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¹⁸F-FDG-PET/CT in the assessment of atherosclerosis in lung cancer.

ABSTRACT: The aim of this study was to assess the risk of atherosclerosis in patients with lung cancer compared to patients with extrapulmonary malignancies using ¹⁸F-fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT). We hypothesized that patients with lung cancer would demonstrate increased FDG uptake in the thoracic aorta compared to patients with extrapulmonary cancers. Thirty-four lung cancer patients (21 male, 13 female, 64.1 ± 12.9 yo) were retrospectively compared to seventy-eight patients with extrapulmonary malignancies (46 male, 32 female, 59.6 ± 12.8 yo). Average maximum standardized uptake value (avgSUVmax) and maximum target-to-blood pool ratio (TBRmax) were measured by mapping regions of interest of the ascending aorta, aortic arch, and descending aorta. Two-tailed Student's t-test was used to assess the differences in avgSUVmax and TBRmax between the two groups and between smokers and non-smokers. Age and gender distribution between the groups were not statistically different. AvgSUVmax and TBRmax were statistically significant increase in lung cancer patients compared to extrapulmonary cancer patients in the ascending aorta, aortic arch, and descending aorta, suggesting a lung cancer-associated increased risk of atherosclerosis development. AvgSUVmax was not significantly different between smokers and non-smokers in all sections of the thoracic aorta. Moving forward, large, prospective studies that directly compare PET data between different malignancies of different stages will help determine the role of FDG-PET/CT in assessing paraneoplastic vascular disease.

SUBMITTER: Koa B

PROVIDER: S-EPMC7936247 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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<sup>18</sup>F-FDG-PET/CT in the assessment of atherosclerosis in lung cancer.

Koa Benjamin B Borja Austin J AJ Yellanki Dani D Rojulpote Chaitanya C Tran Joseph J Zhang Vincent V Werner Thomas J TJ Alavi Abass A Revheim Mona-Elisabeth ME

American journal of nuclear medicine and molecular imaging 20210215 1

The aim of this study was to assess the risk of atherosclerosis in patients with lung cancer compared to patients with extrapulmonary malignancies using <sup>18</sup>F-fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT). We hypothesized that patients with lung cancer would demonstrate increased FDG uptake in the thoracic aorta compared to patients with extrapulmonary cancers. Thirty-four lung cancer patients (21 male, 13 female, 64.1 ± 12.9 yo) were retrospectively c ...[more]

PMID: 33688450

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Project description:IntroductionCompartmental modelling is an established method of quantifying 18F-FDG uptake; however, only recently has it been applied to evaluate pulmonary inflammation. Implementation of compartmental models remains challenging in the lung, partly due to the low signal-to-noise ratio compared to other organs and the lack of standardisation. Good reproducibility is a key requirement of an imaging biomarker which has yet to be demonstrated in pulmonary compartmental models of 18F-FDG; in this paper, we address this unmet need.MethodsRetrospective subject data were obtained from the EVOLVE observational study: Ten COPD patients (age =66±9; 8M/2F), 10 α1ATD patients (age =63±8; 7M/3F) and 10 healthy volunteers (age =68±8; 9M/1F) never smokers. PET and CT images were co-registered, and whole lung regions were extracted from CT using an automated algorithm; the descending aorta was defined using a manually drawn region. Subsequent stages of the compartmental analysis were performed by two independent operators using (i) a MIAKATTM based pipeline and (ii) an in-house developed pipeline. We evaluated the metabolic rate constant of 18F-FDG (Kim) and the fractional blood volume (Vb); Bland-Altman plots were used to compare the results. Further, we adjusted the in-house pipeline to identify the salient features in the analysis which may help improve the standardisation of this technique in the lung.ResultsThe initial agreement on a subject level was poor: Bland-Altman coefficients of reproducibility for Kim and Vb were 0.0031 and 0.047 respectively. However, the effect size between the groups (i.e. COPD, α1ATD and healthy subjects) was similar using either pipeline. We identified the key drivers of this difference using an incremental approach: ROI methodology, modelling of the IDIF and time delay estimation. Adjustment of these factors led to improved Bland-Altman coefficients of reproducibility of 0.0015 and 0.027 for Kim and Vb respectively.ConclusionsDespite similar methodology, differences in implementation can lead to disparate results in the outcome parameters. When reporting the outcomes of lung compartmental modelling, we recommend the inclusion of the details of ROI methodology, input function fitting and time delay estimation to improve reproducibility.

Project description:BackgroundAn inflammatory reaction in the airways and lung parenchyma, comprised mainly of neutrophils and alveolar macrophages, is present in some patients with chronic obstructive pulmonary disease (COPD). Thoracic fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) has been proposed as a promising imaging biomarker to assess this inflammation. We sought to introduce a fully quantitative analysis method and compare this with previously published studies based on the Patlak approach using a dataset comprising 18F-FDG PET scans from COPD subjects with elevated circulating inflammatory markers (fibrinogen) and matched healthy volunteers (HV). Dynamic 18F-FDG PET scans were obtained for high-fibrinogen (>2.8 g/l) COPD subjects (N = 10) and never smoking HV (N = 10). Lungs were segmented using co-registered computed tomography images and subregions (upper, middle and lower) were semi-automatically defined. A quantitative analysis approach was developed, which corrects for the presence of air and blood in the lung (qABL method), enabling direct estimation of the metabolic rate of FDG in lung tissue. A normalised Patlak analysis approach was also performed to enable comparison with previously published results. Effect sizes (Hedge's g) were used to compare HV and COPD groups.ResultsThe qABL method detected no difference (Hedge's g = 0.15 [-0.76 1.04]) in the tissue metabolic rate of FDG in the whole lung between HV (μ = 6.0 ± 1.9 × 10-3 ml cm-3 min-1) and COPD (μ = 5.7 ± 1.7 × 10-3 ml cm-3 min-1). However, analysis with the normalised Patlak approach detected a significant difference (Hedge's g = -1.59 [-2.57 -0.48]) in whole lung between HV (μ = 2.9 ± 0.5 × 10-3 ml cm-3 min-1) and COPD (μ = 3.9 ± 0.7 × 10-3 ml cm-3 min-1). The normalised Patlak endpoint was shown to be a composite measure influenced by air volume, blood volume and actual uptake of 18F-FDG in lung tissue.ConclusionsWe have introduced a quantitative analysis method that provides a direct estimate of the metabolic rate of FDG in lung tissue. This work provides further understanding of the underlying origin of the 18F-FDG signal in the lung in disease groups and helps interpreting changes following standard or novel therapies.

Project description:Knowledge of the within-subject variability of 18F-FDG PET/MRI measurements is necessary for proper interpretation of quantitative PET or MRI metrics in the context of therapeutic efficacy assessments with integrated PET/MRI scanners. The goal of this study was to determine the test-retest repeatability of these metrics on PET/MRI, with comparison to similar metrics acquired by PET/CT. Methods: This prospective study enrolled subjects with pathology-proven pelvic malignancies. Baseline imaging consisted of PET/CT immediately followed by PET/MRI, using a single 370-MBq 18F-FDG dose. Repeat imaging was performed within 7 d using an identical imaging protocol, with no oncologic therapy between sessions. PET imaging on both scanners consisted of a list-mode acquisition at a single pelvic station. The MRI consisted of 2-point Dixon imaging for attenuation correction, standard sequences for anatomic correlation, and diffusion-weighted imaging. PET data were statically reconstructed using various frame durations and minimizing uptake time differences between sessions. SUV metrics were extracted for both PET/CT and PET/MRI in each imaging session. Apparent diffusion coefficient (ADC) metrics were extracted for both PET/MRI sessions. Results: The study cohort consisted of 14 subjects (13 female, 1 male) with various pelvic cancers (11 cervical, 2 rectal, 1 endometrial). For SUVmax, the within-subject coefficient of variation (wCV) appeared higher for PET/CT (8.5%-12.8%) than PET/MRI (6.6%-8.7%) across all PET reconstructions, though with no significant repeatability differences (all P values ≥ 0.08) between modalities. For lean body mass-adjusted SUVpeak, the wCVs appeared similar for PET/CT (9.9%-11.5%) and PET/MRI (9.2%-11.3%) across all PET reconstructions, again with no significant repeatability differences (all P values ≥ 0.14) between modalities. For PET/MRI, the wCV for ADCmedian of 3.5% appeared lower than the wCVs for SUVmax (6.6%-8.7%) and SULpeak (9.2%-11.3%), though without significant repeatability differences (all P values ≥ 0.23). Conclusion: For solid tumors of the pelvis, the repeatability of the evaluated SUV and ADC metrics on 18F-FDG PET/MRI is both acceptably high and similar to previously published values for 18F-FDG PET/CT and MRI, supporting the use of 18F-FDG PET/MRI for quantitative oncologic treatment response assessments.

Project description:Diagnosing relapse after radiotherapy for lung cancer is challenging. The specificity of both CT and 18F-FDG PET/CT is low because of radiation-induced changes. 3'-deoxy-3'-18F-fluorothymidine (18F-FLT) PET has previously demonstrated higher specificity for malignancy than 18F-FDG PET. We investigated the value of 18F-FLT PET/CT for diagnosing relapse in irradiated lung cancer. Methods: Patients suspected of relapse of lung cancer after definitive radiotherapy (conventional fractionated radiotherapy [cRT] or stereotactic body radiotherapy [SBRT]) were included. Sensitivity and specificity were analyzed both within the irradiated high-dose volume (HDV) and on a patient basis. Marginal differences and interobserver agreement were assessed. Results: Sixty-three patients who had received radiotherapy in 70 HDVs (34 cRT; 36 SBRT) were included. The specificity of 18F-FLT PET/CT was higher than that of 18F-FDG PET/CT (HDV, 96% [95% CI, 87-100] vs. 71% [95% CI, 57-83] [P = 0.0039]; patient-based, 90% [95% CI, 73-98] vs. 55% [95% CI, 36-74] [P = 0.0020]). The difference in specificity between 18F-FLT PET/CT and 18F-FDG PET/CT was higher after cRT than after SBRT. The sensitivity of 18F-FLT PET/CT was lower than that of 18F-FDG PET/CT (HDV, 69% [95% CI, 41-89] vs. 94% [95% CI, 70-100] [P = 0.1250]; patient-based, 70% [95% CI, 51-84] vs. 94% [95% CI, 80-99] [P = 0.0078]). Adding 18F-FLT PET/CT when 18F-FDG PET/CT was positive or inconclusive improved the diagnostic value compared with 18F-FDG PET/CT alone. In cRT HDVs, the probability of malignancy increased from 67% for 18F-FDG PET/CT alone to 100% when both tracers were positive. Conclusion: 18F-FLT PET/CT adds diagnostic value to 18F-FDG PET/CT in patients with suspected relapse. The diagnostic impact of 18F-FLT PET/CT was highest after cRT. We suggest adding 18F-FLT PET/CT when 18F-FDG PET/CT is inconclusive or positive within the previously irradiated volume to improve diagnostic value in patients for whom histologic confirmation is not easily obtained.

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.

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¹⁸F-FDG-PET/CT in the assessment of atherosclerosis in lung cancer.

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<sup>18</sup>F-FDG-PET/CT in the assessment of atherosclerosis in lung cancer.

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18F-FDG-PET/CT in the assessment of atherosclerosis in lung cancer.

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<sup>18</sup>F-FDG-PET/CT in the assessment of atherosclerosis in lung cancer.

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OmicsDI is part of the ELIXIR infrastructure

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¹⁸F-FDG-PET/CT in the assessment of atherosclerosis in lung cancer.