Intégration des analyses dynamique optimisée et radiomique en TEP à la 18F-FDOPA pour la caractérisation des gliomes en routine clinique

In the era of personalized medicine, having an efficient tool to provide additional information for the non-invasive characterization of gliomas is crucial. Such a tool would help clinicians make the best decisions to increase patient overall survival while preserving quality of life. In this context, medical imaging is becoming increasingly important especially amino-acid PET imaging, which is recommended by international guidelines as an adjunct to MRI. The 18F-FDOPA amino-acid radiotracer, which has been validated for PET in neuro-oncology, is the most widely used radiotracer for these indications in France. However, the interpretation of 18F-FDOPA PET results actually mainly relies on simple visual and semi-quantitative analyses of a static image that represents the spatial distribution of the radiotracer. This thesis seeks to better exploit each acquisition by adding dynamic and radiomics analyses. Dynamic analysis extract information about the temporal variation of tumoral metabolism and radiomics analysis quantify tumoral heterogeneity, a potential marker of tumor aggressiveness. This research work optimizes the methods for 18F-FDOPA PET dynamic analysis. It allows to provide a sound basis for drafting harmonization guidelines based on the simple and optimized semi quantitative model, which may be associated with normalization of tumor data with to the healthy brain, easily transposable to clinical routine practice and allowing to harmonize heterogeneously Carbidopa premedicated cases. The work then investigates the diagnostic performances of dynamic parameters for characterizing gliomas. Dynamic parameters are biomarkers of interest to non-invasively predict the IDH mutation even in the company of radiomics features, which may themselves help predict of the 1p/19q co-deletion. Although dynamic parameters are less important for the detection of glioma recurrences they deserve to be defined more precisely, particularly for high gr ade gliomas. In order to extend these results to the clinic, we developed a dedicated software for the advanced analysis of 18F-FDOPA PET in gliomas. In light of the results presented, dynamic and radiomics 18F-FDOPA PET analyses in neuro-oncology deserve to be further explored by integrating the temporal dimension in the computation of radiomics features. These analyses also need to be applied to other clinical questions through multicentric studies.À l’ère de la médecine personnalisée, il est essentiel de disposer d’un outil de caractérisation non-invasive des gliomes pour aider les cliniciens à prendre les meilleures décisions pour améliorer la survie globale du patient tout en préservant leur qualité de vie. L’imagerie médicale prend une place grandissante dans ce contexte et notamment l’imagerie TEP aux acides aminés qui est recommandée par les groupes d’experts internationaux en complément de l’IRM. Le radiotraceur aux acides aminés 18F-FDOPA, validé pour les indications de neuro-oncologie, est le plus utilisé en France pour ces indications. Cependant, l’interprétation des résultats d'une TEP à la 18F-FDOPA repose actuellement majoritairement sur de simples analyses visuelle et semi-quantitative d’une image statique qui retranscrit la distribution spatiale du radiotraceur. Cette thèse cherche à mieux exploiter chaque acquisition avec l’ajout des analyses dynamique et radiomique. L'analyse dynamique extrait une information sur l’évolution temporelle du métabolisme tumoral et l'analyse radiomique quantifie l’hétérogénéité tumorale, potentiel marqueur d’agressivité. Ce travail de recherche optimise les méthodes à utiliser pour effectuer une analyse dynamique en TEP à la 18F-FDOPA en neuro-oncologie. Cela permet de fournir une base pour la conception de recommandations avec l’utilisation d’un simple modèle semi-quantitatif, pouvant être associé à une normalisation des données de la tumeur avec celles du cerveau sain, facilement implémentable en routine clinique et permettant une harmonisation en cas de prémédication par Carbidopa hétérogène. Cette thèse étudie ensuite la performance diagnostique des paramètres dynamiques pour caractériser les gliomes. Les paramètres dynamiques sont des biomarqueurs d’intérêt pour la prédiction non-invasive de la mutation IDH même en présence des caractéristiques radiomiques, qui peuvent elles-mêmes aider à prédire la co-délétion 1p/19q. Bien que l es paramètres dynamiques soient moins importants pour la détection des récidives de gliomes, ils méritent être définis plus précisément, en particulier pour les gliomes de haut grade. Afin d’exploiter ces résultats en routine clinique, nous avons développé un logiciel dédié à l’analyse avancée d’images de TEP à la 18F-FDOPA dans les gliomes. Devant les résultats présentés, les analyses dynamique et radiomique méritent d'être explorées plus en profondeur en TEP à la 18F-FDOPA en neuro-oncologie par exemple en intégrant la dimension temporelle dans le calcul des caractéristiques radiomiques. Ces analyses doivent aussi être appliquées à d’autres questions cliniques à travers des études multicentriques

Intégration des analyses dynamique optimisée et radiomique en TEP à la 18F-FDOPA pour la caractérisation des gliomes en routine clinique

In the era of personalized medicine, having an efficient tool to provide additional information for the non-invasive characterization of gliomas is crucial. Such a tool would help clinicians make the best decisions to increase patient overall survival while preserving quality of life. In this context, medical imaging is becoming increasingly important especially amino-acid PET imaging, which is recommended by international guidelines as an adjunct to MRI. The 18F-FDOPA amino-acid radiotracer, which has been validated for PET in neuro-oncology, is the most widely used radiotracer for these indications in France. However, the interpretation of 18F-FDOPA PET results actually mainly relies on simple visual and semi-quantitative analyses of a static image that represents the spatial distribution of the radiotracer. This thesis seeks to better exploit each acquisition by adding dynamic and radiomics analyses. Dynamic analysis extract information about the temporal variation of tumoral metabolism and radiomics analysis quantify tumoral heterogeneity, a potential marker of tumor aggressiveness. This research work optimizes the methods for 18F-FDOPA PET dynamic analysis. It allows to provide a sound basis for drafting harmonization guidelines based on the simple and optimized semi quantitative model, which may be associated with normalization of tumor data with to the healthy brain, easily transposable to clinical routine practice and allowing to harmonize heterogeneously Carbidopa premedicated cases. The work then investigates the diagnostic performances of dynamic parameters for characterizing gliomas. Dynamic parameters are biomarkers of interest to non-invasively predict the IDH mutation even in the company of radiomics features, which may themselves help predict of the 1p/19q co-deletion. Although dynamic parameters are less important for the detection of glioma recurrences they deserve to be defined more precisely, particularly for high gr ade gliomas. In order to extend these results to the clinic, we developed a dedicated software for the advanced analysis of 18F-FDOPA PET in gliomas. In light of the results presented, dynamic and radiomics 18F-FDOPA PET analyses in neuro-oncology deserve to be further explored by integrating the temporal dimension in the computation of radiomics features. These analyses also need to be applied to other clinical questions through multicentric studies.À l’ère de la médecine personnalisée, il est essentiel de disposer d’un outil de caractérisation non-invasive des gliomes pour aider les cliniciens à prendre les meilleures décisions pour améliorer la survie globale du patient tout en préservant leur qualité de vie. L’imagerie médicale prend une place grandissante dans ce contexte et notamment l’imagerie TEP aux acides aminés qui est recommandée par les groupes d’experts internationaux en complément de l’IRM. Le radiotraceur aux acides aminés 18F-FDOPA, validé pour les indications de neuro-oncologie, est le plus utilisé en France pour ces indications. Cependant, l’interprétation des résultats d'une TEP à la 18F-FDOPA repose actuellement majoritairement sur de simples analyses visuelle et semi-quantitative d’une image statique qui retranscrit la distribution spatiale du radiotraceur. Cette thèse cherche à mieux exploiter chaque acquisition avec l’ajout des analyses dynamique et radiomique. L'analyse dynamique extrait une information sur l’évolution temporelle du métabolisme tumoral et l'analyse radiomique quantifie l’hétérogénéité tumorale, potentiel marqueur d’agressivité. Ce travail de recherche optimise les méthodes à utiliser pour effectuer une analyse dynamique en TEP à la 18F-FDOPA en neuro-oncologie. Cela permet de fournir une base pour la conception de recommandations avec l’utilisation d’un simple modèle semi-quantitatif, pouvant être associé à une normalisation des données de la tumeur avec celles du cerveau sain, facilement implémentable en routine clinique et permettant une harmonisation en cas de prémédication par Carbidopa hétérogène. Cette thèse étudie ensuite la performance diagnostique des paramètres dynamiques pour caractériser les gliomes. Les paramètres dynamiques sont des biomarqueurs d’intérêt pour la prédiction non-invasive de la mutation IDH même en présence des caractéristiques radiomiques, qui peuvent elles-mêmes aider à prédire la co-délétion 1p/19q. Bien que l es paramètres dynamiques soient moins importants pour la détection des récidives de gliomes, ils méritent être définis plus précisément, en particulier pour les gliomes de haut grade. Afin d’exploiter ces résultats en routine clinique, nous avons développé un logiciel dédié à l’analyse avancée d’images de TEP à la 18F-FDOPA dans les gliomes. Devant les résultats présentés, les analyses dynamique et radiomique méritent d'être explorées plus en profondeur en TEP à la 18F-FDOPA en neuro-oncologie par exemple en intégrant la dimension temporelle dans le calcul des caractéristiques radiomiques. Ces analyses doivent aussi être appliquées à d’autres questions cliniques à travers des études multicentriques

Is IDH mutation status associated with 18F-FDopa PET uptake?

International audienc

Implementing the Point Spread Function Deconvolution for Better Molecular Characterization of Newly Diagnosed Gliomas: A Dynamic ¹⁸F-FDOPA PET Radiomics Study

Purpose: This study aims to investigate the effects of applying the point spread function deconvolution (PSFd) to the radiomics analysis of dynamic L-3,4-dihydroxy-6-[18F]-fluoro-phenyl-alanine (18F-FDOPA) positron emission tomography (PET) images, to non-invasively identify isocitrate dehydrogenase (IDH) mutated and/or 1p/19q codeleted gliomas. Methods: Fifty-seven newly diagnosed glioma patients underwent dynamic 18F-FDOPA imaging on the same digital PET system. All images were reconstructed with and without PSFd. An L1-penalized (Lasso) logistic regression model, with 5-fold cross-validation and 20 repetitions, was trained with radiomics features extracted from the static tumor-to-background-ratio (TBR) and dynamic time-to-peak (TTP) parametric images, as well as a combination of both. Feature importance was assessed using Shapley additive explanation values. Results: The PSFd significantly modified 95% of TBR, but only 79% of TTP radiomics features. Applying the PSFd significantly improved the ability to identify IDH-mutated and/or 1p/19q codeleted gliomas, compared to PET images not processed with PSFd, with respective areas under the curve of 0.83 versus 0.79 and 0.75 versus 0.68 for a combination of static and dynamic radiomics features (p 18F-FDOPA PET imaging significantly improves the detection of molecular parameters in newly diagnosed gliomas, most notably by modifying TBR radiomics features

Application of PET imaging delta radiomics for predicting progression-free survival in rare high-grade glioma

Abstract This study assesses the feasibility of using a sample-efficient model to investigate radiomics changes over time for predicting progression-free survival in rare diseases. Eighteen high-grade glioma patients underwent two L-3,4-dihydroxy-6-[18F]-fluoro-phenylalanine positron emission tomography (PET) dynamic scans: the first during treatment and the second at temozolomide chemotherapy discontinuation. Radiomics features from static/dynamic parametric images, alongside conventional features, were extracted. After excluding highly correlated features, 16 different models were trained by combining various feature selection methods and time-to-event survival algorithms. Performance was assessed using cross-validation. To evaluate model robustness, an additional dataset including 35 patients with a single PET scan at therapy discontinuation was used. Model performance was compared with a strategy extracting informative features from the set of 35 patients and applying them to the 18 patients with 2 PET scans. Delta-absolute radiomics achieved the highest performance when the pipeline was directly applied to the 18-patient subset (support vector machine (SVM) and recursive feature elimination (RFE): C-index = 0.783 [0.744–0.818]). This result remained consistent when transferring informative features from 35 patients (SVM + RFE: C-index = 0.751 [0.716–0.784], p = 0.06). In addition, it significantly outperformed delta-absolute conventional (C-index = 0.584 [0.548–0.620], p < 0.001) and single-time-point radiomics features (C-index = 0.546 [0.512–0.580], p < 0.001), highlighting the considerable potential of delta radiomics in rare cancer cohorts

Relevance of Dynamic ¹⁸F-DOPA PET Radiomics for Differentiation of High-Grade Glioma Progression from Treatment-Related Changes

This study evaluates the relevance of 18F-DOPA PET static and dynamic radiomics for differentiation of high-grade glioma (HGG) progression from treatment-related changes (TRC) by comparing diagnostic performances to the current PET imaging standard of care. Eighty-five patients with histologically confirmed HGG and investigated by dynamic 18F-FDOPA PET in two institutions were retrospectively selected. ElasticNet logistic regression, Random Forest and XGBoost machine models were trained with different sets of features—radiomics extracted from static tumor-to-background-ratio (TBR) parametric images, radiomics extracted from time-to-peak (TTP) parametric images, as well as combination of both—in order to discriminate glioma progression from TRC at 6 months from the PET scan. Diagnostic performances of the models were compared to a logistic regression model with TBRmean ± clinical features used as reference. Training was performed on data from the first center, while external validation was performed on data from the second center. Best radiomics models showed only slightly better performances than the reference model (respective AUCs of 0.834 vs. 0.792, p < 0.001). Our current results show similar findings at the multicentric level using different machine learning models and report a marginal additional value for TBR static and TTP dynamic radiomics over the classical analysis based on TBR values

High-quality brain perfusion SPECT images may be achieved with a high-speed recording using 360° CZT camera

International audienceAbstract Objective The aim of this study was to compare brain perfusion SPECT obtained from a 360° CZT and a conventional Anger camera. Methods The 360° CZT camera utilizing a brain configuration, with 12 detectors surrounding the head, was compared to a 2-head Anger camera for count sensitivity and image quality on 30-min SPECT recordings from a brain phantom and from 99m Tc-HMPAO brain perfusion in 2 groups of 21 patients investigated with the CZT and Anger cameras, respectively. Image reconstruction was adjusted according to image contrast for each camera. Results The CZT camera provided more than 2-fold increase in count sensitivity, as compared with the Anger camera, as well as (1) lower sharpness indexes, giving evidence of higher spatial resolution, for both peripheral/central brain structures, with respective median values of 5.2%/3.7% versus 2.4%/1.9% for CZT and Anger camera respectively in patients ( p < 0.01), and 8.0%/6.9% versus 6.2%/3.7% on phantom; and (2) higher gray/white matter contrast on peripheral/central structures, with respective ratio median values of 1.56/1.35 versus 1.11/1.20 for CZT and Anger camera respectively in patients ( p < 0.05), and 2.57/2.17 versus 1.40/1.12 on phantom; and (3) no change in noise level. Image quality, scored visually by experienced physicians, was also significantly higher on CZT than on the Anger camera (+ 80%, p < 0.01), and all these results were unchanged on the CZT images obtained with only a 15 min recording time. Conclusion The 360° CZT camera provides brain perfusion images of much higher quality than a conventional Anger camera, even with high-speed recordings, thus demonstrating the potential for repositioning brain perfusion SPECT to the forefront of brain imaging

Effects of Carbidopa Premedication on 18F-FDOPA PET Imaging of Glioma: A Multiparametric Analysis

Purpose: This study aimed to determine the impact of carbidopa premedication on static, dynamic and radiomics parameters of 18F-FDOPA PET in brain tumors. Methods: The study included 54 patients, 18 of whom received carbidopa, who underwent 18F-FDOPA PET for newly diagnosed gliomas. SUV-derived, 105 radiomics features and TTP dynamic parameters were extracted from volumes of interest in healthy brains and tumors. Simulation of the effects of carbidopa on time-activity curves were generated. Results: All static and TTP dynamic parameters were significantly higher in healthy brain regions of premedicated patients (ΔSUVmean = +53%, ΔTTP = +48%, p &lt; 0.001). Furthermore, carbidopa impacted 81% of radiomics features, of which 92% correlated with SUVmean (absolute correlation coefficient ≥ 0.4). In tumors, premedication with carbidopa was an independent predictor of SUVmean (ΔSUVmean = +52%, p &lt; 0.001) and TTP (ΔTTP = +24%, p = 0.025). All parameters were no longer significantly modified by carbidopa premedication when using ratios to healthy brain. Simulated data confirmed that carbidopa leads to higher tumor TTP values, corrected by the ratios. Conclusion: In 18F-FDOPA PET, carbidopa induces similarly higher SUV and TTP dynamic parameters and similarly impacts SUV-dependent radiomics in healthy brain and tumor regions, which is compensated for by correcting for the tumor-to-healthy-brain ratio. This is a significant advantage for multicentric study harmonization

Photopenic Defects in Gliomas With Amino-Acid PET and Relative Prognostic Value

The aim is to explore the concept of photopenic defects in newly diagnosed glioma patients with the 2 widely used 11C-MET and 18F-FDOPA PET amino acid tracers. Thirty-two 11C-MET and 26 18F-FDOPA PET scans with amino acid PET-negative gliomas were selected in this European multicentric study. Of these gliomas, 16 11C-MET and 10 18F-FDOPA PET scans with photopenic defects were identified, exhibiting lower mean tumor-to-background ratio as compared with isometabolic gliomas (P < 0.001). Gliomas with photopenic defects had no different progression-free survival than isometabolic gliomas in the whole population (P = 0.40), but shorter progression-free survival in the subgroup of World Health Organization grade II IDH-mutant astrocytomas (35 vs 68 months; P = 0.047)

Data_Sheet_1_Identification of resting-state networks using dynamic brain perfusion SPECT imaging: A fSPECT case report.pdf

Connectivity studies with nuclear medicine systems are scarce in literature. They mainly employ PET imaging and group level analyses due to the low temporal resolution of PET and especially SPECT imaging. Our current study analyses connectivity at an individual level using dynamic SPECT imaging, which has been enabled by the improved temporal resolution performances provided by the 360°CZT cameras. We present the case of an 80-year-old man referred for brain perfusion SPECT imaging for cognitive disorders for whom a dynamic SPECT acquisition was performed utilizing a 360°CZT camera (temporal sampling of 15 frames × 3 s, 10 frames × 15 s, 14 frames × 30 s), followed by a conventional static acquisition of 15 m. Functional SPECT connectivity (fSPECT) was assessed through a seed correlation analysis and 5 well-known resting-state networks were identified: the executive, the default mode, the sensory motor, the salience, and the visual networks. This case report supports the feasibility of fSPECT imaging to identify well known resting-state networks, thanks to the novel properties of a 360°CZT camera, and opens the way to the development of more dedicated functional connectivity studies using brain perfusion SPECT imaging.</p