A review of radiotherapy-induced late effects research after advanced technology treatments

The number of incident cancers and long-term cancer survivors is expected to increase substantially for at least a decade. Advanced technology radiotherapies, e.g., using beams of protons and photons, offer dosimetric advantages that theoretically yield better outcomes. In general, evidence from controlled clinical trials and epidemiology studies are lacking. To conduct these studies, new research methods and infrastructure will be needed. In the paper, we review several key research methods of relevance to late effects after advanced technology proton-beam and photon-beam radiotherapies. In particular, we focus on the determination of exposures to therapeutic and stray radiation and related uncertainties, with discussion of recent advances in exposure calculation methods, uncertainties, in silico studies, computing infrastructure, electronic medical records, and risk visualization. We identify six key areas of methodology and infrastructure that will be needed to conduct future outcome studies of radiation late effects

Proceedings Virtual Imaging Trials in Medicine 2024

This submission comprises the proceedings of the 1st Virtual Imaging Trials in Medicine conference, organized by Duke University on April 22-24, 2024. The listed authors serve as the program directors for this conference. The VITM conference is a pioneering summit uniting experts from academia, industry and government in the fields of medical imaging and therapy to explore the transformative potential of in silico virtual trials and digital twins in revolutionizing healthcare. The proceedings are categorized by the respective days of the conference: Monday presentations, Tuesday presentations, Wednesday presentations, followed by the abstracts for the posters presented on Monday and Tuesday

3rd EGEE User Forum

We have organized this book in a sequence of chapters, each chapter associated with an application or technical theme introduced by an overview of the contents, and a summary of the main conclusions coming from the Forum for the chapter topic. The first chapter gathers all the plenary session keynote addresses, and following this there is a sequence of chapters covering the application flavoured sessions. These are followed by chapters with the flavour of Computer Science and Grid Technology. The final chapter covers the important number of practical demonstrations and posters exhibited at the Forum. Much of the work presented has a direct link to specific areas of Science, and so we have created a Science Index, presented below. In addition, at the end of this book, we provide a complete list of the institutes and countries involved in the User Forum

Multi-Isotope Multi-Pinhole SPECT Bildgebung in kleinen Labortieren: Experimentelle Messungen und Monte Carlo Simulationen

Single photon emission computed tomography (SPECT) in small laboratory animals has become an integral part of translational medicine. It enables non-invasive validation of drug targeting, safety and efficacy in living organisms, which is progressively gaining importance in pharmaceutical industry. The increasing demand for efficiency in pharmaceutical research could be addressed by novel multitracer study designs. Multi-isotope multi-pinhole sampling allows validation of multiple tracers in a single experiment and consolidation of consecutive research trials. Due to physical and technical limitations, however, image quality and quantification can be substantially reduced. Advanced corrective procedures are required to establish multi-isotope multi-pinhole SPECT as a reliable and quantitative imaging technique for widespread use. For this purpose, the present work aimed to investigate the technical capabilities and physical limitations of multi-isotope multi-pinhole SPECT imaging in small laboratory animals. Based on experimental measurements and Monte Carlo simulations, specific error sources have been identified and procedures for quantitative image correction have been developed. A Monte Carlo simulation model of a state-of-the art SPECT/CT system has been established to provide a generalized framework for in-silico optimization of imaging hardware, acquisition protocols and reconstruction algorithms. The findings of this work can be used to improve image quality and quantification of SPECT in-vivo data for multi-isotope applications. They guide through the laborious process of multi-isotope protocol optimization and support the 3R welfare initiative that aims to replace, reduce and refine animal experimentation.Die Einzelphotonen-Emissionscomputertomographie (SPECT) in kleinen Labortieren hat sich als wichtiger Bestandteil der translationalen Medizin etabliert. Sie ermöglicht die nicht-invasive Validierung der Zielgenauigkeit, Wirksamkeit und Sicherheit von Wirkstoffen in lebenden Organismen und gewinnt zunehmend an Bedeutung in der pharmazeutischen Industrie. Die Forderung nach mehr Effizienz in der pharmazeutischen Forschung könnte durch neuartige Multitracer-Studien adressiert werden. Die Multi-Isotopen Akquisition mit Multi-Pinhole Kollimatoren ermöglicht die Validierung mehrerer Tracer in einem einzelnen Experiment und die Konsolidierung konsekutiver Bildgebungsstudien. Aufgrund physikalischer und technischer Limitationen ist die Bildqualität und Quantifizierbarkeit bei diesem Verfahren jedoch häufig reduziert. Um die Multi-Isotopen SPECT als zuverlässige und quantitative Bildgebungsmethode für den breiten Einsatz zu etablieren sind komplexe Korrekturverfahren erforderlich. Ziel der vorliegenden Arbeit war daher, die technischen Möglichkeiten und physikalischen Limitationen der Multi-Isotopen SPECT-Bildgebung in kleinen Labortieren systematisch zu untersuchen. Mithilfe von experimentellen Messungen und Monte Carlo Simulationen wurden spezifische Fehlerquellen identifiziert und Verfahren zur quantitativen Bildkorrektur entwickelt. Zudem wurde das Monte-Carlo Modell eines neuartigen SPECT/CT-Systems etabliert, um eine Plattform für die in-silico Optimierung von Bildgebungshardware, Aufnahmeprotokollen und Rekonstruktionsalgorithmen zu schaffen. Die Ergebnisse dieser Arbeit können die Bildqualität und Quantifizierbarkeit von SPECT in-vivo Daten für Multi-Isotopen Anwendungen verbessern. Sie führen beispielhaft durch den Prozess der Multi-Isotopen Protokolloptimierung und unterstützen die 3R-Initiative mit dem Ziel, experimentelle Tierversuche zu vermeiden (Replace), zu vermindern (Reduce) und zu verbessern (Refine)

AI in drug discovery and its clinical relevance

The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.  Other InformationPublished in:HeliyonLicense: https://creativecommons.org/licenses/by/4.0/See article on publisher's website: https://doi.org/10.1016/j.heliyon.2023.e17575 </p

Particularities of visualisation of medical and wellness data through a digital patient avatar

In this work particularities of visualisation of medical and wellness data through a digital patient avatar are given from a standpoint of a proposed approach, under which data for a visualisation may be obtained from a variety of sources through defined interfaces, while end-user interfaces of distinct complexity and level of immersion into the model may be exposed to different categories of users. A short introduction of important medical data exchange standards, specifications and models is offered. A brief overview of projects relevant to a subject of this work is given. The proposed approach is presented along with examples of use-cases