BACKGROUND AND OBJECTIVE:This study investigates how adaptive individual behavior influences epidemic dynamics in spatially structured populations. Specifically, it examines how payoff-driven strategy evolution interacts with spatial SEIRS dynamics to shape epidemic outcomes. METHODS:We construct a simulation framework that integrates a spatial SEIRS epidemic model with evolutionary game dynamics based on a Hawk-Dove formulation of behavioral strategies, including vaccination and freeriding. Strategies evolve through payoff-driven adaptation driven by local interactions, while disease transmission follows spatially localized SEIRS dynamics on a two-dimensional lattice. All core scenarios were repeated N=15 times with independent random seeds; results are reported as mean ± standard deviation (SD) with 95% confidence intervals. RESULTS:Behavioral adaptation and spatial structure strongly affect epidemic outcomes. Under high infection penalty (λ=3), epidemics in populations without freeriders were rapidly suppressed (7.0±8.2 cumulative deaths; 95% CI: 2.9-11.1). Introducing 1000 permanent freeriders (62.5% of the population) raised mortality to 187.1±75.0 deaths (95% CI: 149.2-225.1) and extended epidemic duration to 376.4±58.1 generations. Under low infection penalty (λ=1), mortality reached 187.3±25.0 deaths even without freeriders, confirming that perceived infection severity is a key driver of vaccination uptake. Two-dimensional sensitivity sweeps identify a critical freerider cluster size of approximately 300-500 agents (corresponding to 15.6-31.3% of the 1600-agent population used in the main analysis) and a sharp vaccination-cost threshold near Cvac≈0.2-0.5, beyond which epidemic duration approaches the full simulation window regardless of transmission rate. CONCLUSIONS:The framework reveals key feedbacks between individual decision-making and epidemic spread, highlighting the limitations of voluntary vaccination in the presence of permanent non-compliant agents and low perceived infection risk.

Immune checkpoint inhibitors can trigger antitumor immunity, yet most patients, including those with head and neck cancer, show limited benefit because of insufficient immune activation at the tumor site. Oncolytic virotherapy (OV) may overcome this by priming tumors at the injection site for a stronger response to immune checkpoint inhibitors. However, there are limited data on how OV influences systemic immune responses in uninjected tumors and secondary lymphoid organs (spleen, lymph nodes), largely because of the difficulty of sampling these tissues. We investigated whether immuno-PET can noninvasively track systemic and intratumoral changes in programmed death ligand 1 (PD-L1) expression after intratumoral OV administration in a syngeneic mouse model of oral carcinoma. Methods: 89Zr-DFO-PD-L1mAb was injected into murine oral cancer (MOC) models, including MOC1, MOC2, and MOC2(PD-L1) tumor-bearing mice. Whole-body PET/CT static scans were performed 48 h after injection. On the basis of in vitro OV sensitivity, the MOC1 model was selected to study the effects of OV on PD-L1 expression after a single intratumoral dose of RP1, a proprietary strain of oncolytic herpes simplex virus. Immuno-PET scans with concomitant biodistribution studies were performed on days 3 and 7 after OV administration. Radiomics features were extracted from immuno-PET scans, and immunohistochemistry was performed to confirm PD-L1 expression levels. Intratumoral cytokines and CD8 T-cell infiltration were also assessed. Results: RP1 injection significantly increased 89Zr-DFO-PD-L1mAb uptake in spleens and tumor-draining lymph nodes, as observed in the immuno-PET images acquired 3 d after treatment. By day 7, uptake levels in these organs returned to pretreatment levels. In contrast, no transient increase in radioconjugate uptake was observed in tumors treated with RP1 compared with controls. The levels of intratumoral and intrasplenic 89Zr-DFO-PD-L1mAb uptake were consistent with PD-L1 immunohistochemistry performed on representative sections. Radiomics analysis of tumors and spleens revealed several features occurring on day 3 after administration of OV. Elevated levels of intratumoral type I interferon, followed by CD8 T-cell infiltration, indicated local immune stimulation. Conclusion: The present work highlights the potential of anti-PD-L1 immuno-PET to noninvasively assess spatiotemporal changes in PD-L1 expression on a whole-body scale. This method can help guide and optimize OV, a task that would be challenging to achieve with a single biopsy alone.

BACKGROUND AND OBJECTIVE:Ultraviolet-induced fluorescence dermatoscopy (UVFD) is a novel technique utilizing ultraviolet light to produce visible fluorescence. Many skin conditions reveal specific features during UVFD, guiding early detection and diagnosis. Although there are many methods for computer processing of dermatoscopic images, there are no methods targeting UVFD images. This work identifies newly encountered challenges and proposes a preprocessing pipeline for preparing obtained data for further statistical analysis. METHODS:The study discusses the challenges and the applicability of methods developed for regular dermatoscopic images. Finally, the universal pipeline for preprocessing is presented. The study used a collection of 2027 background images, manually selected by an expert from hairless regions, which were later used for hair generation. Additionally, 120 images were manually annotated for dark and light hair, resulting in a set of ground truth masks. The study evaluated a number of algorithmic methods and a deep neural networks: UNet, ChimeraNet and TransUNet. RESULTS:The results clearly showed the superiority of deep learning over algorithmic methods in the task of hair segmentation in UVFD images. For dark hair segmentation, the UNet and TransUNet reported Dice scores ranging 0.95-0.98 on synthetic data. On real-life light hair UNet achieved the higher result, with Dice score of 0.557, while on dark hair all models ranged 0.61-0.65. CONCLUSIONS:The study used a synthetic dataset to train the UNET neural network, which yielded results superior to analytical approaches known from dermatological images. The presented preprocessing pipeline is freely available and can be found on our GitHub repository https://github.com/aKempski01/UVFD-data-preprocessing.

Objective.Cyclotron Centre Bronowice Krakow proton therapy center is establishing a methodology for dosimetric evaluation to support treatments of moving targets, particularly in the mediastinal area. We propose a robust 4D Monte Carlo (MC) dose evaluation approach for proton treatment plans, integrating temporal information on both pencil beam delivery and organ motion in the context of free-breathing treatments.Approach.A specialized tool combining the GPU-accelerated FRED MC code and temporal patient and treatment delivery data was developed. The methodology includes standard 3D dose evaluation for setup and CT calibration uncertainties, plan recalculation across respiratory phases, and robust 4D dose evaluation (4DDE). The 4DDE approach accumulates dose over deformable respiratory phases using log file time information to assess treatment plans at various respiratory cycle starting points. Validation used anonymized 4D computed tomography scans from a Hodgkin lymphoma patient, with deformable image registration extracting motion vector fields. For reference, proton plans were also recalculated on the median tumor position phase image and results were analyzed for dose-volume histograms parameters variability, 3D gamma index (GI) analysis (3%/3 mm) and voxel-wise worst case scenario, relative to the reference treatment planning system calculation.Main results.Exemplary robust 4DDE revealed that considering time provides insight into the worst-case scenarios, resulting in wider dose ranges (GI 55.05% to 88.36% vs 48.26% to 92.92% for 3D and 4D analysis, respectively). The voxel-wise worst-case dose distribution showed reduced target coverage by 47.4% and 20.8% (for 95% and 90% isodose) when accounting for respiratory motion.Significance.The study introduces a fast tool for 4DDE, demonstrating versatility ensures robustness in 3D and 4D evaluations, underscoring the significance of dynamic anatomical considerations in 4D proton radiotherapy.

BACKGROUND:Radiomic features, derived from a region of interest (ROI) in medical images, are valuable as prognostic factors. Selecting an appropriate ROI is critical, and many recent studies have focused on leveraging multiple ROIs by segmenting analogous regions across patients - such as the primary tumour and peritumoral area or subregions of the tumour. These can be straightforwardly incorporated into models as additional features. However, a more complex scenario arises, for example, in a regionally disseminated disease, when multiple distinct lesions are present. AIM:This study aims to evaluate the feasibility of integrating radiomic data from multiple lesions into survival models. We explore strategies for incorporating these ROIs and hypothesize that including all available lesions can improve model performance. METHODS:While each lesion produces a feature vector, the desired result is a unified prediction. We propose methods to aggregate either the feature vectors to form a representative one or the modelling results to compute a consolidated risk score. As a proof of concept, we apply these strategies to predict distant metastasis risk in a cohort of 115 non-small cell lung cancer patients, 60% of whom exhibit regionally advanced disease. Two feature sets (radiomics extracted from PET and PET interpolated to CT resolution) are tested across various survival models using a Monte Carlo Cross-Validation framework. RESULTS:Across both feature sets, incorporating all available lesions - rather than limiting analysis to the primary tumour - consistently improved the c-index, irrespective of the survival model used. The highest c-Index obtained by a primary tumour-only model was 0.611 for the PET dataset and 0.614 for the PET_CT dataset, while by using all lesions we were able to achieve c-Indices of 0.632 and 0.634. CONCLUSION:Lesions beyond the primary tumour carry information that should be utilized in radiomics-based models to enhance predictive ability.

BACKGROUND AND OBJECTIVE:Videodermoscopy is the gold standard in melanoma diagnosis. A new research trend aims to support specialists' work by automating this examination method, but most research focuses on developing detectors to replace dermatologists. Our study aims to present a novel idea which focuses on supporting the examination process instead of completely automating it. Instead of detecting and classifying all naevi each time, our approach focuses on full screening at the first visit and shortening the examination at subsequent visits. During the following examinations, images of all naevi are taken by supporting staff and are compared with previously taken images using feature-matching or co-registration-based algorithms. An unmatched naevus needs to be checked by the specialist, meaning that a new naevus appeared or an old one had changed. METHODS:The similarity between images (ranging from 0% to 100%) was obtained with characteristic points searching (ORB method) and co-registration-based methods. No dataset presenting changes of naevi over time exists, so augmenting the images from PH2 and HAM10000 datasets was required to simulate the time flow. RESULTS:The decision bound was set, so the true positive rate of each method was equal to 99%, giving the specificity of ORB equal to 97% (PH2) and 89% (HAM10000). Similar tests were performed using a co-registration algorithm giving specificity equal to 23% (PH2) and 19% (HAM10000). CONCLUSIONS:We believe that employing our approach will allow supporting staff to perform screening without experts' help, decreasing examination time and costs and increasing the accessibility of dermoscopic examinations.

BACKGROUND AND OBJECTIVE:This study explores an extension of the classic Hawk and Dove evolutionary game model by considering the influence of environmental or external resources on the players' fitness. This allows us to model the resulting heterogeneous population dynamics, which is of great importance for simulating cancer population growth and optimizing anti-cancer therapies. METHODS:To model population heterogeneity, we are using an extension of classical spatial evolutionary game theory by introducing multidimensional spatial evolutionary games (MSEG). This allows for the study of genetic heterogeneity on a multidimensional lattice. The classic Hawk and Dove model is modified to reflect the impact of external resources. Various types and shapes of resource functions were included in the payoff matrix and then simulated to examine their impact on the model's dynamics and population heterogeneity. RESULTS:The results are presented in time-dependent plots for both mean-field and spatial models. Additionally, spatial 2D and 3D matrices are presented to show the spatial distribution of both phenotypes analyzed in the extended Hawk and Dove model. The results reveal significant differences between the mean-field and spatial models for the same parameter values. Furthermore, differences are observed when comparing models with different resource functions. CONCLUSION:The two-phenotype model was used to show the influence of external, time- and phenotype-specific resource functions on the dynamics of the game's phenotypes. Moreover, the study highlights that spatial models, which provide more accurate information about population heterogeneity, can yield significantly different results compared to mean-field models.

This study presents Fast paRticle thErapy Dose optimizer (FREDopt), a newly developed GPU-accelerated open-source optimization software for simultaneous proton dose and dose-averaged linear energy transfer (LET d ) optimization in intensity-modulated proton therapy treatment planning. FREDopt was implemented entirely in Python, leveraging CuPy for GPU acceleration and incorporating fast Monte Carlo simulations from the FRED code. The treatment plan optimization workflow includes pre-optimization and optimization, the latter equipped with a novel superiorization of feasibility-seeking algorithms. Feasibility-seeking requires finding a point that satisfies prescribed constraints. Superiorization interlaces computational perturbations into iterative feasibility-seeking steps to steer them toward a superior feasible point, replacing the need for costly full-fledged constrained optimization. The method was validated on two treatment plans of patients treated in a clinical proton therapy center, with dose and LET d distributions compared before and after reoptimization. Simultaneous dose and LET d optimization using FREDopt led to a substantial reduction of LET d and (dose) × (LET d ) in organs at risk while preserving target dose conformity. Computational performance evaluation showed execution times of 14–50 min, depending on the algorithm and target volume size—satisfactory for clinical and research applications while enabling further development of the well-tested, documented open-source software.

Background and purpose:18F-Fluorodeoxyglucose-Positron Emission Tomography - Computed Tomography (18FDG-PET-CT) is commonly used for baseline clinical staging in cervical cancer. In this study, we assessed the prognostic value of standardised PET-CT parameters for the overall survival (OS) of patients treated with definitive chemoradiotherapy (CRT), or radiotherapy (RT) with subsequent brachytherapy (BT). Material and methods:This study included consecutive cervical cancer patients treated with definitive CRT or RT and BT, between 2011 and 2017, at a single tertiary institution. Each patient had a 18-FDG-PET-CT scan before treatment. Patients from three institutions with equal inclusion criteria were included in external validation group. The metabolic parameters of the primary tumour: standardized uptake value (SUV) derivatives, metabolic tumour volume (MTV) and total lesion glycolysis (TLG), were evaluated using the semi-automated method. Statistical analysis was conducted using the Kaplan-Meier method, log-rank tests, Cox regression models, and the Akaike Information Criterion (AIC). Results:The study group included 198 patients treated with RT (100 %) concurrent with Cisplatin-based CT (91.4 %), and subsequent BT (99 %). The majority of patients were diagnosed with squamous cell carcinoma (96.5 %) and International Federation of Gynaecology and Obstetrics (FIGO) stage III disease (84.3 %). The OS was significantly higher in patients with TLG30 below the median value (78.8 % vs. 58.9 %; p < 0.01). TLG30 remained as the only independent prognostic factor (hazard ratio 1.32,95 % confidence interval:1.12-1.56, p < 0.01). In the external validation model neither of analysed PET parameters were significant. Conclusions:A high TLG30 was associated with worse OS in primary cohort. However, external validation model did not confirm the clinical utility of the cervical tumour's metabolic parameters.

PURPOSE:This work aims at implementation, validation, and proof of application of fast, voxel-based single proton linear energy transfer (LET) spectra scoring for clinical proton therapy. The LET spectra provide more comprehensive information on the mixed radiation field produced by protons in heterogeneous patient geometry in comparison to the dose-averaged LET (LETd), commonly investigated pre-clinically and clinically. MATERIALS AND METHODS:We implemented single particle spectra scoring methods for LET and other physics quantities, e.g. deposited energy or track length, characterising mixed radiation fields in voxelized geometries. The scorers were implemented in a GPU-accelerated Monte Carlo (MC) code Fred, as well as a general purpose MC codes Gate/Geant4 and Fluka. The validation included a comparison of spectra obtained with Fred, Gate and Fluka, and evaluating the calculation performance. The LET spectra were also calculated for an intensity-modulated proton therapy (IMPT) patient treatment plan and compared to LETd. RESULTS:Implementation of spectra scorers of various quantities, including the LET was shown to be conducted accurately and spectra obtained with Fred, Gate and Fluka are in excellent agreement. The GPU acceleration allows precise and time-efficient calculation of the LET spectra and can be conducted within about an hour with Fred (using two GPU cards) compared to tens of hours with Gate or Fluka (using up to 400 CPUs). We have also shown that for an IMPT patient treatment plan, single particle LET spectra may differ for the same LETd, being potentially responsible for uncertainties in the advanced treatment planning methods based on variable RBE and LETd. CONCLUSIONS:Single particle LET spectra scoring is possible with the state-of-the-art Monte Carlo methods, allowing a more detailed insight into radiation effects in mixed radiation fields produced by proton beams in a human body and, thanks to time efficient calculations enables future clinical translation of the method.

Objective. To study the effect of dose-rate in the time evolution of chemical yields produced in pure water versus a cellular-like environment for FLASH radiotherapy research. Approach. A version of TOPAS-nBio with Tau-Leaping algorithm was used to simulate the homogenous chemistry stage of water radiolysis using three chemical models: (1) liquid water model that considered scavenging of eaq-, H center dot by dissolved oxygen; (2) Michaels & Hunt model that considered scavenging of center dot OH, eaq-, and H center dot by biomolecules existing in cellular environment; (3) Wardman model that considered model 2) and the non-enzymatic antioxidant glutathione (GSH). H2O2 concentrations at conventional and FLASH dose-rates were compared with published measurements. Model 3) was used to estimate DNA single-strand break (SSB) yields and compared with published data. SSBs were estimated from simulated yields of DNA hydrogen abstraction and attenuation factors to account for the scavenging capacity of the medium. The simulation setup consisted of monoenergetic protons (100 MeV) delivered in pulses at conventional (0.2857 Gy s-1) and FLASH (500 Gy s-1) dose rates. Dose varied from 5-20 Gy, and oxygen concentration from 10 mu M-1 mM. Main Results. At the steady state, for model (1), H2O2 concentration differed by 81.5%+/- 4.0% between FLASH and conventional dose-rates. For models (2) and (3) the differences were within 8.0%+/- 4.8%, and calculated SSB yields agreed with published data within 3.8%+/- 1.2%. A maximum oxygen concentration difference of 60% and 50% for models (2) and (3) between conventional and FLASH dose-rates was found between 2 x 106 and 9 x 1013 ps for 20 Gy of absorbed dose. Significance. The findings highlight the importance of developing more advanced cellular models to account for both the chemical and biological factors that comprise the FLASH effect. It was found that differences between pure water and cellular environment models were significant and extrapolating results between the two should be avoided. Observed differences call for further experimental investigation.

Due to the difficulties in retrieving both the time-dependent shapes of the vessels and the generation of numerical meshes for such cases, most of the simulations of blood flow in the cardiac arteries use static geometry. The article describes a methodology for generating a sequence of time-dependent 3D shapes based on images of different resolutions and qualities acquired from ECG-gated coronary artery CT angiography. The precision of the shape restoration method has been validated using an independent technique. The original proposed approach also generates for each of the retrieved vessel shapes a numerical mesh of the same topology (connectivity matrix), greatly simplifying the CFD blood flow simulations. This feature is of significant importance in practical CFD simulations, as it gives the possibility of using the mesh-morphing utility, minimizing the computation time and the need of interpolation between boundary meshes at subsequent time instants. The developed technique can be applied to generate numerical meshes in arteries and other organs whose shapes change over time. It is applicable to medical images produced by other than angio-CT modalities.

The GATE toolkit (GEANT4 Application for Tomographic Emission) is a GEANT4-based (GEometry ANd Tracking) platform for Monte Carlo simulations in medical physics. GATE applications can be divided into two main axes: radiation-based medical imaging and radiotherapy/dosimetry. The accurate modeling of the first one is crucial for system design and optimization as well as for development and refinement of image analysis algorithms. The importance of the precise simulation of the second is essential for characterisation of external beam radiotherapy (proton therapy and carbon ion therapy) and absorbed dose assessment. Within this paper, we discuss the main features of GATE and give a general view on applications, followed by insights into future development perspectives.

Objective: The aim of this work is to investigate the feasibility of the Jagiellonian Positron Emission Tomography (J-PET) scanner for intra-treatment proton beam range monitoring. Approach: The Monte Carlo simulation studies with GATE and PET image reconstruction with CASToR were performed in order to compare six J-PET scanner geometries (three dual-heads and three cylindrical). We simulated proton irradiation of a PMMA phantom with a Single Pencil Beam (SPB) and Spread-Out Bragg Peak (SOBP) of various ranges. The sensitivity and precision of each scanner were calculated, and considering the setup's cost-effectiveness, we indicated potentially optimal geometries for the J-PET scanner prototype dedicated to the proton beam range assessment. Main results: The investigations indicate that the double-layer cylindrical and triple-layer double-head configurations are the most promising for clinical application. We found that the scanner sensitivity is of the order of 10$^{-5}$ coincidences per primary proton, while the precision of the range assessment for both SPB and SOBP irradiation plans was found below 1 mm. Among the scanners with the same number of detector modules, the best results are found for the triple-layer dual-head geometry. Significance: We performed simulation studies demonstrating that the feasibility of the J-PET detector for PET-based proton beam therapy range monitoring is possible with reasonable sensitivity and precision enabling its pre-clinical tests in the clinical proton therapy environment. Considering the sensitivity, precision and cost-effectiveness, the double-layer cylindrical and triple-layer dual-head J-PET geometry configurations seem promising for the future clinical application. Experimental tests are needed to confirm these findings.

Abstract Background Tumour hypoxia is a recognised cause of radiotherapy treatment resistance in head and neck squamous cell carcinoma (HNSCC). Current positron emission tomography-based hypoxia imaging techniques are not routinely available in many centres. We investigated if an alternative technique called oxygen-enhanced magnetic resonance imaging (OE-MRI) could be performed in HNSCC. Methods A volumetric OE-MRI protocol for dynamic T1 relaxation time mapping was implemented on 1.5-T clinical scanners. Participants were scanned breathing room air and during high-flow oxygen administration. Oxygen-induced changes in T1 times (ΔT1) and R 2* rates (ΔR 2*) were measured in malignant tissue and healthy organs. Unequal variance t-test was used. Patients were surveyed on their experience of the OE-MRI protocol. Results Fifteen patients with HNSCC (median age 59 years, range 38 to 76) and 10 non-HNSCC subjects (median age 46.5 years, range 32 to 62) were scanned; the OE-MRI acquisition took less than 10 min and was well tolerated. Fifteen histologically confirmed primary tumours and 41 malignant nodal masses were identified. Median (range) of ΔT1 times and hypoxic fraction estimates for primary tumours were -3.5% (-7.0 to -0.3%) and 30.7% (6.5 to 78.6%) respectively. Radiotherapy-responsive and radiotherapy-resistant primary tumours had mean estimated hypoxic fractions of 36.8% (95% confidence interval [CI] 17.4 to 56.2%) and 59.0% (95% CI 44.6 to 73.3%), respectively (p = 0.111). Conclusions We present a well-tolerated implementation of dynamic, volumetric OE-MRI of the head and neck region allowing discernment of differing oxygen responses within biopsy-confirmed HNSCC. Trial registration ClinicalTrials.gov, NCT04724096 . Registered on 26 January 2021. Relevance statement MRI of tumour hypoxia in head and neck cancer using routine clinical equipment is feasible and well tolerated and allows estimates of tumour hypoxic fractions in less than ten minutes. Key points • Oxygen-enhanced MRI (OE-MRI) can estimate tumour hypoxic fractions in ten-minute scanning. • OE-MRI may be incorporable into routine clinical tumour imaging. • OE-MRI has the potential to predict outcomes after radiotherapy treatment. Graphical Abstract

Objective. The Jagiellonian positron emission tomography (J-PET) technology, based on plastic scintillators, has been proposed as a cost effective tool for detecting range deviations during proton therapy. This study investigates the feasibility of using J-PET for range monitoring by means of a detailed Monte Carlo simulation study of 95 patients who underwent proton therapy at the Cyclotron Centre Bronowice (CCB) in Krakow, Poland. Approach. Discrepancies between prescribed and delivered treatments were artificially introduced in the simulations by means of shifts in patient positioning and in the Hounsfield unit to the relative proton stopping power calibration curve. A dual-layer, cylindrical J-PET geometry was simulated in an in-room monitoring scenario and a triple-layer, dual-head geometry in an in-beam protocol. The distribution of range shifts in reconstructed PET activity was visualized in the beam's eye view. Linear prediction models were constructed from all patients in the cohort, using the mean shift in reconstructed PET activity as a predictor of the mean proton range deviation. Main results. Maps of deviations in the range of reconstructed PET distributions showed agreement with those of deviations in dose range in most patients. The linear prediction model showed a good fit, with coefficient of determination r (2) = 0.84 (in-room) and 0.75 (in-beam). Residual standard error was below 1 mm: 0.33 mm (in-room) and 0.23 mm (in-beam). Significance. The precision of the proposed prediction models shows the sensitivity of the proposed J-PET scanners to shifts in proton range for a wide range of clinical treatment plans. Furthermore, it motivates the use of such models as a tool for predicting proton range deviations and opens up new prospects for investigations into the use of intra-treatment PET images for predicting clinical metrics that aid in the assessment of the quality of delivered treatment.