A multimodality assessment of the protective capacity of statin therapy in a mouse model of radiation cardiotoxicity.

PURPOSE: Despite technological advances in radiotherapy (RT), cardiotoxicity remains a common complication in patients with lung, oesophageal and breast cancers. Statin therapy has been shown to have pleiotropic properties beyond its lipid-lowering effects. Previous murine models have shown statin therapy can reduce short-term functional effects of whole-heart irradiation. In this study, we assessed the efficacy of atorvastatin in protecting against the late effects of radiation exposure on systolic function, cardiac conduction, and atrial natriuretic peptide (ANP) following a clinically relevant partial-heart radiation exposure. MATERIALS AND METHODS: Female, 12-week old, C57BL/6j mice received an image-guided 16 Gy X-ray field to the base of the heart using a small animal radiotherapy research platform (SARRP), with or without atorvastatin from 1 week prior to irradiation until the end of the experiment. The animals were followed for 50 weeks with longitudinal transthoracic echocardiography (TTE) and electrocardiography (ECG) every 10 weeks, and plasma ANP every 20 weeks. RESULTS: At 30-50 weeks, mild left ventricular systolic function impairment observed in the RT control group was less apparent in animals receiving atorvastatin. ECG analysis demonstrated prolongation of components of cardiac conduction related to the heart base at 10 and 30 weeks in the RT control group but not in animals treated with atorvastatin. In contrast to systolic function, conduction disturbances resolved at later time-points with radiation alone. ANP reductions were lower in irradiated animals receiving atorvastatin at 30 and 50 weeks. CONCLUSIONS: Atorvastatin prevents left ventricular systolic dysfunction, and the perturbation of cardiac conduction following partial heart irradiation. If confirmed in clinical studies, these data would support the use of statin therapy for cardioprotection during thoracic radiotherapy.

Parametric Modeling and Deep Learning for Enhancing Pain Assessment in Postanesthesia.

OBJECTIVE: The problem of reliable and widely accepted measures of pain is still open. It follows the objective of this work as pain estimation through post-surgical trauma modeling and classification, to increase the needed reliability compared to measurements only. METHODS: This article proposes (i) a recursive identification method to obtain the frequency response and parameterization using fractional-order impedance models (FOIM), and (ii) deep learning with convolutional neural networks (CNN) classification algorithms using time-frequency data and spectrograms. The skin impedance measurements were conducted on 12 patients throughout the postanesthesia care in a proof-of-concept clinical trial. Recursive least-squares system identification was performed using a genetic algorithm for initializing the parametric model. The online parameter estimates were compared to the self-reported level by the Numeric Rating Scale (NRS) for analysis and validation of the results. Alternatively, the inputs to CNNs were the spectrograms extracted from the time-frequency dataset, being pre-labeled in four intensities classes of pain during offline and online training with the NRS. RESULTS: The tendency of nociception could be predicted by monitoring the changes in the FOIM parameters' values or by retraining online the network. Moreover, the tissue heterogeneity, assumed during nociception, could follow the NRS trends. The online predictions of retrained CNN have more specific trends to NRS than pain predicted by the offline population-trained CNN. CONCLUSION: We propose tailored online identification and deep learning for artefact corrupted environment. The results indicate estimations with the potential to avoid over-dosing due to the objectivity of the information. SIGNIFICANCE: Models and artificial intelligence (AI) allow objective and personalized nociception-antinociception prediction in the patient safety era for the design and evaluation of closed-loop analgesia controllers.

Assessment of Variabilities in Lung-Contouring Methods on CBCT Preclinical Radiomics Outputs.

Radiomics image analysis has the potential to uncover disease characteristics for the development of predictive signatures and personalised radiotherapy treatment. Inter-observer and inter-software delineation variabilities are known to have downstream effects on radiomics features, reducing the reliability of the analysis. The purpose of this study was to investigate the impact of these variabilities on radiomics outputs from preclinical cone-beam computed tomography (CBCT) scans. Inter-observer variabilities were assessed using manual and semi-automated contours of mouse lungs (n = 16). Inter-software variabilities were determined between two tools (3D Slicer and ITK-SNAP). The contours were compared using Dice similarity coefficient (DSC) scores and the 95th percentile of the Hausdorff distance (HD95p) metrics. The good reliability of the radiomics outputs was defined using intraclass correlation coefficients (ICC) and their 95% confidence intervals. The median DSC scores were high (0.82-0.94), and the HD95p metrics were within the submillimetre range for all comparisons. the shape and NGTDM features were impacted the most. Manual contours had the most reliable features (73%), followed by semi-automated (66%) and inter-software (51%) variabilities. From a total of 842 features, 314 robust features overlapped across all contouring methodologies. In addition, our results have a 70% overlap with features identified from clinical inter-observer studies.

Small field dosimetry for the small animal radiotherapy research platform (SARRP).

BACKGROUND: Preclinical radiation biology has become increasingly sophisticated due to the implementation of advanced small animal image guided radiation platforms into laboratory investigation. These small animal radiotherapy devices enable state-of-the-art image guided therapy (IGRT) research to be performed by combining high-resolution cone beam computed tomography (CBCT) imaging with an isocentric irradiation system. Such platforms are capable of replicating modern clinical systems similar to those that integrate a linear accelerator with on-board CBCT image guidance. METHODS: In this study, we present a dosimetric evaluation of the small animal radiotherapy research platform (SARRP, Xstrahl Inc.) focusing on small field dosimetry. Physical dosimetry was assessed using ion chamber for calibration and radiochromic film, investigating the impact of beam focus size on the dose rate output as well as beam characteristics (beam shape and penumbra). Two film analysis tools) have been used to assess the dose output using the 0.5 mm diameter aperture. RESULTS: Good agreement (between 1.7-3%) was found between the measured physical doses and the data provided by Xstrahl for all apertures used. Furthermore, all small field dosimetry data are in good agreement for both film reading methods and with our Monte Carlo simulations for both focal spot sizes. Furthermore, the small focal spot has been shown to produce a more homogenous beam with more stable penumbra over time. CONCLUSIONS: FilmQA Pro is a suitable tool for small field dosimetry, with a sufficiently small sampling area (0.1 mm) to ensure an accurate measurement. The electron beam focus should be chosen with care as this can potentially impact on beam stability and reproducibility.