Priming associations between bodily sensations and catastrophic misinterpretations: Specific for panic disorder?

Cognitive models assume that panic disorder is characterised by a tendency to misinterpret benign bodily symptoms (e.g. breathlessness) in a catastrophic fashion (e.g. suffocation). This is a central part of the cognitive model which presents a core focus for treatment. Several studies have supported this hypothesis. These studies have, however, almost always relied on self-report. In addition to susceptibility to biases (e.g. distortions of memory), a limitation of research based on verbal report is its inability to capture the spontaneous/automatic nature that is attributed to these catastrophic interpretations. The present paper reports on two experiments in which a priming procedure was used to test the hypothesis that panic disorder is characterised by spontaneous catastrophic interpretations and whether this effect is ‘specific’ to panic disorder. In line with predictions from the cognitive model, it was observed in the first experiment that the panic group demonstrated facilitated responses to trials consisting of a ‘symptom’ prime and a ‘catastrophic outcome’ target (e.g. breathlessness - suffocate). Similar effects were not observed for an anxious control group and a nonclinical control group, supporting the specificity of this effect. Interestingly, however, significant priming effects were observed for a group of mental health professionals (part of the healthy control group) who had no history of panic disorder. Subsequently, this unexpected observation was explicitly addressed in a second experiment, which confirmed the findings of Experiment 1. Together, these results suggest that associations between mental representations of benign bodily symptoms and catastrophic outcomes might develop as part of professional knowledge and experience, and should not necessarily be viewed as pathogenic. Theoretical and clinical implications are discussed

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.</p

The weight of cognitions in panic: The link between misinterpretations and panic attacks

In cognitive theory it is hypothesized that panic attacks are provoked by catastrophic misinterpretations of bodily sensations. The aim of the present study was to investigate the ability of associated word pairs referring to catastrophic thinking (e.g. palpitations-heart attack) in producing panic attacks. Patients with PD (n = 20),patients with mixed anxiety disorders (n = 20), and a healthy control group (n = 30) participated in the present study. To enhance ecological validity we first conducted a stimulus validation experiment. Subsequently, nine suitable panic and neutral word pairs were presented in block to the participants. Anxiety levels were assessed before and after the presentation. PD patients were more anxious when reading these word pairs, compared to neutral word pairs. However, none of the participants experienced a panic attack upon reading the word pairs. From the present results it seems that catastrophic thinking is rather related to the anticipatory anxiety for panic attacks, but not necessarily with the occurrence of the panic attacks themselves.status: publishe

Heritage and Identity

The paper focuses on the historical landscape structures and their contribution to local identity in a pluralistic society. The aim of this workshop was to explore the potentials of landscape biography for a contemporary and ‘inclusive’ landscape development. The historical town of Dachau, the Dachau Concentration Camp Memorial, the (former Olympic) Rowing Regatta Oberschleißheim, and the Schleissheim Castle with its park were the core sites of this working grou

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

[F-18]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 [F-18]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 ([F-18]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

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

Purpose To evaluate whether quantitative [F-18]FDG-PET/CT assessment, including radiomic analysis of [F-18]FDG-positive thyroid nodules, improved the preoperative differentiation of indeterminate thyroid nodules of non-Hurthle cell and Hurthle cell cytology.Methods Prospectively included patients with a Bethesda III or IV thyroid nodule underwent [F-18]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). [F-18]FDG-positive scans were included in radiomic analysis. After segmentation at 50% of SUVpeak, 107 radiomic features were extracted from [F-18]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 [F-18]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-Hurthle (n= 94), and Hurthle 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 [F-18]FDG-PET/CT was 95.8% (95% CI, 78.9-99.9%) in non-Hurthle cell nodules. In Hurthle 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%) [F-18]FDG-positive nodules showed a mean test set AUC of 0.445 (95% CI, 0.290-0.600) for the PET model.Conclusion Quantitative [F-18]FDG-PET/CT assessment ruled out malignancy in indeterminate thyroid nodules. Distinctive, higher SUV cut-offs should be applied in Hurthle cell nodules to optimize rule-out ability. Radiomic analysis did not contribute to the additional differentiation of [F-18]FDG-positive nodules

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

Purpose To evaluate whether quantitative [F-18]FDG-PET/CT assessment, including radiomic analysis of [F-18]FDG-positive thyroid nodules, improved the preoperative differentiation of indeterminate thyroid nodules of non-Hurthle cell and Hurthle cell cytology.Methods Prospectively included patients with a Bethesda III or IV thyroid nodule underwent [F-18]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). [F-18]FDG-positive scans were included in radiomic analysis. After segmentation at 50% of SUVpeak, 107 radiomic features were extracted from [F-18]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 [F-18]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-Hurthle (n= 94), and Hurthle 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 [F-18]FDG-PET/CT was 95.8% (95% CI, 78.9-99.9%) in non-Hurthle cell nodules. In Hurthle 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%) [F-18]FDG-positive nodules showed a mean test set AUC of 0.445 (95% CI, 0.290-0.600) for the PET model.Conclusion Quantitative [F-18]FDG-PET/CT assessment ruled out malignancy in indeterminate thyroid nodules. Distinctive, higher SUV cut-offs should be applied in Hurthle cell nodules to optimize rule-out ability. Radiomic analysis did not contribute to the additional differentiation of [F-18]FDG-positive nodules.Radiolog