cytokines and lung 18F-FDG dose response parameters were further investigated. Results:From the dose response analysis, SUV0 at post-therapy was significantly higher (P < 0.001) than at mid- and pre-therapy (45% and 58%, respectively) for the group receiving RT + erlotinib. Also, SUV0 at post-therapy was higher for patients receiving RT + erlotinib compared to RT alone (42%; P < 0.001). No differences in ΔSUV were seen with treatments or time. SUV0 was positively associated (r = 0.47, P = 0.01) with serum levels of the chemokine C-C motif ligand 21 (CCL21) for patients receiving RT + erlotinib. Conclusions:Concomitant RT and erlotinib causes an elevation in pulmonary 18F-FDG uptake post treatment compared to RT alone. Pulmonary glucose uptake is associated with an upregulation of a chemokine (CCL21) involved in inflammatory reactions.

SUBMITTER: Abravan A

PROVIDER: S-EPMC5833916 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundAssessment of dual time point (DTP) positron emission tomography was carried out with the aim of a quantitative determination of Km, the metabolic uptake rate of [18F]fluorodeoxyglucose as a measure of glucose consumption.MethodsStarting from the Patlak equation, it is shown that Km?mt/ca0+V?r/?a, where mt is the secant slope of the tissue response function between the dual time point measurements centered at t?=?t0. ca0=ca(t0) denotes arterial tracer concentration, V?r is an estimate of the Patlak intercept, and ?a is the time constant of the ca(t) decrease. We compared the theoretical predictions with the observed relation between Ks=mt/ca0 and Km in a group of nine patients with liver metastases of colorectal cancer for which dynamic scans were available, and Km was derived from conventional Patlak analysis. Twenty-two lesion regions of interest (ROIs) were evaluated. ca(t) was determined from a three-dimensional ROI in the aorta. Furthermore, the correlation between Km and late standard uptake value (SUV) as well as retention index was investigated. Additionally, feasibility of the approach was demonstrated in a whole-body investigation.ResultsPatlak analysis yielded a mean Vr of V?r=0.53±0.08 ml/ml. The patient averaged ?a was 99?±?23 min. Linear regression between Patlak-derived Km and DTP-derived Ks according to Ks?=?b?·?Km?+?a yielded b?=?0.98?±?0.05 and a?=?-0.0054?±?0.0013 ml/min/ml (r?=?0.98) in full accordance with the theoretical predictions b?=?1 and a?-V?r/?a. Ks exhibits better correlation with Km than late SUV and retention index, respectively. Ks(c)=Ks+V?r/?a is proposed as a quantitative estimator of Km which is independent of patient weight, scan time, and scanner calibration.ConclusionQuantification of Km from dual time point measurements compatible with clinical routine is feasible. The proposed approach eliminates the issues of static SUV and conventional DTP imaging regarding influence of chosen scanning times and inter-study variability of the input function. Ks and Ks(c) exhibit improved stability and better correlation with the true Km. These properties might prove especially relevant in the context of radiation treatment planning and therapy response control.

Project description:BACKGROUND:To identify whether early metabolic responses as determined using 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) during radiotherapy (RT) predict outcomes in patients with esophageal cancer. METHODS:Twenty-one patients with esophageal cancer who received pre-treatment 18F-FDG PET/CT (PET1) and inter-fractional 18F-FDG PET/CT (PET2) after 11 fractions of RT (median 23.1?Gy, 2.1?Gy per fraction) were retrospectively reviewed. The region of interest for each calculation was delineated using "PET Edge". We calculated PET parameters including maximum and mean standardized uptake values (SUVmax and SUVmean, respectively), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). The relative changes (%) were calculated using the logarithmically transformed parameter values for the PET1 and PET2 scans. Multivariate analysis of locoregional recurrence and distant failures were performed using Cox regression analysis. After identifying statistically significant PET parameters for discriminating responders from non-responders, receiver operating characteristics curve analyses were used to assess the potentials of the studied PET parameters. RESULTS:After a median follow-up of 13?months, the 1-year overall and progression-free survival rates were 79.0% and 34.4%, respectively. Four patients developed locoregional recurrences (LRRs) and 8 had distant metastases (DMs). The 1-year overall LRR-free rate was 76.9% while the DM-free rate was 60.6%. The relative changes in MTV (?MTV) were significantly associated with LRR (p =?0.03). Conversely, the relative changes in SUVmean (?SUVmean) were associated with the risk of DM (p =?0.02). An ?MTV threshold of 1.14 yielded a sensitivity of 60%, specificity of 94%, and an accuracy of 86% for predicting an LRR. Additionally, a ?SUVmean threshold of a 35% decrease yielded a sensitivity of 67%, specificity of 83%, and accuracy of 76% for the prediction DM. TRIAL REGISTRATION:Retrospectively registered. CONCLUSIONS:Changes in tumor metabolism during RT could be used to predict treatment responses, recurrences, and prognoses in patients with esophageal cancer.

Project description:We introduce a novel computational framework to enable automated identification of texture and shape features of lesions on (18)F-FDG-PET images through a graph-based image segmentation method. The proposed framework predicts future morphological changes of lesions with high accuracy. The presented methodology has several benefits over conventional qualitative and semi-quantitative methods, due to its fully quantitative nature and high accuracy in each step of (i) detection, (ii) segmentation, and (iii) feature extraction. To evaluate our proposed computational framework, thirty patients received 2 (18)F-FDG-PET scans (60 scans total), at two different time points. Metastatic papillary renal cell carcinoma, cerebellar hemongioblastoma, non-small cell lung cancer, neurofibroma, lymphomatoid granulomatosis, lung neoplasm, neuroendocrine tumor, soft tissue thoracic mass, nonnecrotizing granulomatous inflammation, renal cell carcinoma with papillary and cystic features, diffuse large B-cell lymphoma, metastatic alveolar soft part sarcoma, and small cell lung cancer were included in this analysis. The radiotracer accumulation in patients' scans was automatically detected and segmented by the proposed segmentation algorithm. Delineated regions were used to extract shape and textural features, with the proposed adaptive feature extraction framework, as well as standardized uptake values (SUV) of uptake regions, to conduct a broad quantitative analysis. Evaluation of segmentation results indicates that our proposed segmentation algorithm has a mean dice similarity coefficient of 85.75 ± 1.75%. We found that 28 of 68 extracted imaging features were correlated well with SUV(max) (p<0.05), and some of the textural features (such as entropy and maximum probability) were superior in predicting morphological changes of radiotracer uptake regions longitudinally, compared to single intensity feature such as SUV(max). We also found that integrating textural features with SUV measurements significantly improves the prediction accuracy of morphological changes (Spearman correlation coefficient = 0.8715, p<2e-16).

Dataset Information

Assessment of pulmonary 18F-FDG-PET uptake and cytokine profiles in non-small cell lung cancer patients treated with radiotherapy and erlotinib.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure