Digital Twins' Advancements and Applications in Healthcare, Towards Precision Medicine.

This review examines the significant influence of Digital Twins (DTs) and their variant, Digital Human Twins (DHTs), on the healthcare field. DTs represent virtual replicas that encapsulate both medical and physiological characteristics-such as tissues, organs, and biokinetic data-of patients. These virtual models facilitate a deeper understanding of disease progression and enhance the customization and optimization of treatment plans by modeling complex interactions between genetic factors and environmental influences.

Investigation of Machine and Deep Learning Techniques to Detect HPV Status.

Background: This study investigated alternative, non-invasive methods for human papillomavirus (HPV) detection in head and neck cancers (HNCs). We compared two approaches: analyzing computed tomography (CT) scans with a Deep Learning (DL) model and using radiomic features extracted from CT images with machine learning (ML) models.

Methods: Fifty patients with histologically confirmed HNC were included.

A radiomic- and dosiomic-based machine learning regression model for pretreatment planning in Lu-DOTATATE therapy.

Background: Standardized patient-specific pretreatment dosimetry planning is mandatory in the modern era of nuclear molecular radiotherapy, which may eventually lead to improvements in the final therapeutic outcome. Only a comprehensive definition of a dosage therapeutic window encompassing the range of absorbed doses, that is, helpful without being detrimental can lead to therapy individualization and improved outcomes. As a result, setting absorbed dose safety limits for organs at risk (OARs) requires knowledge of the absorbed dose-effect relationship.

A framework for prediction of personalized pediatric nuclear medical dosimetry based on machine learning and Monte Carlo techniques.

A methodology is introduced for the development of an internal dosimetry prediction toolkit for nuclear medical pediatric applications. The proposed study exploits Artificial Intelligence techniques using Monte Carlo simulations as ground truth for accurate prediction of absorbed doses per organ prior to the imaging acquisition considering only personalized anatomical characteristics of any new pediatric patient.GATE Monte Carlo simulations were performed using a population of computational pediatric models to calculate the specific absorbed dose rates (SADRs) in several organs.

HPC+ in the medical field: Overview and current examples.

Background: To say data is revolutionising the medical sector would be a vast understatement. The amount of medical data available today is unprecedented and has the potential to enable to date unseen forms of healthcare. To process this huge amount of data, an equally huge amount of computing power is required, which cannot be provided by regular desktop computers.

The OpenGATE ecosystem for Monte Carlo simulation in medical physics.

This paper reviews the ecosystem of GATE, an open-source Monte Carlo toolkit for medical physics. Based on the shoulders of Geant4, the principal modules (geometry, physics, scorers) are described with brief descriptions of some key concepts (Volume, Actors, Digitizer). The main source code repositories are detailed together with the automated compilation and tests processes (Continuous Integration).

Standardization and Validation of Brachytherapy Seeds' Modelling Using GATE and GGEMS Monte Carlo Toolkits.

This study aims to validate GATE and GGEMS simulation toolkits for brachytherapy applications and to provide accurate models for six commercial brachytherapy seeds, which will be freely available for research purposes. The AAPM TG-43 guidelines were used for the validation of two Low Dose Rate (LDR), three High Dose Rate (HDR), and one Pulsed Dose Rate (PDR) brachytherapy seeds. Each seed was represented as a 3D model and then simulated in GATE to produce one single Phase-Space (PHSP) per seed.

A deep-learning-based prediction model for the biodistribution of Y microspheres in liver radioembolization.

Background: Radioembolization with Y microspheres is a treatment approach for liver cancer. Currently, employed dosimetric calculations exhibit low accuracy, lacking consideration of individual patient, and tissue characteristics.

Purpose: The purpose of the present study was to employ deep learning (DL) algorithms to differentiate patterns of pretreatment distribution of Tc-macroaggregated albumin on SPECT/CT and post-treatment distribution of Y microspheres on PET/CT and to accurately predict how the Y-microspheres will be distributed in the liver tissue by radioembolization therapy.

Advanced Monte Carlo simulations of emission tomography imaging systems with GATE.

Built on top of the Geant4 toolkit, GATE is collaboratively developed for more than 15 years to design Monte Carlo simulations of nuclear-based imaging systems. It is, in particular, used by researchers and industrials to design, optimize, understand and create innovative emission tomography systems. In this paper, we reviewed the recent developments that have been proposed to simulate modern detectors and provide a comprehensive report on imaging systems that have been simulated and evaluated in GATE.

Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization.

Over the last decade there has been an extensive evolution in the Artificial Intelligence (AI) field. Modern radiation oncology is based on the exploitation of advanced computational methods aiming to personalization and high diagnostic and therapeutic precision. The quantity of the available imaging data and the increased developments of Machine Learning (ML), particularly Deep Learning (DL), triggered the research on uncovering "hidden" biomarkers and quantitative features from anatomical and functional medical images.

IDDRRA: A novel platform, based on Geant4-DNA to quantify DNA damage by ionizing radiation.

Purpose: This study proposes a novel computational platform that we refer to as IDDRRA (DNA Damage Response to Ionizing RAdiation), which uses Monte Carlo (MC) simulations to score radiation induced DNA damage. MC simulations provide results of high accuracy on the interaction of radiation with matter while scoring the energy deposition based on state-of-the-art physics and chemistry models and probabilistic methods.

Methods: The IDDRRA software is based on the Geant4-DNA toolkit together with new tools that were developed for the purpose of this study, including a new algorithm that was developed in Python for the design of the DNA molecules.

Clinical Evaluation of a Three-Dimensional Internal Dosimetry Technique for Liver Radioembolization with Y Microspheres Using Dose Voxel Kernels.

The purpose of this study was to develop a rapid, reliable, and efficient tool for three-dimensional (3D) dosimetry treatment planning and post-treatment evaluation of liver radioembolization with Y microspheres, using tissue-specific dose voxel kernels (DVKs) that can be used in everyday clinical practice. Two tissue-specific DVKs for Y were calculated through Monte Carlo (MC) simulations. DVKs for the liver and lungs were generated, and the dose distribution was compared with direct MC simulations.

Deep learning networks on chronic liver disease assessment with fine-tuning of shear wave elastography image sequences.

Chronic liver disease (CLD) is currently one of the major causes of death worldwide. If not treated, it may lead to cirrhosis, hepatic carcinoma and death. Ultrasound (US) shear wave elastography (SWE) is a relatively new, popular, non-invasive technique among radiologists.

Conjugated Photosensitizers for Imaging and PDT in Cancer Research.

Early cancer detection and perfect understanding of the disease are imperative toward efficient treatments. It is straightforward that, for choosing a specific cancer treatment methodology, diagnostic agents undertake a critical role. Imaging is an extremely intriguing tool since it assumes a follow up to treatments to survey the accomplishment of the treatment and to recognize any conceivable repeating injuries.

Ionizing Radiation and Complex DNA Damage: Quantifying the Radiobiological Damage Using Monte Carlo Simulations.

Ionizing radiation is a common tool in medical procedures. Monte Carlo (MC) techniques are widely used when dosimetry is the matter of investigation. The scientific community has invested, over the last 20 years, a lot of effort into improving the knowledge of radiation biology.

Benchtop systems for in vivo molecular screening of labeled compounds, as a tool to speed up drug research.

Background: For every new drug, >10,000 candidate molecules are tested for ~15 years. This is the daily mission of thousands research teams worldwide. It is well proven that small animal imaging speeds up this work, increases accuracy and decreases costs.

Size-specific dose estimations for pediatric chest, abdomen/pelvis and head CT scans with the use of GATE.

Purpose: The purpose of this study is to create an organ dose database for pediatric individuals undergoing chest, abdomen/pelvis, and head computed tomography (CT) examinations, and to report the differences in absorbed organ doses, when anatomical differences exist for pediatric patients.

Methods: The GATE Monte Carlo (MC) toolkit was used to model the GE BrightSpeed Elite CT model. The simulated scanner model was validated with the standard Computed Tomography Dose Index (CTDI) head phantom.

Quantification of DNA double-strand breaks using Geant4-DNA.

Purpose: This study aims to standardize the simulation procedure in measuring DNA double-strand breaks (DSBs), by using advanced Monte Carlo toolkits, and newly introduced experimental methods for DNA DSB measurement.

Methods: For the experimental quantification of DNA DSB, an innovative DNA dosimeter was used to produce experimental data. GATE in combination with Geant4-DNA toolkit were exploited to simulate the experimental environment.

A personalized, Monte Carlo-based method for internal dosimetric evaluation of radiopharmaceuticals in children.

Purpose: Herein, we introduce a methodology for estimating the absorbed dose in organs at risk that is based on specified clinically derived radiopharmaceutical biodistributions and personalized anatomical characteristics.

Methods: To evaluate the proposed methodology, we used realistic Monte Carlo (MC) simulations and computational pediatric models to calculate a parameter called in this work the "specific absorbed dose rate" (SADR). The SADR is a unique quantitative metric in that it is specific to a particular organ.

TRIMAGE: A dedicated trimodality (PET/MR/EEG) imaging tool for schizophrenia.

Simultaneous PET/MR/EEG (Positron Emission Tomography - Magnetic Resonance - Electroencephalography), a new tool for the investigation of neuronal networks in the human brain, is presented here within the framework of the European Union Project TRIMAGE. The trimodal, cost-effective PET/MR/EEG imaging tool makes use of cutting edge technology both in PET and in MR fields. A novel type of magnet (1.

A prototype PET/SPECT/X-rays scanner dedicated for whole body small animal studies.

Objective: To present a prototype tri-modal imaging system, consisting of a single photon emission computed tomography (SPET), a positron emission tomography (PET), and a computed tomography (CT) subsystem, evaluated in planar mode.

Materials And Methods: The subsystems are mounted on a rotating gantry, so as to be able to allow tomographic imaging in the future. The system, designed and constructed by our group, allows whole body mouse imaging of competent performance and is currently, to the best of our knowledge, unequaled in a national and regional level.

Dosimetry applications in GATE Monte Carlo toolkit.

Purpose: Monte Carlo (MC) simulations are a well-established method for studying physical processes in medical physics. The purpose of this review is to present GATE dosimetry applications on diagnostic and therapeutic simulated protocols. There is a significant need for accurate quantification of the absorbed dose in several specific applications such as preclinical and pediatric studies.

A preclinical simulated dataset of S-values and investigation of the impact of rescaled organ masses using the MOBY phantom.

Nuclear medicine and radiation therapy, although well established, are still rapidly evolving, by exploiting animal models, aiming to define precise dosimetry in molecular imaging protocols. The purpose of the present study was to create a dataset based on the MOBY phantom for the calculation of organ-to-organ S-values of commonly used radionuclides. S-values of most crucial organs were calculated using specific biodistributions with a whole-body heterogeneous source.