Dataset Information

Evaluation of quantitative modeling methods in whole-body, dynamic [¹¹C]-erlotinib PET.

ABSTRACT:

Background

[¹¹C]-Erlotinib is a radiolabeled analogue of a tyrosine kinase inhibitor used to treat non-small cell lung cancer (NSCLC) which expresses specific kinase domain mutations of the epidermal growth factor receptor (EGFR). In this study, 10 subjects with NSCLC and assorted EGFR mutation status underwent a dynamic, multi-bed positron emission tomography (PET) scan using [¹¹C]-erlotinib. Data were analyzed using a variety of quantitative techniques common in PET (graphical methods, kinetic models, and uptake value-based endpoints). Our primary goal was to determine the most reliable imaging endpoint given the need for maintaining minimal patient burden and recognizing the advantage of simple calculations in future trials.

Results

Standard uptake values (a semi-quantitative endpoint) were well correlated with both binding potential and volume of distribution (fully quantitative endpoints). Normalized tracer uptake was found to stabilize approximately 60 minutes post tracer injection. Conclusions: The kinetic properties of [¹¹C]-erlotinib varied greatly across subjects. Our novel scanning protocol produced an important dataset which highlights the great heterogeneity of NSCLC and its apparent impact on [¹¹C]-erlotinib kinetics. A lack of correlation between EGFR mutational status and quantitative endpoints appears to be due to disease heterogeneity and low tracer uptake. The most reliable fits of the dynamic data were based on the one-tissue compartmental model which were well correlated with mean SUV. Due to this correlation and good stability at late-time, SUV seems sufficiently well-suited to quantitative imaging of NSCLC lesions in the whole body with [¹¹C]-erlotinib.

SUBMITTER: Petrulli JR

PROVIDER: S-EPMC8165727 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundDynamic course of flourine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) Patlak muti-parametric imaging spatial distribution in the targeted tissues may reveal highly useful clinical information about the tissue's metabolic properties. The characteristics of the Patlak multi-parametric imaging in lung cancer and the influence of different delineation methods on quantitative parameters may provide reference for the clinical application of this new technology.MethodsA total of 27 patients with pathologically diagnosed lung cancer underwent whole-body dynamic 18F-FDG PET/CT examination before treatment. Parametric images of metabolic rate of FDG (MRFDG) and Patlak intercept (or distribution volume; DV) were generated using Patlak reconstruction. The values of primary lung cancer lesions, target-to-background ratio (TBR), and contrast-to-noise ratio (CNR) were investigated using contour delineation and boundary delineation. Statistical analysis was performed to analyze the relationship between multi-parametric images and clinicopathological features, and to compare the effects of contour delineation and boundary delineation on quantitative parameters.ResultsMRFDG images showed higher TBR and CNR than did standardized uptake value (SUV) images. There were significant differences in MRFDG-max, MRFDG-mean, and MRFDG-peak among groups with different tumor diameters and pathology types (P<0.05). Moreover, the metabolic parameters of MRFDG were higher in patients with tumor diameters ≥3 cm and squamous carcinoma. The differences of the maximum and peak values of MRFDG and DV were not statistically significant in the different outlining method subgroups (all P>0.05). However, the difference of the mean values of MRFDG and DV were statistically significant in the different outline method groupings (all P<0.05).ConclusionsDynamic 18F-FDG PET/CT Patlak multi-parametric imaging can obtain quantitative values for lung cancer with high TBR and CNR. Moreover, the multi-parameters are various from different pathology types to tumor size. Different delineation methods have a greater influence on the mean value of quantitative parameters.

Project description:PurposeIn PSMA-ligand PET/CT imaging, standardized evaluation frameworks and image-derived parameters are increasingly used to support prostate cancer staging. Clinical applicability remains challenging wherever manual measurements of numerous suspected lesions are required. Deep learning methods are promising for automated image analysis, typically requiring extensive expert-annotated image datasets to reach sufficient accuracy. We developed a deep learning method to support image-based staging, investigating the use of training information from two radiotracers.MethodsIn 173 subjects imaged with 68Ga-PSMA-11 PET/CT, divided into development (121) and test (52) sets, we trained and evaluated a convolutional neural network to both classify sites of elevated tracer uptake as nonsuspicious or suspicious for cancer and assign them an anatomical location. We evaluated training strategies to leverage information from a larger dataset of 18F-FDG PET/CT images and expert annotations, including transfer learning and combined training encoding the tracer type as input to the network. We assessed the agreement between the N and M stage assigned based on the network annotations and expert annotations, according to the PROMISE miTNM framework.ResultsIn the development set, including 18F-FDG training data improved classification performance in four-fold cross validation. In the test set, compared to expert assessment, training with 18F-FDG data and the development set yielded 80.4% average precision [confidence interval (CI): 71.1-87.8] for identification of suspicious uptake sites, 77% (CI: 70.0-83.4) accuracy for anatomical location classification of suspicious findings, 81% agreement for identification of regional lymph node involvement, and 77% agreement for identification of metastatic stage.ConclusionThe evaluated algorithm showed good agreement with expert assessment for identification and anatomical location classification of suspicious uptake sites in whole-body 68Ga-PSMA-11 PET/CT. With restricted PSMA-ligand data available, the use of training examples from a different radiotracer improved performance. The investigated methods are promising for enabling efficient assessment of cancer stage and tumor burden.

Project description:BackgroundArterial blood sampling is the gold standard method to obtain the arterial input function (AIF) for quantification of whole body (WB) dynamic 18F-FDG PET imaging. However, this procedure is invasive and not typically available in clinical environments. As an alternative, we compared AIFs to population-based input functions (PBIFs) using two normalization methods: area under the curve (AUC) and extrapolated initial plasma concentration (CP*(0)). To scale the PBIFs, we tested two methods: (1) the AUC of the image-derived input function (IDIF) and (2) the estimated CP*(0). The aim of this study was to validate IDIF and PBIF for FDG oncological WB PET studies by comparing to the gold standard arterial blood sampling.MethodsThe Feng 18F-FDG plasma concentration model was applied to estimate AIF parameters (n = 23). AIF normalization used either AUC(0-60?min) or CP*(0), estimated from an exponential fit. CP*(0) is also described as the ratio of the injected dose (ID) to initial distribution volume (iDV). iDV was modeled using the subject height and weight, with coefficients that were estimated in 23 subjects. In 12 oncological patients, we computed IDIF (from the aorta) and PBIFs with scaling by the AUC of the IDIF from 4 time windows (15-45, 30-60, 45-75, 60-90?min) (PBIFAUC) and estimated CP*(0) (PBIFiDV). The IDIF and PBIFs were compared with the gold standard AIF, using AUC values and Patlak Ki values.ResultsThe IDIF underestimated the AIF at early times and overestimated it at later times. Thus, based on the AUC and Ki comparison, 30-60?min was the most accurate time window for PBIFAUC; later time windows for scaling underestimated Ki (-?6 ± 8 to -?13 ± 9%). Correlations of AUC between AIF and IDIF, PBIFAUC(30-60), and PBIFiDV were 0.91, 0.94, and 0.90, respectively. The bias of Ki was -?9 ± 10%, -?1 ± 8%, and 3 ± 9%, respectively.ConclusionsBoth PBIF scaling methods provided good mean performance with moderate variation. Improved performance can be obtained by refining IDIF methods and by evaluating PBIFs with test-retest data.

Project description:BackgroundCorrect classification of estrogen receptor (ER) status is essential for prognosis and treatment planning in patients with breast cancer (BC). Therefore, it is recommended to sample tumor tissue from an accessible metastasis. However, ER expression can show intra- and intertumoral heterogeneity. 16α-[18F]fluoroestradiol ([18F]FES) Positron Emission Tomography/Computed Tomography (PET/CT) allows noninvasive whole-body (WB) identification of ER distribution and is usually performed as a single static image 60 min after radiotracer injection. Using dynamic whole-body (D-WB) PET imaging, we examine [18F]FES kinetics and explore whether Patlak parametric images ( Ki ) are quantitative and improve lesion visibility.ResultsThis prospective study included eight patients with metastatic ER-positive BC scanned using a D-WB PET acquisition protocol. The kinetics of [18F]FES were best characterized by the irreversible two-tissue compartment model in tumor lesions and in the majority of organ tissues. Ki values from Patlak parametric images correlated with Ki values from the full kinetic analysis, r2 = 0.77, and with the semiquantitative mean standardized uptake value (SUVmean), r2 = 0.91. Furthermore, parametric Ki images had the highest target-to-background ratio (TBR) in 162/164 metastatic lesions and the highest contrast-to-noise ratio (CNR) in 99/164 lesions compared to conventional SUV images. TBR was 2.45 (95% confidence interval (CI): 2.25-2.68) and CNR 1.17 (95% CI: 1.08-1.26) times higher in Ki images compared to SUV images. These quantitative differences were seen as reduced background activity in the Ki images.Conclusion[18F]FES uptake is best described by an irreversible two-tissue compartment model. D-WB [18F]FES PET/CT scans can be used for direct reconstruction of parametric Ki images, with superior lesion visibility and Ki values comparable to Ki values found from full kinetic analyses. This may aid correct ER classification and treatment decisions. Trial registration ClinicalTrials.gov: NCT04150731, https://clinicaltrials.gov/study/NCT04150731.

Project description:BackgroundKetones are increasingly recognized as an important and possibly oxygen sparing source of energy in vital organs such as the heart, the brain and the kidneys. Drug treatments, dietary regimens and oral ketone drinks designed to deliver ketones for organ and tissue energy production have therefore gained popularity. However, whether ingested ketones are taken up by various extra-cerebral tissues and to what extent is still largely unexplored. It was therefore the aim of this study to use positron emission tomography (PET) to explore the whole body dosimetry, biodistribution and kinetics of the ketone tracer (R)-[1-11C]β-hydroxybutyrate ([11C]OHB). Six healthy subjects (3 women and 3 men) underwent dynamic PET studies after both intravenous (90 min) and oral (120 min) administration of [11C]OHB. Dosimetry estimates of [11C]OHB was calculated using OLINDA/EXM software, biodistribution was assessed visually and [11C]OHB tissue kinetics were obtained using an arterial input function and tissue time-activity curves.ResultsRadiation dosimetry yielded effective doses of 3.28 [Formula: see text]Sv/MBq (intravenous administration) and 12.51 [Formula: see text]Sv/MBq (oral administration). Intravenous administration of [11C]OHB resulted in avid radiotracer uptake in the heart, liver, and kidneys, whereas lesser uptake was observed in the salivary glands, pancreas, skeletal muscle and red marrow. Only minimal uptake was noted in the brain. Oral ingestion of the tracer resulted in rapid radiotracer appearance in the blood and radiotracer uptake in the heart, liver and kidneys. In general, [11C]OHB tissue kinetics after intravenous administration were best described by a reversible 2-tissue compartmental model.ConclusionThe PET radiotracer [11C]OHB shows promising potential in providing imaging data on ketone uptake in various physiologically relevant tissues. As a result, it may serve as a safe and non-invasive imaging tool for exploring ketone metabolism in organs and tissues of both patients and healthy individuals. Trial registration Clinical trials, NCT0523812, Registered February 10th 2022, https://clinicaltrials.gov/ct2/show/NCT05232812?cond=NCT05232812&draw=2&rank=1 .

Dataset Information

Evaluation of quantitative modeling methods in whole-body, dynamic [¹¹C]-erlotinib PET.

Background

Results

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Background

Results

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OmicsDI is part of the ELIXIR infrastructure

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Evaluation of quantitative modeling methods in whole-body, dynamic [¹¹C]-erlotinib PET.