Dataset Information

Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic [¹⁸F]FET PET radiomics.

ABSTRACT:

Purpose

To evaluate radiomic features extracted from standard static images (20-40 min p.i.), early summation images (5-15 min p.i.), and dynamic [¹⁸F]FET PET images for the prediction of TERTp-mutation status in patients with IDH-wildtype high-grade glioma.

Methods

A total of 159 patients (median age 60.2 years, range 19-82 years) with newly diagnosed IDH-wildtype diffuse astrocytic glioma (WHO grade III or IV) and dynamic [¹⁸F]FET PET prior to surgical intervention were enrolled and divided into a training (n = 112) and a testing cohort (n = 47) randomly. First-order, shape, and texture radiomic features were extracted from standard static (20-40 min summation images; TBR_20-40), early static (5-15 min summation images; TBR_5-15), and dynamic (time-to-peak; TTP) images, respectively. Recursive feature elimination was used for feature selection by 10-fold cross-validation in the training cohort after normalization, and logistic regression models were generated using the radiomic features extracted from each image to differentiate TERTp-mutation status. The areas under the ROC curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive value were calculated to illustrate diagnostic power in both the training and testing cohort.

Results

The TTP model comprised nine selected features and achieved highest predictability of TERTp-mutation with an AUC of 0.82 (95% confidence interval 0.71-0.92) and sensitivity of 92.1% in the independent testing cohort. Weak predictive capability was obtained in the TBR_5-15 model, with an AUC of 0.61 (95% CI 0.42-0.80) in the testing cohort, while no predictive power was observed in the TBR_20-40 model.

Conclusions

Radiomics based on TTP images extracted from dynamic [¹⁸F]FET PET can predict the TERTp-mutation status of IDH-wildtype diffuse astrocytic high-grade gliomas with high accuracy preoperatively.

SUBMITTER: Li Z

PROVIDER: S-EPMC8566644 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe ability to accurately and non-invasively distinguish high-grade glioma from low-grade glioma remains a challenge despite advances in molecular and magnetic resonance imaging. We investigated the ability of fluciclovine (18F) PET as a means to identify and distinguish these lesions in patients with known gliomas and to correlate uptake with Ki-67.ResultsSixteen patients with a total of 18 newly diagnosed low-grade gliomas (n?=?6) and high grade gliomas (n?=?12) underwent fluciclovine PET imaging after histopathologic assessment. Fluciclovine PET analysis comprised tumor SUVmax and SUVmean, as well as metabolic tumor thresholds (1.3*, 1.6*, 1.9*) to normal brain background (TBmax, and TBmean). Comparison was additionally made to the proliferative status of the tumor as indicated by Ki-67 values. Fluciclovine uptake greater than normal brain parenchyma was found in all lesions studied. Time activity curves demonstrated statistically apparent flattening of the curves for both high-grade gliomas and low-grade gliomas starting 30 min after injection, suggesting an influx/efflux equilibrium. The best semiquantitative metric in discriminating HGG from LGG was obtained utilizing a metabolic 1 tumor threshold of 1.3* contralateral normal brain parenchyma uptake to create a tumor: background (TBmean1.3) cutoff of 2.15 with an overall sensitivity of 97.5% and specificity of 95.5%. Additionally, using a SUVmax?>?4.3 cutoff gave a sensitivity of 90.9% and specificity of 97.5%. Tumor SUVmean and tumor SUVmax as a ratio to mean normal contralateral brain were both found to be less relevant predictors of tumor grade. Both SUVmax (R?=?0.71, p?=?0.0227) and TBmean (TBmean1.3: R?=?0.81, p?=?0.00081) had a high correlation with the tumor proliferative index Ki-67.ConclusionsFluciclovine PET produces high-contrast images between both low-grade and high grade gliomas and normal brain by visual and semiquantitative analysis. Fluciclovine PET appears to discriminate between low-grade glioma and high-grade glioma, but must be validated with a larger sample size.

Project description:PurposeWe aimed to investigate the predictive models based on O-[2-(18F)fluoroethyl]-l-tyrosine positron emission tomography/computed tomography (18F-FET PET/CT) radiomics features for the isocitrate dehydrogenase (IDH) genotype identification in adult gliomas.MethodsFifty-eight consecutive pathologically confirmed adult glioma patients with pretreatment 18F-FET PET/CT were retrospectively enrolled. One hundred and five radiomics features were extracted for analysis in each modality. Three independent radiomics models (PET-Rad Model, CT-Rad Model and PET/CT-Rad Model) predicting IDH mutation status were generated using the least absolute shrinkage and selection operator (LASSO) regression analysis based on machine learning algorithms. All-subsets regression and cross validation were applied for the filter and calibration of the predictive radiomics models. Besides, semi-quantitative parameters including maximum, peak and mean tumor to background ratio (TBRmax, TBRpeak, TBRmean), standard deviation of glioma lesion standardized uptake value (SUVSD), metabolic tumor volume (MTV) and total lesion tracer uptake (TLU) were obtained and filtered for the simple model construction with clinical feature of brain midline involvement status. The area under the receiver operating characteristic curve (AUC) was applied for the evaluation of the predictive models.ResultsThe AUC of the simple predictive model consists of semi-quantitative parameter SUVSD and dichotomized brain midline involvement status was 0.786 (95% CI 0.659-0.883). The AUC of PET-Rad Model building with three 18F-FET PET radiomics parameters was 0.812 (95% CI 0.688-0.902). The AUC of CT-Rad Model building with three co-registered CT radiomics parameters was 0.883 (95% CI 0.771-0.952). While the AUC of the combined 18F-FET PET/CT-Rad Model building with three CT and one PET radiomics features was 0.912 (95% CI 0.808-0.970). DeLong test results indicated the PET/CT-Rad Model outperformed the PET-Rad Model (p = 0.048) and simple predictive model (p = 0.034). Further combination of the PET/CT-Rad Model with the clinical feature of dichotomized tumor location status could slightly enhance the AUC to 0.917 (95% CI 0.814-0.973).ConclusionThe predictive model combining 18F-FET PET and integrated CT radiomics features could significantly enhance and well balance the non-invasive IDH genotype prediction in untreated gliomas, which is important in clinical decision making for personalized treatment.

Project description:PurposeGlioblastoma multiforme (GBM) is the most common glioma and standard therapies can only slightly prolong the survival. Neo-vascularization is a potential target to image tumor microenvironment, as it defines its brain invasion. We investigate [18F]rhPSMA-7.3 with PET/MRI for quantitative imaging of neo-vascularization in GBM bearing mice and human tumor tissue and compare it to [18F]FET and [18F]fluciclovine using PET pharmacokinetic modeling (PKM).Methods[18F]rhPSMA-7.3, [18F]FET, and [18F]fluciclovine were i.v. injected with 10.5 ± 3.1 MBq, 8.0 ± 2.2 MBq, 11.5 ± 1.9 MBq (n = 28, GL261-luc2) and up to 90 min PET/MR imaged 21/28 days after surgery. Regions of interest were delineated on T2-weighted MRI for (i) tumor, (ii) brain, and (iii) the inferior vena cava. Time-activity curves were expressed as SUV mean, SUVR and PKM performed using 1-/2-tissue-compartment models (1TCM, 2TCM), Patlak and Logan analysis (LA). Immunofluorescent staining (IFS), western blotting, and autoradiography of tumor tissue were performed for result validation.Results[18F]rhPSMA-7.3 showed a tumor uptake with a tumor-to-background-ratio (TBR) = 2.1-2.5, in 15-60 min. PKM (2TCM) confirmed higher K1 (0.34/0.08, p = 0.0012) and volume of distribution VT (0.24/0.1, p = 0.0017) in the tumor region compared to the brain. Linearity in LA and similar k3 = 0.6 and k4 = 0.47 (2TCM, tumor, p = ns) indicated reversible binding. K1, an indicator for vascularization, increased (0.1/0.34, 21 to 28 days, p < 0.005). IFS confirmed co-expression of PSMA and tumor vascularization. [18F]fluciclovine showed higher TBR (2.5/1.8, p < 0.001, 60 min) and VS (1.3/0.7, p < 0.05, tumor) compared to [18F]FET and LA indicated reversible binding. VT increased (p < 0.001, tumor, 21 to 28 days) for [18F]FET (0.5-1.4) and [18F]fluciclovine (0.84-1.5).Conclusion[18F]rhPSMA-7.3 showed to be a potential candidate to investigate the tumor microenvironment of GBM. Following PKM, this uptake was associated with tumor vascularization. In contrast to what is known from PSMA-PET in prostate cancer, reversible binding was found for [18F]rhPSMA-7.3 in GBM, contradicting cellular trapping. Finally, [18F]fluciclovine was superior to [18F]FET rendering it more suitable for PET imaging of GBM.

Project description:BackgroundThe aim of this study was to assess radiomics features on pre-treatment [18F]FDG positron emission tomography (PET) as potential biomarkers for response and survival in patients with metastatic colorectal cancer (mCRC).MethodsPatients with mCRC underwent [18F]FDG PET/computed tomography (CT) prior to first- or third-line palliative systemic treatment. Tumour lesions were semiautomatically delineated and standard uptake value (SUV), metabolically active tumour volume (MATV), total lesion glycolysis (TLG), entropy, area under the curve of the cumulative SUV-volume histogram (AUC-CSH), compactness and sphericity were obtained.ResultsLesions of 47 patients receiving third-line systemic treatment had higher SUVmax, SUVpeak, SUVmean, MATV and TLG, and lower AUC-CSH, compactness and sphericity compared to 52 patients receiving first-line systemic treatment. Therefore, first- and third-line groups were evaluated separately. In the first-line group, anatomical changes on CT correlated negatively with TLG (ρ = 0.31) and MATV (ρ = 0.36), and positively with compactness (ρ = -0.27) and sphericity (ρ = -0.27). Patients without benefit had higher mean entropy (p = 0.021). Progression-free survival (PFS) and overall survival (OS) were worse with a decreased mean AUC [hazard ratio (HR) 0.86, HR 0.77] and increase in mean MATV (HR 1.15, HR 1.22), sum MATV (HR 1.14, HR 1.19), mean TLG (HR 1.16, HR 1.22) and sum TLG (HT1.12, HR1.18). In the third-line group, AUC-CSH correlated negatively with anatomical change (ρ = 0.21). PFS and OS were worse with an increased mean MATV (HR 1.27, HR 1.68), sum MATV (HR 1.35, HR 2.04), mean TLG (HR 1.29, HR 1.52) and sum TLG (HT 1.27, HR 1.80). SUVmax and SUVpeak negatively correlated with OS (HR 1.19, HR 1.21). Cluster analysis of the 10 radiomics features demonstrated no complementary value in identifying aggressively growing lesions or patients with impaired survival.ConclusionWe demonstrated an association between improved clinical outcome and pre-treatment low tumour volume and heterogeneity as well as high sphericity on [18F]FDG PET. Future PET imaging research should include radiomics features that incorporate tumour volume and heterogeneity when correlating PET data with clinical outcome.

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Dataset Information

Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic [¹⁸F]FET PET radiomics.

Purpose

Methods

Results

Conclusions

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OmicsDI is part of the ELIXIR infrastructure

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Dataset Information

Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic [18F]FET PET radiomics.

Purpose

Methods

Results

Conclusions

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic [¹⁸F]FET PET radiomics.