Virtual Reality Exposure to a Healthy Weight Body Is a Promising Adjunct Treatment for Anorexia Nervosa.

INTRODUCTION/OBJECTIVE: Treatment results of anorexia nervosa (AN) are modest, with fear of weight gain being a strong predictor of treatment outcome and relapse. Here, we present a virtual reality (VR) setup for exposure to healthy weight and evaluate its potential as an adjunct treatment for AN. METHODS: In two studies, we investigate VR experience and clinical effects of VR exposure to higher weight in 20 women with high weight concern or shape concern and in 20 women with AN. RESULTS: In study 1, 90% of participants (18/20) reported symptoms of high arousal but verbalized low to medium levels of fear. Study 2 demonstrated that VR exposure to healthy weight induced high arousal in patients with AN and yielded a trend that four sessions of exposure improved fear of weight gain. Explorative analyses revealed three clusters of individual reactions to exposure, which need further exploration. CONCLUSIONS: VR exposure is a well-accepted and powerful tool for evoking fear of weight gain in patients with AN. We observed a statistical trend that repeated virtual exposure to healthy weight improved fear of weight gain with large effect sizes. Further studies are needed to determine the mechanisms and differential effects.

Revisiting the Complexation of Cm(III) with Aqueous Phosphates: What Can We Learn from the Complex Structures Using Luminescence Spectroscopy and Ab Initio Simulations?

The coordination chemistry of Cm(III) with aqueous phosphates was investigated by means of laser-induced luminescence spectroscopy and ab initio simulations. For the first time, in addition to the presence of Cm(H2PO4)2+, the formation of Cm(H2PO4)2+ was unambiguously established from the luminescence spectroscopic data collected at various H+ concentrations (-log10 [H+] = 2.52, 3.44, and 3.65), ionic strengths (0.5-3.0 mol·L-1 NaClO4), and temperatures (25-90 °C). Complexation constants for both species were derived and extrapolated to standard conditions using the specific ion interaction theory. The molal enthalpy ΔRHm0 and molal entropy ΔRSm0 of both complexation reactions were derived using the integrated van't Hoff equation and indicated an endothermic and entropy-driven complexation. For the Cm(H2PO4)2+ complex, a more satisfactory description could be obtained when including the molal heat capacity term. While monodentate binding of the H2PO4- ligand(s) to the central curium ion was found to be the most stable configuration for both complexes in our ab initio simulations and luminescence lifetime analyses, a different temperature-dependent coordination to hydration water molecules could be deduced from the electronic structure of the Cm(III)-phosphate complexes. More precisely, where the Cm(H2PO4)2+ complex could be shown to retain an overall coordination number of 9 over the entire investigated temperature range, a coordination change from 9 to 8 was established for the Cm(H2PO4)2+ species with increasing temperature.

Analysis of Rutherford backscattering spectra with CNN-GRU mixture density network.

Ion Beam Analysis (IBA) utilizing MeV ion beams provides valuable insights into surface elemental composition across the entire periodic table. While ion beam measurements have advanced towards high throughput for mapping applications, data analysis has lagged behind due to the challenges posed by large volumes of data and multiple detectors providing diverse analytical information. Traditional physics-based fitting algorithms for these spectra can be time-consuming and prone to local minima traps, often taking days or weeks to complete. This study presents an approach employing a Mixture Density Network (MDN) to model the posterior distribution of Elemental Depth Profiles (EDP) from input spectra. Our MDN architecture includes an encoder module (EM), leveraging a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU), and a Mixture Density Head (MDH) employing a Multi-Layer Perceptron (MLP). Validation across three datasets with varying complexities demonstrates that for simple and intermediate cases, the MDN performs comparably to the conventional automatic fitting method (Autofit). However, for more complex datasets, Autofit still outperforms the MDN. Additionally, our integrated approach, combining MDN with the automatic fit method, significantly enhances accuracy while still reducing computational time, offering a promising avenue for improved analysis in IBA.

A deep-learning-based surrogate model for Monte-Carlo simulations of the linear energy transfer in primary brain tumor patients treated with proton-beam radiotherapy.

OBJECTIVE: This study explores the use of neural networks (NNs) as surrogate models for Monte-Carlo (MC) simulations in predicting the dose-averaged linear energy transfer (LETd) of protons in proton-beam therapy based on the planned dose distribution and patient anatomy in the form of computed tomography (CT) images. As LETdis associated with variability in the relative biological effectiveness (RBE) of protons, we also evaluate the implications of using NN predictions for normal tissue complication probability (NTCP) models within a variable-RBE context. Approach: The predictive performance of three-dimensional NN architectures was evaluated using five-fold cross-validation on a cohort of brain tumor patients (n=151). The best-performing model was identified and externally validated on patients from a different center (n=107). LETdpredictions were compared to MC-simulated results in clinically relevant regions of interest. We assessed the impact on NTCP models by leveraging LETdpredictions to derive RBE-weighted doses, using the Wedenberg RBE model. Main results: We found NNs based solely on the planned dose profile, i.e. without additional usage of CT images, can approximate MC-based LETddistributions. Root mean squared errors (RMSE) for the median LETdwithin the brain, brainstem, CTV, chiasm, lacrimal glands (ipsilateral/contralateral) and optic nerves (ipsilateral/contralateral) were 0.36, 0.87, 0.31, 0.73, 0.68, 1.04, 0.69 and 1.24~keV/µm, respectively. Although model predictions showed statistically significant differences from MC outputs, these did not result in substantial changes in NTCP predictions, with RMSEs of at most 3.2 percentage points. Significance: The ability of NNs to predict LETdbased solely on planned dose profiles suggests a viable alternative to the compute-intensive MC simulations in a variable-RBE setting. This is particularly useful in scenarios where MC simulation data are unavailable, facilitating resource-constrained proton therapy treatment planning, retrospective patient data analysis and further investigations on the variability of proton RBE.

Longitudinal and Multimodal Radiomics Models for Head and Neck Cancer Outcome Prediction.

Radiomics analysis provides a promising avenue towards the enabling of personalized radiotherapy. Most frequently, prognostic radiomics models are based on features extracted from medical images that are acquired before treatment. Here, we investigate whether combining data from multiple timepoints during treatment and from multiple imaging modalities can improve the predictive ability of radiomics models. We extracted radiomics features from computed tomography (CT) images acquired before treatment as well as two and three weeks after the start of radiochemotherapy for 55 patients with locally advanced head and neck squamous cell carcinoma (HNSCC). Additionally, we obtained features from FDG-PET images taken before treatment and three weeks after the start of therapy. Cox proportional hazards models were then built based on features of the different image modalities, treatment timepoints, and combinations thereof using two different feature selection methods in a five-fold cross-validation approach. Based on the cross-validation results, feature signatures were derived and their performance was independently validated. Discrimination regarding loco-regional control was assessed by the concordance index (C-index) and log-rank tests were performed to assess risk stratification. The best prognostic performance was obtained for timepoints during treatment for all modalities. Overall, CT was the best discriminating modality with an independent validation C-index of 0.78 for week two and weeks two and three combined. However, none of these models achieved statistically significant patient stratification. Models based on FDG-PET features from week three provided both satisfactory discrimination (C-index = 0.61 and 0.64) and statistically significant stratification (p=0.044 and p<0.001), but produced highly imbalanced risk groups. After independent validation on larger datasets, the value of (multimodal) radiomics models combining several imaging timepoints should be prospectively assessed for personalized treatment strategies.

Multitask Learning with Convolutional Neural Networks and Vision Transformers Can Improve Outcome Prediction for Head and Neck Cancer Patients.

Neural-network-based outcome predictions may enable further treatment personalization of patients with head and neck cancer. The development of neural networks can prove challenging when a limited number of cases is available. Therefore, we investigated whether multitask learning strategies, implemented through the simultaneous optimization of two distinct outcome objectives (multi-outcome) and combined with a tumor segmentation task, can lead to improved performance of convolutional neural networks (CNNs) and vision transformers (ViTs). Model training was conducted on two distinct multicenter datasets for the endpoints loco-regional control (LRC) and progression-free survival (PFS), respectively. The first dataset consisted of pre-treatment computed tomography (CT) imaging for 290 patients and the second dataset contained combined positron emission tomography (PET)/CT data of 224 patients. Discriminative performance was assessed by the concordance index (C-index). Risk stratification was evaluated using log-rank tests. Across both datasets, CNN and ViT model ensembles achieved similar results. Multitask approaches showed favorable performance in most investigations. Multi-outcome CNN models trained with segmentation loss were identified as the optimal strategy across cohorts. On the PET/CT dataset, an ensemble of multi-outcome CNNs trained with segmentation loss achieved the best discrimination (C-index: 0.29, 95% confidence interval (CI): 0.22-0.36) and successfully stratified patients into groups with low and high risk of disease progression (p=0.003). On the CT dataset, ensembles of multi-outcome CNNs and of single-outcome ViTs trained with segmentation loss performed best (C-index: 0.26 and 0.26, CI: 0.18-0.34 and 0.18-0.35, respectively), both with significant risk stratification for LRC in independent validation (p=0.002 and p=0.011). Further validation of the developed multitask-learning models is planned based on a prospective validation study, which has recently completed recruitment.

2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma.

For treatment individualisation of patients with locally advanced head and neck squamous cell carcinoma (HNSCC) treated with primary radiochemotherapy, we explored the capabilities of different deep learning approaches for predicting loco-regional tumour control (LRC) from treatment-planning computed tomography images. Based on multicentre cohorts for exploration (206 patients) and independent validation (85 patients), multiple deep learning strategies including training of 3D- and 2D-convolutional neural networks (CNN) from scratch, transfer learning and extraction of deep autoencoder features were assessed and compared to a clinical model. Analyses were based on Cox proportional hazards regression and model performances were assessed by the concordance index (C-index) and the model's ability to stratify patients based on predicted hazards of LRC. Among all models, an ensemble of 3D-CNNs achieved the best performance (C-index 0.31) with a significant association to LRC on the independent validation cohort. It performed better than the clinical model including the tumour volume (C-index 0.39). Significant differences in LRC were observed between patient groups at low or high risk of tumour recurrence as predicted by the model ([Formula: see text]). This 3D-CNN ensemble will be further evaluated in a currently ongoing prospective validation study once follow-up is complete.