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¹⁸F-FDOPA PET for the Noninvasive Prediction of Glioma Molecular Parameters: A Radiomics Study.

ABSTRACT: The assessment of gliomas by ¹⁸F-FDOPA PET imaging as an adjunct to MRI showed high performance by combining static and dynamic features to noninvasively predict the isocitrate dehydrogenase (IDH) mutations and the 1p/19q codeletion, which the World Health Organization classified as significant parameters in 2016. The current study evaluated whether other ¹⁸F-FDOPA PET radiomics features further improve performance and the contributions of each of these features to performance. Methods: Our study included 72 retrospectively selected, newly diagnosed glioma patients with ¹⁸F-FDOPA PET dynamic acquisitions. A set of 114 features, including conventional static features and dynamic features, as well as other radiomics features, were extracted and machine-learning models trained to predict IDH mutations and the 1p/19q codeletion. Models were based on a machine-learning algorithm built from stable, relevant, and uncorrelated features selected by hierarchic clustering followed by a bootstrapped feature selection process. Models were assessed by comparing area under the curve using a nested cross-validation approach. Feature importance was assessed using Shapley additive explanations values. Results: The best models were able to predict IDH mutations (logistic regression with L2 regularization) and the 1p/19q codeletion (support vector machine with radial basis function kernel) with an area under the curve of 0.831 (95% CI, 0.790-0.873) and 0.724 (95% CI, 0.669-0.782), respectively. For the prediction of IDH mutations, dynamic features were the most important features in the model (time to peak, 35.5%). In contrast, other radiomics features were the most useful for predicting the 1p/19q codeletion (up to 14.5% of importance for the small-zone low-gray-level emphasis). Conclusion: ¹⁸F-FDOPA PET is an effective tool for the noninvasive prediction of glioma molecular parameters using a full set of amino-acid PET radiomics features. The contribution of each feature set shows the importance of systematically integrating dynamic acquisition for prediction of the IDH mutations as well as developing the use of radiomics features in routine practice for prediction of the 1p/19q codeletion.

SUBMITTER: Zaragori T

PROVIDER: S-EPMC8717204 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Multiple myeloma (MM) is a debilitating neoplasm of terminally differentiated plasma B cells that resulted in over 13,000 deaths in 2017 alone. Combination therapies involving melphalan, a small-molecule DNA alkylating agent, are commonly prescribed to patients with relapsed or refractory MM, necessitating the stratification of responding patients to minimize toxicities and improve quality of life. Here, we evaluated the use of 3,4-dihydroxy-6-18F-fluoro-l-phenylalanine (18F-FDOPA), a clinically available PET radiotracer with specificity to the L-type amino acid transporter 1 (LAT1), which also mediates melphalan uptake, for imaging melphalan therapy response in a preclinical immunocompetent model of MM. Methods: C57BL/KaLwRij mice were implanted subcutaneously with unilateral murine green fluorescent protein-expressing 5TGM1 tumors and divided into 3 independent groups: untreated, treated beginning week 2 after tumor implantation, and treated beginning week 3 after tumor implantation. The untreated and week 2 treated groups were imaged with preclinical MRI and dynamic 18F-FDG and 18F-FDOPA PET/CT at week 4 on separate, contiguous days, whereas the week 3 treated group was longitudinally imaged weekly for 3 wk. Metabolic tumor volume, total lesion avidity, SUVmax, and total uptake were calculated for both tracers. Immunohistochemistry was performed on representative tissue from all groups for LAT1 and glucose transporter 1 (GLUT1) expression. Results: Melphalan therapy induced a statistically significant reduction in lesion avidity and uptake for both 18F-FDG and 18F-FDOPA. There was no visible effect on GLUT1 expression, but LAT1 density increased in the week 2 treated group. Longitudinal imaging of the week 3 treated group showed variable changes in 18F-FDG and 18F-FDOPA uptake, with an increase in 18F-FDOPA lesion avidity in the second week relative to baseline. LAT1 and GLUT1 surface density in the untreated and week 3 treated groups were qualitatively similar. Conclusion: 18F-FDOPA PET/CT complemented 18F-FDG PET/CT in imaging melphalan therapy response in preclinical extramedullary MM. 18F-FDOPA uptake was linked to LAT1 expression and melphalan response, with longitudinal imaging suggesting stabilization of LAT1 levels and melphalan tumor cytotoxicity. Future work will explore additional MM cell lines with heterogeneous LAT1 expression and response to melphalan therapy.

Project description:BackgroundDiffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma in adults. Standard treatment includes chemoimmunotherapy with R-CHOP or similar regimens. Despite treatment advancements, many patients with DLBCL experience refractory disease or relapse. While baseline 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) parameters have shown promise in predicting survival, they may not fully capture lesion heterogeneity. This study aimed to assess the prognostic value of baseline 18F-FDG PET radiomics features in comparison with clinical factors and metabolic parameters for assessing 2-year progression-free survival (PFS) and 5-year overall survival (OS) in patients with DLBCL.ResultsA total of 201 patients with DLBCL were enrolled in this study, and 1328 radiomics features were extracted. The radiomics signatures, clinical factors, and metabolic parameters showed significant prognostic value for individualized prognosis prediction in patients with DLBCL. Radiomics signatures showed the lowest Akaike information criterion (AIC) value and highest Harrell's concordance index (C-index) value in comparison with clinical factors and metabolic parameters for both PFS (AIC: 571.688 vs. 596.040 vs. 576.481; C-index: 0.732 vs. 0.658 vs. 0.702, respectively) and OS (AIC: 339.843 vs. 363.671 vs. 358.412; C-index: 0.759 vs. 0.667 vs. 0.659, respectively). Statistically significant differences were observed in the area under the curve (AUC) values between the radiomics signatures and clinical factors for both PFS (AUC: 0.768 vs. 0.681, P = 0.017) and OS (AUC: 0.767 vs. 0.667, P = 0.023). For OS, the AUC of the radiomics signatures were significantly higher than those of metabolic parameters (AUC: 0.767 vs. 0.688, P = 0.007). However, for PFS, no significant difference was observed between the radiomics signatures and metabolic parameters (AUC: 0.768 vs. 0.756, P = 0.654). The combined model and the best-performing individual model (radiomics signatures) alone showed no significant difference for both PFS (AUC: 0.784 vs. 0.768, P = 0.163) or OS (AUC: 0.772 vs. 0.767, P = 0.403).ConclusionsRadiomics signatures derived from PET images showed the high predictive power for progression in patients with DLBCL. The combination of radiomics signatures, clinical factors, and metabolic parameters may not significantly improve predictive value beyond that of radiomics signatures alone.

Project description:PurposeDynamic amino acid positron emission tomography (PET) has become essential in neuro-oncology, most notably for its prognostic value in the noninvasive prediction of isocitrate dehydrogenase (IDH) mutations in newly diagnosed gliomas. The 6-[18F]fluoro-l-DOPA (18F-FDOPA) kinetic model has an underlying complexity, while previous studies have predominantly used a semiquantitative dynamic analysis. Our study addresses whether a semiquantitative analysis can capture all the relevant information contained in time-activity curves for predicting the presence of IDH mutations compared to the more sophisticated graphical and compartmental models.MethodsThirty-seven tumour time-activity curves from 18F-FDOPA PET dynamic acquisitions of newly diagnosed gliomas (median age = 58.3 years, range = 20.3-79.9 years, 16 women, 16 IDH-wild type) were analyzed with a semiquantitative model based on classical parameters, with (SQ) or without (Ref SQ) a reference region, or on parameters of a fit function (SQ Fit), a graphical Logan model with input function (Logan) or reference region (Ref Logan), and a two-tissue compartmental model previously reported for 18F-FDOPA PET imaging of gliomas (2TCM). The overall predictive performance of each model was assessed with an area under the curve (AUC) comparison using multivariate analysis of all the parameters included in the model. Moreover, each extracted parameter was assessed in a univariate analysis by a receiver operating characteristic curve analysis.ResultsThe SQ model with an AUC of 0.733 for predicting IDH mutations showed comparable performance to the other models with AUCs of 0.752, 0.814, 0.693, 0.786, and 0.863, respectively corresponding to SQ Fit, Ref SQ, Logan, Ref Logan, and 2TCM (p ≥ 0.10 for the pairwise comparisons with other models). In the univariate analysis, the SQ time-to-peak parameter had the best diagnostic performance (75.7% accuracy) compared to all other individual parameters considered.ConclusionsThe SQ model circumvents the complexities of the 18F-FDOPA kinetic model and yields similar performance in predicting IDH mutations when compared to the other models, most notably the compartmental model. Our study provides supportive evidence for the routine clinical application of the SQ model for the dynamic analysis of 18F-FDOPA PET images in newly diagnosed gliomas.

Project description:PurposeMethylation of the O6-methylguanine methyltransferase (MGMT) gene promoter is associated with improved treatment response and survival in patients with glioblastoma (GB), but the necessary pathologic specimen can be nondiagnostic. In this study, we assessed whether radiomics features from pretreatment 18F-DOPA positron emission tomography (PET) imaging could be used to predict pathologic MGMT status.Methods and materialsThis study included 86 patients with newly diagnosed GB, split into 3 groups (training, validating, and predicting). We performed a radiomics analysis on 18F-DOPA PET images by extracting features from 2 tumor-based contours: a "Gold" contour of all abnormal uptake per expert nuclear medicine physician and a high-grade glioma (HGG) contour based on a tumor-to-normal hemispheric ratio >2.0, representing the most aggressive components. Feature selection was performed by comparing the weighted feature importance and filtering with bivariate analysis. Optimization of model parameters was explored using grid search with selected features. The stability of the model with increasing input features was also investigated for model robustness. The model predictions were then applied by comparing the overall survival probability of the patients with GB and unknown MGMT status versus those with known MGMT status.ResultsA radiomics signature was constructed to predict MGMT methylation status. Using features extracted from HGG contour alone with a random forest model, we achieved 80% ± 10% accuracy for 95% confidence level in predicting MGMT status. The prediction accuracy was not improved with the addition of the Gold contour or with more input features. The model was applied to the patients with unknown MGMT methylation status. The prediction results are consistent with what is expected using overall survival as a surrogate.ConclusionsThis study suggests that 3 features from radiomics modeling of 18F-DOPA PET imaging can predict MGMT methylation status with reasonable accuracy. These results could provide valuable therapeutic guidance for patients in whom MGMT testing is inconclusive or nondiagnostic.

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¹⁸F-FDOPA PET for the Noninvasive Prediction of Glioma Molecular Parameters: A Radiomics Study.

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18F-FDOPA PET for the Noninvasive Prediction of Glioma Molecular Parameters: A Radiomics Study.

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OmicsDI is part of the ELIXIR infrastructure

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¹⁸F-FDOPA PET for the Noninvasive Prediction of Glioma Molecular Parameters: A Radiomics Study.