Challenges in Pharmacokinetic Modelling of [18F]fluoro-PEG-folate PET/CT Imaging in Epithelial Ovarian Cancer Patients.

PURPOSE: To describe the pharmacokinetic properties of the [18F]fluoro-polyethylene glycol(PEG)-folate radiotracer in PET/CT imaging of patients with advanced stage epithelial ovarian cancer (EOC). PROCEDURES: In five patients with advanced EOC (FIGO stage IIIB/IIIC, Fédération Internationale de Gynécologie et d'Obstétrique), a 90-min dynamic PET acquisition of the pelvis was performed directly after i.v. administration of 185 MBq [18F]fluoro-PEG6-folate. Arterial blood samples collected at nineteen timepoints were used to determine the plasma input function. A static volume of interest (VOI) for included tumor lesions was drawn manually on the PET images. Modelling was performed using PMOD software. Three different models (a 1-tissue compartment model (1T2k) and two 2-tissue compartment models, irreversible (2T3k) and reversible (2T4k)) were compared in goodness of fit with the time activity curves by means of the Akaike information criterion. RESULTS: The pharmacokinetic analysis in the pelvic area has proven to be much more challenging than expected. Only four out of 22 tumor lesions in five patients were considered suitable to perform modelling on. The remaining tumor lesions were inapt due to either low tracer uptake, small size, proximity to other [18F]fluoro-PEG6-folate -avid structures and/or displacement by abdominal organ motion in the dynamic scan. Data from the four analyzed tumor lesions suggest that the irreversible 2T3k may best describe the pharmacokinetics. All 22 lesions were immunohistochemically stained positive for the folate receptor alpha (FRα) after resection. CONCLUSION: Performing pharmacokinetic analysis in the abdominal pelvic region is very challenging. This brief article describes the challenges and pitfalls in pharmacokinetic analysis of a tracer with high physiological accumulation in the intestines, in case of lesions of limited size in the abdominal pelvic area.

Prognostic Value of [18F]FDG PET Radiomics to Detect Peritoneal and Distant Metastases in Locally Advanced Gastric Cancer-A Side Study of the Prospective Multicentre PLASTIC Study.

AIM: To improve identification of peritoneal and distant metastases in locally advanced gastric cancer using [18F]FDG-PET radiomics. METHODS: [18F]FDG-PET scans of 206 patients acquired in 16 different Dutch hospitals in the prospective multicentre PLASTIC-study were analysed. Tumours were delineated and 105 radiomic features were extracted. Three classification models were developed to identify peritoneal and distant metastases (incidence: 21%): a model with clinical variables, a model with radiomic features, and a clinicoradiomic model, combining clinical variables and radiomic features. A least absolute shrinkage and selection operator (LASSO) regression classifier was trained and evaluated in a 100-times repeated random split, stratified for the presence of peritoneal and distant metastases. To exclude features with high mutual correlations, redundancy filtering of the Pearson correlation matrix was performed (r = 0.9). Model performances were expressed by the area under the receiver operating characteristic curve (AUC). In addition, subgroup analyses based on Lauren classification were performed. RESULTS: None of the models could identify metastases with low AUCs of 0.59, 0.51, and 0.56, for the clinical, radiomic, and clinicoradiomic model, respectively. Subgroup analysis of intestinal and mixed-type tumours resulted in low AUCs of 0.67 and 0.60 for the clinical and radiomic models, and a moderate AUC of 0.71 in the clinicoradiomic model. Subgroup analysis of diffuse-type tumours did not improve the classification performance. CONCLUSION: Overall, [18F]FDG-PET-based radiomics did not contribute to the preoperative identification of peritoneal and distant metastases in patients with locally advanced gastric carcinoma. In intestinal and mixed-type tumours, the classification performance of the clinical model slightly improved with the addition of radiomic features, but this slight improvement does not outweigh the laborious radiomic analysis.

Development and External Validation of a PET Radiomic Model for Prognostication of Head and Neck Cancer.

AIM: To build and externally validate an [18F]FDG PET radiomic model to predict overall survival in patients with head and neck squamous cell carcinoma (HNSCC). METHODS: Two multicentre datasets of patients with operable HNSCC treated with preoperative afatinib who underwent a baseline and evaluation [18F]FDG PET/CT scan were included (EORTC: n = 20, Unicancer: n = 34). Tumours were delineated, and radiomic features were extracted. Each cohort served once as a training and once as an external validation set for the prediction of overall survival. Supervised feature selection was performed using variable hunting with variable importance, selecting the top two features. A Cox proportional hazards regression model using selected radiomic features and clinical characteristics was fitted on the training dataset and validated in the external validation set. Model performances are expressed by the concordance index (C-index). RESULTS: In both models, the radiomic model surpassed the clinical model with validation C-indices of 0.69 and 0.79 vs. 0.60 and 0.67, respectively. The model that combined the radiomic features and clinical variables performed best, with validation C-indices of 0.71 and 0.82. CONCLUSION: Although assessed in two small but independent cohorts, an [18F]FDG-PET radiomic signature based on the evaluation scan seems promising for the prediction of overall survival for HNSSC treated with preoperative afatinib. The robustness and clinical applicability of this radiomic signature should be assessed in a larger cohort.

Baseline and early digital [ 18 F]FDG PET/CT and multiparametric MRI contain promising features to predict response to neoadjuvant therapy in locally advanced rectal cancer patients: a pilot study.

OBJECTIVE: In this pilot study, we investigated the feasibility of response prediction using digital [ 18 F]FDG PET/computed tomography (CT) and multiparametric MRI before, during, and after neoadjuvant chemoradiation therapy in locally advanced rectal cancer (LARC) patients and aimed to select the most promising imaging modalities and timepoints for further investigation in a larger trial. METHODS: Rectal cancer patients scheduled to undergo neoadjuvant chemoradiation therapy were prospectively included in this trial, and underwent multiparametric MRI and [ 18 F]FDG PET/CT before, 2 weeks into, and 6-8 weeks after chemoradiation therapy. Two groups were created based on pathological tumor regression grade, that is, good responders (TRG1-2) and poor responders (TRG3-5). Using binary logistic regression analysis with a cutoff value of P  ≤ 0.2, promising predictive features for response were selected. RESULTS: Nineteen patients were included. Of these, 5 were good responders, and 14 were poor responders. Patient characteristics of these groups were similar at baseline. Fifty-seven features were extracted, of which 13 were found to be promising predictors of response. Baseline [T2: volume, diffusion-weighted imaging (DWI): apparent diffusion coefficient (ADC) mean, DWI: difference entropy], early response (T2: volume change, DWI: ADC mean change) and end-of-treatment presurgical evaluation MRI (T2: gray level nonuniformity, DWI: inverse difference normalized, DWI: gray level nonuniformity normalized), as well as baseline (metabolic tumor volume, total lesion glycolysis) and early response PET/CT (Δ maximum standardized uptake value, Δ peak standardized uptake value corrected for lean body mass), were promising features. CONCLUSION: Both multiparametric MRI and [ 18 F]FDG PET/CT contain promising imaging features to predict response to neoadjuvant chemoradiotherapy in LARC patients. A future larger trial should investigate baseline, early response, and end-of-treatment presurgical evaluation MRI and baseline and early response PET/CT.

Prostate-Specific Membrane Antigen Targeted Pet/CT Imaging in Patients with Colon, Gastric and Pancreatic Cancer.

Current imaging modalities frequently misjudge disease stage in colorectal, gastric and pancreatic cancer. As treatment decisions are dependent on disease stage, incorrect staging has serious consequences. Previous preclinical research and case reports indicate that prostate-specific membrane antigen (PSMA)-targeted PET/CT imaging might provide a solution to some of these challenges. This prospective clinical study aims to assess the feasibility of [18F]DCFPyL PET/CT imaging to target and visualize primary colon, gastric and pancreatic cancer. In this prospective clinical trial, patients with colon, gastric and pancreatic cancer were included and underwent both [18F]DCFPyL and [18F]FDG PET/CT scans prior to surgical resection or (for gastric cancer) neoadjuvant therapy. Semiquantitative analysis of immunohistochemical PSMA staining was performed on the surgical resection specimens, and the results were correlated to imaging parameters. The results of this study demonstrate detection of the primary tumor by [18F]DCFPyL PET/CT in 7 out of 10 patients with colon, gastric and pancreatic cancer, with a mean tumor-to-blood pool ratio (TBR) of 3.3 and mean SUVmax of 3.6. However, due to the high surrounding uptake, visual distinction of these tumors was difficult, and the SUVmax and TBR on [18F]FDG PET/CT were significantly higher than on [18F]DCFPyL PET/CT. In addition, no correlation between PSMA expression in the resection specimen and SUVmax on [18F]DCFPyL PET/CT was found. In conclusion, the detection of several gastrointestinal cancers using [18F]DCFPyL PET/CT is feasible. However, low tumor expression and high uptake physiologically in organs/background hamper the clear distinction of the tumor. As a result, [18F]FDG PET/CT was superior in detecting colon, gastric and pancreatic cancers.

[18F]FDG-PET/CT radiomics for the identification of genetic clusters in pheochromocytomas and paragangliomas.

OBJECTIVES: Based on germline and somatic mutation profiles, pheochromocytomas and paragangliomas (PPGLs) can be classified into different clusters. We investigated the use of [18F]FDG-PET/CT radiomics, SUVmax and biochemical profile for the identification of the genetic clusters of PPGLs. METHODS: In this single-centre cohort, 40 PPGLs (13 cluster 1, 18 cluster 2, 9 sporadic) were delineated using a 41% adaptive threshold of SUVpeak ([18F]FDG-PET) and manually (low-dose CT; ldCT). Using PyRadiomics, 211 radiomic features were extracted. Stratified 5-fold cross-validation for the identification of the genetic cluster was performed using multinomial logistic regression with dimensionality reduction incorporated per fold. Classification performances of biochemistry, SUVmax and PET(/CT) radiomic models were compared and presented as mean (multiclass) test AUCs over the five folds. Results were validated using a sham experiment, randomly shuffling the outcome labels. RESULTS: The model with biochemistry only could identify the genetic cluster (multiclass AUC 0.60). The three-factor PET model had the best classification performance (multiclass AUC 0.88). A simplified model with only SUVmax performed almost similarly. Addition of ldCT features and biochemistry decreased the classification performances. All sham AUCs were approximately 0.50. CONCLUSION: PET radiomics achieves a better identification of PPGLs compared to biochemistry, SUVmax, ldCT radiomics and combined approaches, especially for the differentiation of sporadic PPGLs. Nevertheless, a model with SUVmax alone might be preferred clinically, weighing model performances against laborious radiomic analysis. The limited added value of radiomics to the overall classification performance for PPGL should be validated in a larger external cohort. KEY POINTS: • Radiomics derived from [18F]FDG-PET/CT has the potential to improve the identification of the genetic clusters of pheochromocytomas and paragangliomas. • A simplified model with SUVmax only might be preferred clinically, weighing model performances against the laborious radiomic analysis. • Cluster 1 and 2 PPGLs generally present distinctive characteristics that can be captured using [18F]FDG-PET imaging. Sporadic PPGLs appear more heterogeneous, frequently resembling cluster 2 PPGLs and occasionally resembling cluster 1 PPGLs.

Quantitative classification and radiomics of [18F]FDG-PET/CT in indeterminate thyroid nodules.

PURPOSE: To evaluate whether quantitative [18F]FDG-PET/CT assessment, including radiomic analysis of [18F]FDG-positive thyroid nodules, improved the preoperative differentiation of indeterminate thyroid nodules of non-Hürthle cell and Hürthle cell cytology. METHODS: Prospectively included patients with a Bethesda III or IV thyroid nodule underwent [18F]FDG-PET/CT imaging. Receiver operating characteristic (ROC) curve analysis was performed for standardised uptake values (SUV) and SUV-ratios, including assessment of SUV cut-offs at which a malignant/borderline neoplasm was reliably ruled out (≥ 95% sensitivity). [18F]FDG-positive scans were included in radiomic analysis. After segmentation at 50% of SUVpeak, 107 radiomic features were extracted from [18F]FDG-PET and low-dose CT images. Elastic net regression classifiers were trained in a 20-times repeated random split. Dimensionality reduction was incorporated into the splits. Predictive performance of radiomics was presented as mean area under the ROC curve (AUC) across the test sets. RESULTS: Of 123 included patients, 84 (68%) index nodules were visually [18F]FDG-positive. The malignant/borderline rate was 27% (33/123). SUV-metrices showed AUCs ranging from 0.705 (95% CI, 0.601-0.810) to 0.729 (0.633-0.824), 0.708 (0.580-0.835) to 0.757 (0.650-0.864), and 0.533 (0.320-0.747) to 0.700 (0.502-0.898) in all (n = 123), non-Hürthle (n = 94), and Hürthle cell (n = 29) nodules, respectively. At SUVmax, SUVpeak, SUVmax-ratio, and SUVpeak-ratio cut-offs of 2.1 g/mL, 1.6 g/mL, 1.2, and 0.9, respectively, sensitivity of [18F]FDG-PET/CT was 95.8% (95% CI, 78.9-99.9%) in non-Hürthle cell nodules. In Hürthle cell nodules, cut-offs of 5.2 g/mL, 4.7 g/mL, 3.4, and 2.8, respectively, resulted in 100% sensitivity (95% CI, 66.4-100%). Radiomic analysis of 84 (68%) [18F]FDG-positive nodules showed a mean test set AUC of 0.445 (95% CI, 0.290-0.600) for the PET model. CONCLUSION: Quantitative [18F]FDG-PET/CT assessment ruled out malignancy in indeterminate thyroid nodules. Distinctive, higher SUV cut-offs should be applied in Hürthle cell nodules to optimize rule-out ability. Radiomic analysis did not contribute to the additional differentiation of [18F]FDG-positive nodules. TRIAL REGISTRATION NUMBER: This trial is registered with ClinicalTrials.gov: NCT02208544 (5 August 2014), https://clinicaltrials.gov/ct2/show/NCT02208544 .

The Influence of the Exclusion of Central Necrosis on [18F]FDG PET Radiomic Analysis.

BACKGROUND: Central necrosis can be detected on [18F]FDG PET/CT as a region with little to no tracer uptake. Currently, there is no consensus regarding the inclusion of regions of central necrosis during volume of interest (VOI) delineation for radiomic analysis. The aim of this study was to assess how central necrosis affects radiomic analysis in PET. METHODS: Forty-three patients, either with non-small cell lung carcinomas (NSCLC, n = 12) or with pheochromocytomas or paragangliomas (PPGL, n = 31), were included retrospectively. VOIs were delineated with and without central necrosis. From all VOIs, 105 radiomic features were extracted. Differences in radiomic features between delineation methods were assessed using a paired t-test with Benjamini-Hochberg multiple testing correction. In the PPGL cohort, performances of the radiomic models to predict the noradrenergic biochemical profile were assessed by comparing the areas under the receiver operating characteristic curve (AUC) for both delineation methods. RESULTS: At least 65% of the features showed significant differences between VOIvital-tumour and VOIgross-tumour (65%, 79% and 82% for the NSCLC, PPGL and combined cohort, respectively). The AUCs of the radiomic models were not significantly different between delineation methods. CONCLUSION: In both tumour types, almost two-third of the features were affected, demonstrating that the impact of whether or not to include central necrosis in the VOI on the radiomic feature values is significant. Nevertheless, predictive performances of both delineation methods were comparable. We recommend that radiomic studies should report whether or not central necrosis was included during delineation.

Adding the temporal domain to PET radiomic features.

BACKGROUND: Radiomic features, extracted from positron emission tomography, aim to characterize tumour biology based on tracer intensity, tumour geometry and/or tracer uptake heterogeneity. Currently, radiomic features are derived from static images. However, temporal changes in tracer uptake might reveal new aspects of tumour biology. This study aims to explore additional information of these novel dynamic radiomic features compared to those derived from static or metabolic rate images. METHODS: Thirty-five patients with non-small cell lung carcinoma underwent dynamic [18F]FDG PET/CT scans. Spatial intensity, shape and texture radiomic features were derived from volumes of interest delineated on static PET and parametric metabolic rate PET. Dynamic grey level cooccurrence matrix (GLCM) and grey level run length matrix (GLRLM) features, assessing the temporal domain unidirectionally, were calculated on eight and sixteen time frames of equal length. Spearman's rank correlations of parametric and dynamic features with static features were calculated to identify features with potential additional information. Survival analysis was performed for the non-redundant temporal features and a selection of static features using Kaplan-Meier analysis. RESULTS: Three out of 90 parametric features showed moderate correlations with corresponding static features (ρ≥0.61), all other features showed high correlations (ρ>0.7). Dynamic features are robust independent of frame duration. Five out of 22 dynamic GLCM features showed a negligible to moderate correlation with any static feature, suggesting additional information. All sixteen dynamic GLRLM features showed high correlations with static features, implying redundancy. Log-rank analyses of Kaplan-Meier survival curves for all features dichotomised at the median were insignificant. CONCLUSION: This study suggests that, compared to static features, some dynamic GLCM radiomic features show different information, whereas parametric features provide minimal additional information. Future studies should be conducted in larger populations to assess whether there is a clinical benefit of radiomics using the temporal domain over traditional radiomics.

Nuclear medicine radiomics in precision medicine: why we can't do without artificial intelligence.

In recent years, radiomics, defined as the extraction of large amounts of quantitative features from medical images, has gained emerging interest. Radiomics consists of the extraction of handcrafted features combined with sophisticated statistical methods or machine learning algorithms for modelling, or deep learning algorithms that both learn features from raw data and perform modelling. These features have the potential to serve as non-invasive biomarkers for tumor characterization, prognostic stratification and response prediction, thereby contributing to precision medicine. However, especially in nuclear medicine, variable results are obtained when using radiomics for these purposes. Individual studies show promising results, but due to small numbers of patients per study and little standardization, it is difficult to compare and validate results on other datasets. This review describes the radiomic pipeline, its applications and the increasing role of artificial intelligence within the field. Furthermore, the challenges that need to be overcome to achieve clinical translation are discussed, so that, eventually, radiomics, combined with clinical data and other biomarkers, can contribute to precision medicine, by providing the right treatment to the right patient, with the right dose, at the right time.