177Lu-labeled somatostatin receptor targeted radionuclide therapy dosimetry in meningioma: a systematic review.

INTRODUCTION: Few therapeutic options are currently available for refractory meningiomas. Encouraging results have been reported for 177Lu-labeled somatostatin receptor-targeted radiopeptide therapy (SSTR-RT). The current therapeutic scheme is based on the fixed doses that are recommended for neuroendocrine tumor treatment. However, in personalized medicine, tumor dosimetry can be determined from repeat 177Lu scintigraphy. The aim of this review was to report on the methods used for calculating the tumor absorbed dose (AD) in meningioma patients treated with 177Lu-SSTR-RT and their values. EVIDENCE ACQUISITION: The search was performed in Medline, Embase and the Cochrane Library until March 1st, 2024 to retrieve papers related to the topic. The following terms were used for searching: (meningioma) AND ((sstr) OR (receptors somatostatin) OR (somatostatin) OR (octreotide)) AND ((PRRT) OR (radionuclide therapy) OR (dotatate) OR (dotatoc) OR (177Lu-DOTATOC) OR (177Lu-DOTATATE) OR (radiopeptide)). EVIDENCE SYNTHESIS: Seven articles (including 46 patients and 108 cycles of treatment) reporting on tumor AD during 177Lu-SSTR-RT were included in the analysis. The methods of acquisition, reconstruction parameters and postimage processing to determine tumor AD were very heterogeneous among the studies. The meningioma AD associated with the agonist 177Lu-SSTR-RT reported in the majority of studies ranged from 0.1-1.5 Gy/GBq, which was lower than that reported for neuroendocrine tumors (1.3-22.9 Gy/GBq). CONCLUSIONS: The tumor AD that was reported during treatment with 177Lu-SSTR-RT in refractory meningioma patients is generally low. Harmonization of the methodology for dosimetry calculations is needed to compare the different reported values and optimize treatment at the individual level.

Amino-acid PET as a prognostic tool after post Stupp protocol temozolomide therapy in high-grade glioma patients.

PURPOSE: This study aimed to evaluate the prognostic performance of amino-acid PET in high-grade gliomas (HGG) patients at the time of temozolomide (TMZ) treatment discontinuation, after the Stupp protocol. METHODS: The analysis included consecutive HGG patients with dynamic [18F]FDOPA PET imaging within 3 months of the end of TMZ therapy, post-Stupp protocol. Static and dynamic PET parameters, responses to RANO criteria for MRI and clinical and histo-molecular factors were correlated to progression-free (PFS). RESULTS: Thirty-two patients (59.4 [54.0;67.6] years old, 13 (41%) women) were included. Static PET parameters peak tumor-to-background ratio and metabolic tumor volume (respective thresholds of 1.9 and 1.5 mL) showed the best 84% accuracies for predicting PFS at 6 months (p = 0.02). These static PET parameters were also independent predictor of PFS in multivariate analysis (p ≤ 0.05). CONCLUSION: In HGG patients having undergone a Stupp protocol, the absence of significant PET uptake after TMZ constitutes a favorable prognostic factor.

The role of [18 F]FDOPA PET as an adjunct to conventional MRI in the diagnosis of aggressive glial lesions.

BACKGROUND: Amino acid PET is recommended for the initial diagnosis of brain lesions, but its value for identifying aggressive lesions remains to be established. The current study therefore evaluates the added-value of dynamic [18 F]FDOPA PET as an adjunct to conventional MRI for determining the aggressiveness of presumed glial lesions at diagnosis. METHODS: Consecutive patients, with a minimal 1 year-follow-up, underwent contrast-enhanced MRI (CE MRI) and dynamic [18 F]FDOPA PET to characterize their suspected glial lesion. Lesions were classified semi-automatically by their CE MRI (MRI-/+), and PET parameters (static tumor-to-background ratio, TBR; dynamic time-to-peak ratio, TTPratio). Diagnostic accuracies of MRI and PET parameters for the differentiation of tumor aggressiveness were evaluated by chi-square test or receiver operating characteristic analyses. Aggressive lesions were either defined as lesions with dismal molecular characteristics based on the WHO 2021 classification of brain tumors or with compatible clinico-radiological profiles. Time-to-treatment failure (TTF) and overall survival (OS) were evaluated. RESULTS: Of the 109 patients included, 46 had aggressive lesions (45 confirmed by histo-molecular analyses). CE MRI identified aggressive lesions with an accuracy of 73%. TBRmax (threshold of 3.2), and TTPratio (threshold of 5.4 min) respectively identified aggressive lesions with an accuracy of 83% and 76% and were independent of CE MRI and clinical factors in the multivariate analysis. Among the MRI-lesions, 11/56 (20%) were aggressive and respectively 55% and 50% of these aggressive lesions showed high TBRmax and short TTPratio in PET. High TBRmax and short TTPratio in PET were significantly associated to poorer survivals (p ≤ 0.009). CONCLUSION: Dynamic [18 F]FDOPA PET provides a similar diagnostic accuracy as contrast enhancement in MRI to identify the aggressiveness of suspected glial lesions at diagnosis. Both methods, however, are complementary and [18 F]FDOPA PET may be a useful additional tool in equivocal cases.

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

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.

Semi-automated segmentation methods of SSTR PET for dosimetry prediction in refractory meningioma patients treated by SSTR-targeted peptide receptor radionuclide therapy.

OBJECTIVES: Tumor dosimetry with somatostatin receptor-targeted peptide receptor radionuclide therapy (SSTR-targeted PRRT) by 177Lu-DOTATATE may contribute to improved treatment monitoring of refractory meningioma. Accurate dosimetry requires reliable and reproducible pretherapeutic PET tumor segmentation which is not currently available. This study aims to propose semi-automated segmentation methods to determine metabolic tumor volume with pretherapeutic 68Ga-DOTATOC PET and evaluate SUVmean-derived values as predictive factors for tumor-absorbed dose. METHODS: Thirty-nine meningioma lesions from twenty patients were analyzed. The ground truth PET and SPECT volumes (VolGT-PET and VolGT-SPECT) were computed from manual segmentations by five experienced nuclear physicians. SUV-related indexes were extracted from VolGT-PET and the semi-automated PET volumes providing the best Dice index with VolGT-PET (Volopt) across several methods: SUV absolute-value (2.3)-threshold, adaptative methods (Jentzen, Otsu, Contrast-based method), advanced gradient-based technique, and multiple relative thresholds (% of tumor SUVmax, hypophysis SUVmean, and meninges SUVpeak) with optimal threshold optimized. Tumor-absorbed doses were obtained from the VolGT-SPECT, corrected for partial volume effect, performed on a 360° whole-body CZT-camera at 24, 96, and 168 h after administration of 177Lu-DOTATATE. RESULTS: Volopt was obtained from 1.7-fold meninges SUVpeak (Dice index 0.85 ± 0.07). SUVmean and total lesion uptake (SUVmeanxlesion volume) showed better correlations with tumor-absorbed doses than SUVmax when determined with the VolGT (respective Pearson correlation coefficients of 0.78, 0.67, and 0.56) or Volopt (0.64, 0.66, and 0.56). CONCLUSION: Accurate definition of pretherapeutic PET volumes is justified since SUVmean-derived values provide the best tumor-absorbed dose predictions in refractory meningioma patients treated by 177Lu-DOTATATE. This study provides a semi-automated segmentation method of pretherapeutic 68Ga-DOTATOC PET volumes to achieve good reproducibility between physicians. CLINICAL RELEVANCE STATEMENT: SUVmean-derived values from pretherapeutic 68Ga-DOTATOC PET are predictive of tumor-absorbed doses in refractory meningiomas treated by 177Lu-DOTATATE, justifying to accurately define pretherapeutic PET volumes. This study provides a semi-automated segmentation of 68Ga-DOTATOC PET images easily applicable in routine. KEY POINTS: • SUVmean-derived values from pretherapeutic 68Ga-DOTATOC PET images provide the best predictive factors of tumor-absorbed doses related to 177Lu-DOTATATE PRRT in refractory meningioma. • A 1.7-fold meninges SUVpeak segmentation method used to determine metabolic tumor volume on pretherapeutic 68Ga-DOTATOC PET images of refractory meningioma treated by 177Lu-DOTATATE is as efficient as the currently routine manual segmentation method and limits inter- and intra-observer variabilities. • This semi-automated method for segmentation of refractory meningioma is easily applicable to routine practice and transferrable across PET centers.

Differentiating high-grade glioma progression from treatment-related changes with dynamic [18F]FDOPA PET: a multicentric study.

OBJECTIVES: Diagnostic accuracy of amino-acid PET for distinguishing progression from treatment-related changes (TRC) is currently based on single-center non-homogeneous glioma populations. Our study assesses the diagnostic value of static and dynamic [18F]FDOPA PET acquisitions to differentiate between high-grade glioma (HGG) recurrence and TRC in a large cohort sourced from two independent nuclear medicine centers. METHODS: We retrospectively identified 106 patients with suspected glioma recurrences (WHO GIII, n = 38; GIV, n = 68; IDH-mutant, n = 35, IDH-wildtype, n = 71). Patients underwent dynamic [18F]FDOPA PET/CT (n = 83) or PET/MRI (n = 23), and static tumor-to-background ratios (TBRs), metabolic tumor volumes and dynamic parameters (time to peak and slope) were determined. The final diagnosis was either defined by histopathology or a clinical-radiological follow-up at 6 months. Optimal [18F]FDOPA PET parameter cut-offs were obtained by receiver operating characteristic analysis. Predictive factors and clinical parameters were assessed using univariate and multivariate Cox regression survival analyses. RESULTS: Surgery or the clinical-radiological 6-month follow-up identified 71 progressions and 35 treatment-related changes. TBRmean, with a threshold of 1.8, best-differentiated glioma recurrence/progression from post-treatment changes in the whole population (sensitivity 82%, specificity 71%, p < 0.0001) whereas curve slope was only significantly different in IDH-mutant HGGs (n = 25). In survival analyses, MTV was a clinical independent predictor of progression-free and overall survival on the multivariate analysis (p ≤ 0.01). A curve slope > -0.12/h was an independent predictor for longer PFS in IDH-mutant HGGs CONCLUSION: Our multicentric study confirms the high accuracy of [18F]FDOPA PET to differentiate recurrent malignant gliomas from TRC and emphasizes the diagnostic and prognostic value of dynamic acquisitions for IDH-mutant HGGs. KEY POINTS: • The diagnostic accuracy of dynamic amino-acid PET, for distinguishing progression from treatment-related changes, is currently based on single-center non-homogeneous glioma populations. • This multicentric study confirms the high accuracy of static [18F]FDOPA PET images for differentiating progression from treatment-related changes in a homogeneous population of high-grade gliomas and highlights the diagnostic and prognostic value of dynamic acquisitions for IDH-mutant high-grade gliomas. • Dynamic acquisitions should be performed in IDH-mutant glioma patients to provide valuable information for the differential diagnosis of recurrence and treatment-related changes.

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 < 0.001). Without the PSFd, four and eight radiomics features contributed to 50% of the model for detecting IDH-mutated and/or 1p/19q codeleted gliomas, respectively. Application of the PSFd reduced this to three and seven contributive radiomics features. Conclusion: Application of the PSFd to dynamic 18F-FDOPA PET imaging significantly improves the detection of molecular parameters in newly diagnosed gliomas, most notably by modifying TBR radiomics features.

Multi-tracer and multiparametric PET imaging to detect the IDH mutation in glioma: a preclinical translational in vitro, in vivo, and ex vivo study.

BACKGROUND: This translational study explores multi-tracer PET imaging for the non-invasive detection of the IDH1 mutation which is a positive prognostic factor in glioma. METHODS: U87 human high-grade glioma (HGG) isogenic cell lines with or without the IDH1 mutation (CRISP/Cas9 method) were stereotactically grafted into rat brains, and examined, in vitro, in vivo and ex vivo. PET imaging sessions, with radiotracers specific for glycolytic metabolism ([18F]FDG), amino acid metabolism ([18F]FDopa), and inflammation ([18F]DPA-714), were performed sequentially during 3-4 days. The in vitro radiotracer uptake was expressed as percent per million cells. For each radiotracer examined in vivo, static analyses included the maximal and mean tumor-to-background ratio (TBRmax and TBRmean) and metabolic tumor volume (MTV). Dynamic analyses included the distribution volume ratio (DVR) and the relative residence time (RRT) extracted from a reference Logan model. Ex vivo analyses consisted of immunological analyses. RESULTS: In vitro, IDH1+ cells (i.e. cells expressing the IDH1 mutation) showed lower levels of [18F]DPA-714 uptake compared to IDH1- cells (p < 0.01). These results were confirmed in vivo with lower [18F]DPA-714 uptake in IDH+ tumors (3.90 versus 5.52 for TBRmax, p = 0.03). Different values of [18F]DPA-714 and [18F] FDopa RRT (respectively 11.07 versus 22.33 and 2.69 versus - 1.81 for IDH+ and IDH- tumors, p < 0.02) were also observed between the two types of tumors. RRT [18F]DPA-714 provided the best diagnostic performance to discriminate between the two cell lines (AUC of 100%, p < 0.01). Immuno-histological analyses revealed lower expression of Iba-1 and TSPO antibodies in IDH1+ tumors. CONCLUSIONS: [18F]DPA-714 and [18F] FDopa both correlate with the presence of the IDH1 mutation in HGG. These radiotracers are therefore good candidates for translational studies investigating their clinical applications in patients.

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

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.

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.

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 < 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 < 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.

Dynamic 18F-FDopa PET Imaging for Newly Diagnosed Gliomas: Is a Semiquantitative Model Sufficient?

PURPOSE: Dynamic 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. METHODS: Thirty-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. RESULTS: The 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. CONCLUSIONS: The 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.

Photopenic Defects in Gliomas With Amino-Acid PET and Relative Prognostic Value: A Multicentric 11C-Methionine and 18F-FDOPA PET Experience.

The aim is to explore the concept of photopenic defects in newly diagnosed glioma patients with the 2 widely used C-MET and F-FDOPA PET amino acid tracers. Thirty-two C-MET and 26 F-FDOPA PET scans with amino acid PET-negative gliomas were selected in this European multicentric study. Of these gliomas, 16 C-MET and 10 F-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).

Use of static and dynamic [18F]-F-DOPA PET parameters for detecting patients with glioma recurrence or progression.

BACKGROUND: Static [18F]-F-DOPA PET images are currently used for identifying patients with glioma recurrence/progression after treatment, although the additional diagnostic value of dynamic parameters remains unknown in this setting. The aim of this study was to evaluate the performances of static and dynamic [18F]-F-DOPA PET parameters for detecting patients with glioma recurrence/progression as well as assess further relationships with patient outcome. METHODS: Fifty-one consecutive patients who underwent an [18F]-F-DOPA PET for a suspected glioma recurrence/progression at post-resection MRI, were retrospectively included. Static parameters, including mean and maximum tumor-to-normal-brain (TBR) ratios, tumor-to-striatum (TSR) ratios, and metabolic tumor volume (MTV), as well as dynamic parameters with time-to-peak (TTP) values and curve slope, were tested for predicting the following: (1) glioma recurrence/progression at 6 months after the PET exam and (2) survival on longer follow-up. RESULTS: All static parameters were significant predictors of glioma recurrence/progression (accuracy ≥ 94%) with all parameters also associated with mean progression-free survival (PFS) in the overall population (all p < 0.001, 29.7 vs. 0.4 months for TBRmax, TSRmax, and MTV). The curve slope was the sole dynamic PET predictor of glioma recurrence/progression (accuracy = 76.5%) and was also associated with mean PFS (p < 0.001, 18.0 vs. 0.4 months). However, no additional information was provided relative to static parameters in multivariate analysis. CONCLUSION: Although patients with glioma recurrence/progression can be detected by both static and dynamic [18F]-F-DOPA PET parameters, most of this diagnostic information can be achieved by conventional static parameters.

Integration of dynamic parameters in the analysis of 18F-FDopa PET imaging improves the prediction of molecular features of gliomas.

PURPOSE: 18F-FDopa PET imaging of gliomas is routinely interpreted with standardized uptake value (SUV)-derived indices. This study aimed to determine the added value of dynamic 18F-FDopa PET parameters for predicting the molecular features of newly diagnosed gliomas. METHODS: We retrospectively included 58 patients having undergone an 18F-FDopa PET for establishing the initial diagnosis of gliomas, whose molecular features were additionally characterized according to the WHO 2016 classification. Dynamic parameters, involving time-to-peak (TTP) values and curve slopes, were tested for the prediction of glioma types in addition to current static parameters, i.e., tumor-to-normal brain or tumor-to-striatum SUV ratios and metabolic tumor volume (MTV). RESULTS: There were 21 IDH mutant without 1p/19q co-deletion (IDH+/1p19q-) gliomas, 16 IDH mutants with 1p/19q co-deletion (IDH+/1p19q+) gliomas, and 21 IDH wildtype (IDH-) gliomas. Dynamic parameters enabled differentiating the gliomas according to these molecular features, whereas static parameters did not. In particular, a longer TTP was the single best independent predictor for identifying (1) IDH mutation status (area under the curve (AUC) of 0.789, global accuracy of 74% for the criterion of a TTP ≥ 5.4 min) and (2) 1p/19q co-deletion status (AUC of 0.679, global accuracy of 69% for the criterion of a TTP ≥ 6.9 min). Moreover, the TTP from IDH- gliomas was significantly shorter than those from both IDH+/1p19q- and IDH+/1p19q+ (p ≤ 0.007). CONCLUSION: Prediction of the molecular features of newly diagnosed gliomas with 18F-FDopa PET and especially of the presence or not of an IDH mutation, may be obtained with dynamic but not with current static uptake parameters.

Comparison of electromagnetic transmitter and ultrasound imaging for intrafraction monitoring of prostate radiotherapy.

BACKGROUND AND PURPOSE: To compare two in-beam monitoring devices for prostate radiotherapy: intra-prostatic electromagnetic transmitters (EM-T) (RayPilot®, Micropos Medical) and ultrasound imaging using transperineal probe (TP-US) (Clarity®, Elekta) used concomitantly on phantom and on patients. MATERIALS AND METHODS: The phantom study evaluated accuracy in presence of known translations and rotations. Then intra-fraction motions were analyzed for 10 prostate cancer patients implanted with the EM-T 8 days before the simulation CT (171 sessions). The percent time in which the differences between the systems were 1-5 mm were scored for each direction. RESULTS: Experiments on phantom confirmed no interference between the systems and showed deviations of less than 0.5 mm when translations were applied progressively. In presence of rotations (5-15°), both systems displayed systematic shifts up to 6.9 and 3.8 mm for the TP-US and the EM-T, respectively. Absolute mean differences between displacements observed on patients with EM-T and TP-US were ≤0.55 mm in all directions except for one patient (≤1.77 mm). With an exception for this patient, a strong correlation was found in left-right direction: differences >2 mm were monitored less than 0.22% of the time (mean acquisition time:164 minutes) and never exceeded 5 s. Maximum differences were observed in supero-inferior direction with differences >2 mm monitored more than 6.5% of the time for 3 patients. Large prostate rotations, the presence of gas and EM-T location in the prostate may explain important differences. CONCLUSION: Apart from the systematic shifts induced by the rotations, the two systems were correlated and represent feasible solutions for monitoring prostate cancer treatment.