Prevalence and treatment implications of ICD-11 complex PTSD in Australian treatment-seeking current and ex-serving military members

Background: Despite growing support for the distinction between posttraumatic stress disorder (PTSD) and complex PTSD (CPTSD) as separate diagnoses within the ICD-11 psychiatric taxonomy, the prevalence and treatment implications of CPTSD among current and ex-serving military members have not been established. Objective: The study aims were to a) establish the prevalence of provisional ICD-11 CPTSD diagnosis relative to PTSD in an Australian sample of treatment-seeking current and ex-serving military members, and b) examine the implications of CPTSD diagnosis for intake profile and treatment response. Methods: The study analysed data collected routinely from Australian-accredited treatment programmes for military-related PTSD. Participants were 480 current and ex-serving military members in this programmes who received a provisional ICD-11 diagnosis of PTSD or CPTSD at intake using proxy measures. Measures of PTSD symptoms, disturbances in self-organisation, psychological distress, mental health and social relationships were considered at treatment intake, discharge, and 3-month follow-up. Results: Among participants with a provisional ICD-11 diagnosis, 78.2% were classified as having CPTSD, while 21.8% were classified as having PTSD. When compared to ICD-11 PTSD, participants with CPTSD reported greater symptom severity and psychological distress at intake, and lower scores on relationship and mental health dimensions of the quality of life measure. These relative differences persisted at each post-treatment assessment. Decreases in PTSD symptoms between intake and discharge were similar across PTSD (dRM = −0.81) and CPTSD (dRM = −0.76) groups, and there were no significant post-treatment differences between groups when controlling for initial scores. Conclusions: CPTSD is common among treatment-seeking current and ex-serving military members, and is associated with initially higher levels of psychiatric severity, which persist over time. Participants with CPTSD were equally responsive to PTSD treatment; however, the tendency for those with CPTSD to remain highly symptomatic post-treatment suggests additional treatment components should be considered

P-glycoprotein transporters and the gastrointestinal tract: Evaluation of the potential in vivo relevance of in vitro data employing talinolol as model compound

Among the different application routes peroral administration remains the one most widely used, Hence, mechanisms affecting p.o. bioavailability are of particular interest, also in drug development, In recent years, intestinal drug Secretion mediated by the multi-drug resistance gene product P-glycoprotein (PEP) has been discovered as a possible mechanism of low and erratic bioavailability, Due to the saturability of this process, a dose-dependent apparent oral clearance may be observed which decreases upon increasing dose, However, in vivo intestinal secretion might be revealed only in the lower or subtherapeutic dose range, in permeability studies with Caco-2 cell monolayers, the MDR-reversing agent verapamil inhibits secretion of P-glycoprotein substrates and, hence, increases apical-to-basolateral permeability. The aim of the rat studies with talinolol presented here was to test the relevance of the intestinal secretion process as well as the extent of inhibition by verapamil in ex vivo, in situ, and in vivo talinolol/verapamil drug-drug interaction studies. Intestinal secretion of talinolol was detected indirectly in ex vivo studies via transport inhibition with verapamil and directly in in situ intestinal perfusions in rats following a talinolol i.v. bolus. Both i.v, and p.o. verapamil appear to affect the concentration-time profiles of talinolol. Relevant observations with respect to drug absorption are the decreased apparent oral clearance upon verapamil coadministration as well as the decreased t(max) and mean absorption times at high verapamil doses. Talinolol may be regarded as a potential model compound for mechanistic studies on Pgp interactions, including permeability as well as binding studies and the involvement of transporters other than Pgp

Radiomics biopsy signature for predicting survival in patients with spinal bone metastases (SBMs)

STUDY DESIGN: Retrospective analysis of a registered cohort of patients treated and irradiated for metastases in the spinal column in a single institute. OBJECTIVE: This is the first study to develop and internally validate radiomics features for predicting six-month survival probability for patients with spinal bone metastases (SBM). BACKGROUND DATA: Extracted radiomics features from routine clinical CT images can be used to identify textural and intensity-based features unperceivable to human observers and associate them with a patient survival probability or disease progression. METHODS: A study was conducted on 250 patients treated for metastases in the spinal column irradiated for the first time between 2014 and 2016, at the MAASTRO clinic in Maastricht, the Netherlands. The first 150 available patients were used to develop the model and the subsequent 100 patient were considered as a test set for the model. A bootstrap (B = 400) stepwise model selection, which combines both the forward and backward variable elimination procedure, was used to select the most useful predictive features from the training data based on the Akaike information criterion (AIC). The stepwise selection procedure was applied to the 400 bootstrap samples, and the results were plotted as a histogram to visualize how often each variable was selected. Only variables selected more than 90 % of the time over the bootstrap runs were used to build the final model. A prognostic index (PI) called radiomics score (radscore) and clinical score (clinscore) was calculated for each patient. The prognostic index was not scaled, the original values were used which can be extracted from the model directly or calculated as a linear combination of the variables in the model multiplied by the respective beta value for each patient. RESULTS: The clinical model had a good discrimination power. The radiomics model, on the other hand, had an inferior performance with no added predictive power to the clinical model. The internal imaging characteristics do not seem to have a value in the prediction of survival. However, the Shape features were excluded from further analyses in our study since all biopsies had a standard shape hence no variability

Dual-energy CT for automatic organs-at-risk segmentation in brain-tumor patients using a multi-atlas and deep-learning approach

Abstract In radiotherapy, computed tomography (CT) datasets are mostly used for radiation treatment planning to achieve a high-conformal tumor coverage while optimally sparing healthy tissue surrounding the tumor, referred to as organs-at-risk (OARs). Based on CT scan and/or magnetic resonance images, OARs have to be manually delineated by clinicians, which is one of the most time-consuming tasks in the clinical workflow. Recent multi-atlas (MA) or deep-learning (DL) based methods aim to improve the clinical routine by an automatic segmentation of OARs on a CT dataset. However, so far no studies investigated the performance of these MA or DL methods on dual-energy CT (DECT) datasets, which have been shown to improve the image quality compared to conventional 120 kVp single-energy CT. In this study, the performance of an in-house developed MA and a DL method (two-step three-dimensional U-net) was quantitatively and qualitatively evaluated on various DECT-derived pseudo-monoenergetic CT datasets ranging from 40 keV to 170 keV. At lower energies, the MA method resulted in more accurate OAR segmentations. Both the qualitative and quantitative metric analysis showed that the DL approach often performed better than the MA method

Radiomics biopsy signature for predicting survival in patients with spinal bone metastases (SBMs)

Study design: Retrospective analysis of a registered cohort of patients treated and irradiated for metastases in the spinal column in a single institute. Objective: This is the first study to develop and internally validate radiomics features for predicting six-month survival probability for patients with spinal bone metastases (SBM). Background data: Extracted radiomics features from routine clinical CT images can be used to identify textural and intensity-based features unperceivable to human observers and associate them with a patient survival probability or disease progression. Methods: A study was conducted on 250 patients treated for metastases in the spinal column irradiated for the first time between 2014 and 2016, at the MAASTRO clinic in Maastricht, the Netherlands. The first 150 available patients were used to develop the model and the subsequent 100 patient were considered as a test set for the model. A bootstrap (B = 400) stepwise model selection, which combines both the forward and backward variable elimination procedure, was used to select the most useful predictive features from the training data based on the Akaike information criterion (AIC). The stepwise selection procedure was applied to the 400 bootstrap samples, and the results were plotted as a histogram to visualize how often each variable was selected. Only variables selected more than 90 % of the time over the bootstrap runs were used to build the final model.A prognostic index (PI) called radiomics score (radscore) and clinical score (clinscore) was calculated for each patient. The prognostic index was not scaled, the original values were used which can be extracted from the model directly or calculated as a linear combination of the variables in the model multiplied by the respective beta value for each patient. Results: The clinical model had a good discrimination power. The radiomics model, on the other hand, had an inferior performance with no added predictive power to the clinical model. The internal imaging characteristics do not seem to have a value in the prediction of survival. However, the Shape features were excluded from further analyses in our study since all biopsies had a standard shape hence no variability

A comparison study between single- and dual-energy CT density extraction methods for neurological proton monte carlo treatment planning

Monte Carlo proton dose calculations requires mass densities calculated from the patient CT image. This work investigates the impact of different single-energy CT (SECT) and dual-energy CT (DECT) to density conversion methods in proton dose distributions for brain tumours. Material and methods: Head CT scans for four patients were acquired in SECT and DECT acquisition modes. Commercial software was used to reconstruct DirectDensity((TM)) images in Relative Electron Densities (RED, ) and to obtain DECT-based pseudo-monoenergetic images (PMI). PMI and SECT images were converted to RED using piecewise linear interpolations calibrated on a head-sized phantom, these fits were referred to as "PMI2RED" and "CT2RED". Two DECT-based calibration methods ("Hunemohr-15it" and "Saito-15it") were also investigated. images were converted to mass-densities () to investigate differences and one representative patient case was used to make a proton treatment plan. Using CT2RED as reference method, dose distribution differences in the target and in five organs-at-risk (OARs) were quantified. Results: In the phantom study, Saito-15it and Hunemohr-15it produced the lowest root-mean-square error (0.7%) and DirectDensity((TM)) the highest error (2.7%). The proton plan evaluated in the Saito-15it and Hunemohr-15it datasets showed the largest relative differences compared to initial CT2RED plan down to -6% of the prescribed dose. Compared to CT2RED, average range differences were calculated: -0.1 +/- 0.3 mm for PMI2RED; -0.8 +/- 0.4 mm for Hunemohr-15it, and -1.2 +/- 0.4 mm for Saito-15it. Conclusion: Given the wide choice of available conversion methods, studies investigating the density accuracy for proton dose calculations are necessary. However, there is still a gap between performing accuracy studies in reference phantoms and applying these methods in human CT images. For this treatment case, the PMI2RED method was equivalent to the conventional CT2RED method in terms of dose distribution, CTV coverage and OAR sparing, whereas Hunemohr-15it and Saito-15it presented the largest differences