The linear quadratic model: usage, interpretation and challenges

Lymphocyte radiosensitivity: An extension to the linear-quadratic model?

BACKGROUND AND PURPOSE

The linear-quadratic (LQ) model has been pivotal for evaluating the effects of radiation on cells, but it is primarily characterized by linear responses, which has exhibited limitations when applied to lymphocyte data. The present research aims to address these limitations and to explore an alternative model extended from the conventional LQ model.

MATERIALS AND METHODS

Literature providing lymphocyte counts from assays investigating apoptosis and survival after in vitro irradiation was selected. To address the nonlinearity in lymphocyte responses to radiation, we developed a saturation model characterized by a negative exponential relationship between radiation dose and cellular response. We compared the performance of this saturation model against that of conventional models, including the LQ model and its variants (linear model LM and linear-quadratic-cubic model LQC), as well as the repair-misrepair (RMR) model. The models were evaluated based on prediction-residual plots, residual standard errors, and the Akaike information criterion (AIC). We applied the saturation model to two additional datasets: (1) a dataset from the existing literature that assessed stimulated and unstimulated human lymphocytes exposed to gamma irradiation in vitro and (2) a novel dataset involving T lymphocytes from rodent spleens after exposure to various radiation types (X-rays and protons).

RESULTS

The literature (n = 15 out of 2342) showed that lymphocyte apoptosis varies with dose, time and experimental conditions. The saturation model had a lower AIC of 718 compared to the LM, LQ, LQC and RMR models (AIC of 728, 720, 720 and 734, respectively). The saturation model had a lower residual error and more consistent error distribution. Integrating time as a covariate, the saturation model also had a better AIC for demonstrating time-dependent variations in lymphocyte responses after irradiation. For datasets involving unstimulated lymphocytes before irradiation, the saturation model provided a more accurate fit than did the LM, LQ, and RMR models. In these cases, the fit of the saturation model was comparable to that of the LQC model but offered an advantage when extrapolating to higher doses, where the LQC model might underestimate survival. For stimulated lymphocytes, which are radioresistant, all the models approximated the LM. Both the LQ and saturation models indicated greater radiosensitivity to protons in vitro.

CONCLUSION

The new "saturation model" performed better than the LQ model in quantifying lymphocyte apoptosis and survival, estimating time dependency and assessing the role of radiation modalities or lymphocyte stimulation. Further experiments are warranted to experimentally explore the validity of the saturation model as a promising alternative in the clinical setting.

Neutron Capture Enhances Dose and Reduces Cancer Cell Viability in and out of Beam During Helium and Carbon Ion Therapy

PURPOSE

Neutron capture enhanced particle therapy (NCEPT) is a proposed augmentation of charged particle therapy that exploits thermal neutrons generated internally, within the treatment volume via nuclear fragmentation, to deliver a biochemically targeted radiation dose to cancer cells. This work is the first experimental demonstration of NCEPT, performed using both carbon and helium ion beams with 2 different targeted neutron capture agents (NCAs).

METHODS AND MATERIALS

Human glioblastoma cells (T98G) were irradiated by carbon and helium ion beams in the presence of NCAs [10B]-BPA and [157Gd]-DOTA-TPP. Cells were positioned within a polymethyl methacrylate phantom either laterally adjacent to or within a 100 × 100 × 60 mm spread out Bragg peak (SOBP). The effect of NCAs and location relative to the SOBP on the cells was measured by cell growth and survival assays in 6 independent experiments. Neutron fluence within the phantom was characterized by quantifying the neutron activation of gold foil.

RESULTS

Cells placed inside the treatment volume reached 10% survival by 2 Gy of carbon or 2 to 3 Gy of helium in the presence of NCAs compared with 5 Gy of carbon and 7 Gy of helium with no NCA. Cells placed adjacent to the treatment volume showed a dose-dependent decrease in cell growth when treated with NCAs, reaching 10% survival by 6 Gy of carbon or helium (to the treatment volume), compared with no detectable effect on cells without NCA. The mean thermal neutron fluence at the center of the SOBP was approximately 2.2 × 109 n/cm2/Gy (relative biological effectiveness) for the carbon beam and 5.8 × 109 n/cm2/Gy (relative biological effectiveness) for the helium beam and gradually decreased in all directions.

CONCLUSIONS

The addition of NCAs to cancer cells during carbon and helium beam irradiation has a measurable effect on cell survival and growth in vitro. Through the capture of internally generated neutrons, NCEPT introduces the concept of a biochemically targeted radiation dose to charged particle therapy. NCEPT enables the established pharmaceuticals and concepts of neutron capture therapy to be applied to a wider range of deeply situated and diffuse tumors, by targeting this dose to microinfiltrates and cells outside of defined treatment regions. These results also demonstrate the potential for NCEPT to provide an increased dose to tumor tissue within the treatment volume, with a reduction in radiation doses to off-target tissue.

LET-based approximation of the microdosimetric kinetic model for proton radiotherapy

BACKGROUND

Phenomenological relative biological effectiveness (RBE) models for proton therapy, based on the dose-averaged linear energy transfer (LET), have been developed to address the apparent RBE increase towards the end of the proton range. The results of these phenomenological models substantially differ due to varying empirical assumptions and fitting functions. In contrast, more theory-based approaches are used in carbon ion radiotherapy, such as the microdosimetric kinetic model (MKM). However, implementing microdosimetry-based models in LET-based proton therapy treatment planning systems poses challenges.

PURPOSE

This work presents a LET-based version of the MKM that is practical for clinical use in proton radiotherapy.

METHODS

At first, we derived an approximation of the Mayo Clinic Florida (MCF) MKM for relatively-sparsely ionizing radiation such as protons. The mathematical formalism of the proposed model is equivalent to the original MKM, but it maintains some key features of the MCF MKM, such as the determination of model parameters from measurable cell characteristics. Subsequently, we carried out Monte Carlo calculations with PHITS in different simulated scenarios to establish a heuristic correlation between microdosimetric quantities and the dose averaged LET of protons.

RESULTS

A simple allometric function was found able to describe the relationship between the dose-averaged LET of protons and the dose-mean lineal energy, which includes the contributions of secondary particles. The LET-based MKM was used to model the in vitro clonogenic survival RBE of five human and rodent cell lines (A549, AG01522, CHO, T98G, and U87) exposed to pristine and spread-out Bragg peak (SOBP) proton beams. The results of the LET-based MKM agree well with the biological data in a comparable or better way with respect to the other models included in the study. A sensitivity analysis on the model results was also performed.

CONCLUSIONS

The LET-based MKM integrates the predictive theoretical framework of the MCF MKM with a straightforward mathematical description of the RBE based on the dose-averaged LET, a physical quantity readily available in modern treatment planning systems for proton therapy.

Size-Dependent Blue Emission from Europium-Doped Strontium Fluoride Nanoscintillators for X-Ray-Activated Photodynamic Therapy

Successful implementation of X-ray-activated photodynamic therapy (X-PDT) is challenging because most photosensitizers (PSs) absorb light in the blue region, but few nanoscintillators produce efficient blue scintillation. Here, efficient blue-emitting SrF2:Eu scintillating nanoparticles (ScNPs) are developed. The optimized synthesis conditions result in cubic nanoparticles with ≈32 nm diameter and blue emission at 416 nm. Coating them with the meso-tetra(n-methyl-4-pyridyl) porphyrin (TMPyP) in a core-shell structure (SrF@TMPyP) results in maximum singlet oxygen (1O2) generation upon X-ray irradiation for nanoparticles with 6TMPyP depositions (SrF@6TMPyP). The 1O2 generation is directly proportional to the dose, does not vary in the low-energy X-ray range (48-160 kVp), but is 21% higher when irradiated with low-energy X-rays than irradiations with higher energy gamma rays. In the clonogenic assay, cancer cells treated with SrF@6TMPyP and exposed to X-rays present a significantly reduced survival fraction compared to the controls. The SrF2:Eu ScNPs and their conjugates stand out as tunable nanoplatforms for X-PDT due to the efficient blue emission from the SrF2:Eu cores; the ability to adjust the scintillation emission in terms of color and intensity by controlling the nanoparticle size; the efficient 1O2 production when conjugated to a PS and the efficacy of killing cancer cells.

Mechanistic in silico explorations of the immunogenic and synergistic effects of radiotherapy and immunotherapy: a critical review

Immunotherapy is a rapidly evolving field, with many models attempting to describe its impact on the immune system, especially when paired with radiotherapy. Tumor response to this combination involves a complex spatiotemporal dynamic which makes either clinical or pre-clinical in vivo investigation across the resulting extensive solution space extremely difficult. In this review, several in silico models of the interaction between radiotherapy, immunotherapy, and the patient's immune system are examined. The study included only mathematical models published in English that investigated the effects of radiotherapy on the immune system, or the effect of immuno-radiotherapy with immune checkpoint inhibitors. The findings indicate that treatment efficacy was predicted to improve when both radiotherapy and immunotherapy were administered, compared to radiotherapy or immunotherapy alone. However, the models do not agree on the optimal schedule and fractionation of radiotherapy and immunotherapy. This corresponds to relevant clinical trials, which report an improved treatment efficacy with combination therapy, however, the optimal scheduling varies between clinical trials. This discrepancy between the models can be attributed to the variation in model approach and the specific cancer types modeled, making the determination of the optimum general treatment schedule and model challenging. Further research needs to be conducted with similar data sets to evaluate the best model and treatment schedule for a specific cancer type and stage.

Ultrasensitive Response Explains the Benefit of Combination Chemotherapy Despite Drug Antagonism

Most aggressive lymphomas are treated with combination chemotherapy, commonly as multiple cycles of concurrent drug administration. Concurrent administration is in theory optimal when combination therapies have synergistic (more than additive) drug interactions. We investigated pharmacodynamic interactions in the standard 4-drug "CHOP" regimen in peripheral T-cell lymphoma (PTCL) cell lines and found that CHOP consistently exhibits antagonism and not synergy. We tested whether staggered treatment schedules could improve tumor cell kill by avoiding antagonism, using in vitro models of concurrent or staggered treatments. Surprisingly, we observed that tumor cell kill is maximized by concurrent drug administration despite antagonistic drug-drug interactions. We propose that an ultrasensitive dose response, as described in radiology by the linear-quadratic (LQ) model, can reconcile these seemingly contradictory experimental observations. The LQ model describes the relationship between cell survival and dose, and in radiology has identified scenarios favoring hypofractionated radiotherapy-the administration of fewer large doses rather than multiple smaller doses. Specifically, hypofractionated treatment can be favored when cells require an accumulation of DNA damage, rather than a "single hit," to die. By adapting the LQ model to combination chemotherapy and accounting for tumor heterogeneity, we find that tumor cell kill is maximized by concurrent administration of multiple drugs, even when chemotherapies have antagonistic interactions. Thus, our study identifies a new mechanism by which combination chemotherapy can be clinically beneficial that is not contingent on positive drug-drug interactions.

A joint physics and radiobiology DREAM team vision - Towards better response prediction models to advance radiotherapy

Radiotherapy developed empirically through experience balancing tumour control and normal tissue toxicities. Early simple mathematical models formalized this practical knowledge and enabled effective cancer treatment to date. Remarkable advances in technology, computing, and experimental biology now create opportunities to incorporate this knowledge into enhanced computational models. The ESTRO DREAM (Dose Response, Experiment, Analysis, Modelling) workshop brought together experts across disciplines to pursue the vision of personalized radiotherapy for optimal outcomes through advanced modelling. The ultimate vision is leveraging quantitative models dynamically during therapy to ultimately achieve truly adaptive and biologically guided radiotherapy at the population as well as individual patient-based levels. This requires the generation of models that inform response-based adaptations, individually optimized delivery and enable biological monitoring to provide decision support to clinicians. The goal is expanding to models that can drive the realization of personalized therapy for optimal outcomes. This position paper provides their propositions that describe how innovations in biology, physics, mathematics, and data science including AI could inform models and improve predictions. It consolidates the DREAM team's consensus on scientific priorities and organizational requirements. Scientifically, it stresses the need for rigorous, multifaceted model development, comprehensive validation and clinical applicability and significance. Organizationally, it reinforces the prerequisites of interdisciplinary research and collaboration between physicians, medical physicists, radiobiologists, and computational scientists throughout model development. Solely by a shared understanding of clinical needs, biological mechanisms, and computational methods, more informed models can be created. Future research environment and support must facilitate this integrative method of operation across multiple disciplines.

Mechanism of the Radioresistant Colorectal Cancer Cell Line SW480RR Established after Fractionated X Irradiation

Radioresistant cancer cells are risk factors for recurrence and are occasionally detected in recurrent tumors after radiotherapy. Intratumor heterogeneity is believed to be a potential cause of treatment resistance. Heterogeneity in DNA content has also been reported in human colorectal cancer; however, little is known about how such heterogeneity changes with radiotherapy or how it affects cancer radioresistance. In the present study, we established radioresistant clone SW480RR cells after fractionated X-ray irradiation of human colorectal cancer-derived SW480.hu cells, which are composed of two cell populations with different chromosome numbers, and examined how cellular radioresistance changed with fractionated radiotherapy. Compared with the parental cell population, which mostly comprised cells with higher ploidy, the radioresistant clones showed lower ploidy and less initial DNA damage. The lower ploidy cells in the parental cell population were identified as having radioresistance prior to irradiation; thus, SW480RR cells were considered intrinsically radioresistant cells selected from the parental population through fractionated irradiation. This study presents a practical example of the emergence of radioresistant cells from a cell population with ploidy heterogeneity after irradiation. The most likely mechanism is the selection of an intrinsically radioresistant population after fractionated X-ray irradiation, with a background in which lower ploidy cells exhibit lower initial DNA damage.

Improving Pediatric Normal Tissue Radiation Dose-Response Modeling in Children With Cancer: A PENTEC Initiative

The development of normal tissue radiation dose-response models for children with cancer has been challenged by many factors, including small sample sizes; the long length of follow-up needed to observe some toxicities; the continuing occurrence of events beyond the time of assessment; the often complex relationship between age at treatment, normal tissue developmental dynamics, and age at assessment; and the need to use retrospective dosimetry. Meta-analyses of published pediatric outcome studies face additional obstacles of incomplete reporting of critical dosimetric, clinical, and statistical information. This report describes general methods used to address some of the pediatric modeling issues. It highlights previous single- and multi-institutional pediatric dose-response studies and summarizes how each PENTEC taskforce addressed the challenges and limitations of the reviewed publications in constructing, when possible, organ-specific dose-effect models.

AAPM Task Group Report 267: A joint AAPM GEC-ESTRO report on biophysical models and tools for the planning and evaluation of brachytherapy

Brachytherapy utilizes a multitude of radioactive sources and treatment techniques that often exhibit widely different spatial and temporal dose delivery patterns. Biophysical models, capable of modeling the key interacting effects of dose delivery patterns with the underlying cellular processes of the irradiated tissues, can be a potentially useful tool for elucidating the radiobiological effects of complex brachytherapy dose delivery patterns and for comparing their relative clinical effectiveness. While the biophysical models have been used largely in research settings by experts, it has also been used increasingly by clinical medical physicists over the last two decades. A good understanding of the potentials and limitations of the biophysical models and their intended use is critically important in the widespread use of these models. To facilitate meaningful and consistent use of biophysical models in brachytherapy, Task Group 267 (TG-267) was formed jointly with the American Association of Physics in Medicine (AAPM) and The Groupe Européen de Curiethérapie and the European Society for Radiotherapy & Oncology (GEC-ESTRO) to review the existing biophysical models, model parameters, and their use in selected brachytherapy modalities and to develop practice guidelines for clinical medical physicists regarding the selection, use, and interpretation of biophysical models. The report provides an overview of the clinical background and the rationale for the development of biophysical models in radiation oncology and, particularly, in brachytherapy; a summary of the results of literature review of the existing biophysical models that have been used in brachytherapy; a focused discussion of the applications of relevant biophysical models for five selected brachytherapy modalities; and the task group recommendations on the use, reporting, and implementation of biophysical models for brachytherapy treatment planning and evaluation. The report concludes with discussions on the challenges and opportunities in using biophysical models for brachytherapy and with an outlook for future developments.

A comparative study on the dose-effect of low-dose radiation based on microdosimetric analysis and single-cell sequencing technology

The biological mechanisms triggered by low-dose exposure still need to be explored in depth. In this study, the potential mechanisms of low-dose radiation when irradiating the BEAS-2B cell lines with a Cs-137 gamma-ray source were investigated through simulations and experiments. Monolayer cell population models were constructed for simulating and analyzing distributions of nucleus-specific energy within cell populations combined with the Monte Carlo method and microdosimetric analysis. Furthermore, the 10 × Genomics single-cell sequencing technology was employed to capture the heterogeneity of individual cell responses to low-dose radiation in the same irradiated sample. The numerical uncertainties can be found both in the specific energy distribution in microdosimetry and in differential gene expressions in radiation cytogenetics. Subsequently, the distribution of nucleus-specific energy was compared with the distribution of differential gene expressions to guide the selection of differential genes bioinformatics analysis. Dose inhomogeneity is pronounced at low doses, where an increase in dose corresponds to a decrease in the dispersion of cellular-specific energy distribution. Multiple screening of differential genes by microdosimetric features and statistical analysis indicate a number of potential pathways induced by low-dose exposure. It also provides a novel perspective on the selection of sensitive biomarkers that respond to low-dose radiation.

Mathematical modeling of the synergistic interplay of radiotherapy and immunotherapy in anti-cancer treatments

Introduction

While radiotherapy has long been recognized for its ability to directly ablate cancer cells through necrosis or apoptosis, radiotherapy-induced abscopal effect suggests that its impact extends beyond local tumor destruction thanks to immune response. Cellular proliferation and necrosis have been extensively studied using mathematical models that simulate tumor growth, such as Gompertz law, and the radiation effects, such as the linear-quadratic model. However, the effectiveness of radiotherapy-induced immune responses may vary among patients due to individual differences in radiation sensitivity and other factors.

Methods

We present a novel macroscopic approach designed to quantitatively analyze the intricate dynamics governing the interactions among the immune system, radiotherapy, and tumor progression. Building upon previous research demonstrating the synergistic effects of radiotherapy and immunotherapy in cancer treatment, we provide a comprehensive mathematical framework for understanding the underlying mechanisms driving these interactions.

Results

Our method leverages macroscopic observations and mathematical modeling to capture the overarching dynamics of this interplay, offering valuable insights for optimizing cancer treatment strategies. One shows that Gompertz law can describe therapy effects with two effective parameters. This result permits quantitative data analyses, which give useful indications for the disease progression and clinical decisions.

Discussion

Through validation against diverse data sets from the literature, we demonstrate the reliability and versatility of our approach in predicting the time evolution of the disease and assessing the potential efficacy of radiotherapy-immunotherapy combinations. This further supports the promising potential of the abscopal effect, suggesting that in select cases, depending on tumor size, it may confer full efficacy to radiotherapy.

Modeling hypoxia-induced radiation resistance and the impact of radiation sources

Hypoxia contributes significantly to resistance in radiotherapy. Our research rigorously examines the influence of microvascular morphology on radiotherapy outcome, specifically focusing on how microvasculature shapes hypoxia within the microenvironment and affects resistance to a standard treatment regimen (30×2GyRBE). Our computational modeling extends to the effects of different radiation sources. For photons and protons, our analysis establishes a clear correlation between hypoxic volume distribution and treatment effectiveness, with vascular density and regularity playing a crucial role in treatment success. On the contrary, carbon ions exhibit distinct effectiveness, even in areas of intense hypoxia and poor vascularization. This finding points to the potential of carbon-based hadron therapy in overcoming hypoxia-induced resistance to RT. Considering that the spatial scale analyzed in this study is closely aligned with that of imaging data voxels, we also address the implications of these findings in a clinical context envisioning the possibility of detecting subvoxel hypoxia.

A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin

The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary.

RadPhysBio: A Radiobiological Database for the Prediction of Cell Survival upon Exposure to Ionizing Radiation

Based on the need for radiobiological databases, in this work, we mined experimental ionizing radiation data of human cells treated with X-rays, γ-rays, carbon ions, protons and α-particles, by manually searching the relevant literature in PubMed from 1980 until 2024. In order to calculate normal and tumor cell survival α and β coefficients of the linear quadratic (LQ) established model, as well as the initial values of the double-strand breaks (DSBs) in DNA, we used WebPlotDigitizer and Python programming language. We also produced complex DNA damage results through the fast Monte Carlo code MCDS in order to complete any missing data. The calculated α/β values are in good agreement with those valued reported in the literature, where α shows a relatively good association with linear energy transfer (LET), but not β. In general, a positive correlation between DSBs and LET was observed as far as the experimental values are concerned. Furthermore, we developed a biophysical prediction model by using machine learning, which showed a good performance for α, while it underscored LET as the most important feature for its prediction. In this study, we designed and developed the novel radiobiological 'RadPhysBio' database for the prediction of irradiated cell survival (α and β coefficients of the LQ model). The incorporation of machine learning and repair models increases the applicability of our results and the spectrum of potential users.

Quantification of biochemical PSA dynamics after radioligand therapy with [177Lu]Lu-PSMA-I&T using a population pharmacokinetic/pharmacodynamic model

BACKGROUND

There is an unmet need for prediction of treatment outcome or patient selection for [177Lu]Lu-PSMA therapy in patients with metastatic castration-resistant prostate cancer (mCRPC). Quantification of the tumor exposure-response relationship is pivotal for further treatment optimization. Therefore, a population pharmacokinetic (PK) model was developed for [177Lu]Lu-PSMA-I&T using SPECT/CT data and, subsequently, related to prostate-specific antigen (PSA) dynamics after therapy in patients with mCRPC using a pharmacokinetic/pharmacodynamic (PKPD) modelling approach.

METHODS

A population PK model was developed using quantitative SPECT/CT data (406 scans) of 76 patients who received multiple cycles [177Lu]Lu-PSMA-I&T (± 7.4 GBq with either two- or six-week interval). The PK model consisted of five compartments; central, salivary glands, kidneys, tumors and combined remaining tissues. Covariates (tumor volume, renal function and cycle number) were tested to explain inter-individual variability on uptake into organs and tumors. The final PK model was expanded with a PD compartment (sequential fitting approach) representing PSA dynamics during and after treatment. To explore the presence of a exposure-response relationship, individually estimated [177Lu]Lu-PSMA-I&T tumor concentrations were related to PSA changes over time.

RESULTS

The population PK model adequately described observed data in all compartments (based on visual inspection of goodness-of-fit plots) with adequate precision of parameters estimates (< 36.1% relative standard error (RSE)). A significant declining uptake in tumors (k14) during later cycles was identified (uptake decreased to 73%, 50% and 44% in cycle 2, 3 and 4-7, respectively, compared to cycle 1). Tumor growth (defined by PSA increase) was described with an exponential growth rate (0.000408 h-1 (14.2% RSE)). Therapy-induced PSA decrease was related to estimated tumor concentrations (MBq/L) using both a direct and delayed drug effect. The final model adequately captured individual PSA concentrations after treatment (based on goodness-of-fit plots). Simulation based on the final PKPD model showed no evident differences in response for the two different dosing regimens currently used.

CONCLUSIONS

Our population PK model accurately described observed [177Lu]Lu-PSMA-I&T uptake in salivary glands, kidneys and tumors and revealed a clear declining tumor uptake over treatment cycles. The PKPD model adequately captured individual PSA observations and identified population response rates for the two dosing regimens. Hence, a PKPD modelling approach can guide prediction of treatment response and thus identify patients in whom radioligand therapy is likely to fail.

Average log change rate of pretreatment squamous cell carcinoma antigen after concurrent chemoradiotherapy in stage IIIC1 cervical squamous cell carcinoma

We aimed to determine whether pretreatment squamous cell carcinoma antigen (SCC-Ag) levels and the average logarithmic change in SCC-Ag levels (Δ log SCC-Ag Δ time ) after concurrent chemoradiotherapy (CCRT) could predict treatment outcomes in patients with stage IIIC1 cervical squamous cell carcinoma (SCC). We analyzed 168 patients with stage IIIC1 cervical SCC who underwent primary CCRT and collected data on age, local extension, treatment details, hematological parameters, and tumor markers such as SCC-Ag and carcinoembryonic antigen 21-1 (Cyfra). Predictive performances of pretreatment SCC-Ag levels andΔ log SCC-Ag Δ time were assessed using receiver operating characteristic curves. Survival analysis was performed using the Cox regression model and Kaplan-Meier plots. The combination of pretreatment SCC-Ag levels andΔ log SCC-Ag Δ time showed higher area under the curve values than pretreatment SCC-Ag levels alone (area under the curve; 95% confidence interval [CI] 0.708 [0.581-0.836] vs. 0.666 [0.528-0.804], respectively). Pretreatment SCC-Ag (≥ 5 ng/ml and Cyfra levels (≥ 3.15 ng/ml) andΔ log SCC-Ag Δ time (≥ - 1.575) were significant predictors of disease-specific survival. The 5-year disease-specific survival rates significantly differed among the low-, intermediate-, and high-risk groups. Risk stratification using both pretreatment SCC-Ag levels andΔ log SCC-Ag Δ time may predict treatment outcomes of patients with stage IIIC1 SCC.

Benchmarking proton RBE models

Objective.To biologically optimise proton therapy, models which can accurately predict variations in proton relative biological effectiveness (RBE) are essential. Current phenomenological models show large disagreements in RBE predictions, due to different model assumptions and differences in the data to which they were fit. In this work, thirteen RBE models were benchmarked against a comprehensive proton RBE dataset to evaluate predictions when all models are fit using the same data and fitting techniques, and to assess the statistical robustness of the models.Approach.Model performance was initially evaluated by fitting to the full dataset, and then a cross-validation approach was applied to assess model generalisability and robustness. The impact of weighting the fit and the choice of biological endpoint (either single or multiple survival levels) was also evaluated.Main results.Fitting the models to a common dataset reduced differences between their predictions, however significant disagreements remained due to different underlying assumptions. All models performed poorly under cross-validation in the weighted fits, suggesting that some uncertainties on the experimental data were significantly underestimated, resulting in over-fitting and poor performance on unseen data. The simplest model, which depends linearly on the LET but has no tissue or dose dependence, performed best for a single survival level. However, when fitting to multiple survival levels simultaneously, more complex models with tissue dependence performed better. All models had significant residual uncertainty in their predictions compared to experimental data.Significance.This analysis highlights that poor quality of error estimation on the dose response parameters introduces substantial uncertainty in model fitting. The significant residual error present in all approaches illustrates the challenges inherent in fitting to large, heterogeneous datasets and the importance of robust statistical validation of RBE models.

Clonogenic assay and computational modeling using real cell images to study physical enhancement and cellular sensitization induced by metal nanoparticles under MV and kV X-ray irradiation

This study was initiated due to the physically unexplainable tumor controls resulting from metal nanoparticle (MNP) experiments even under MV X-ray irradiation. A more accurate explanation of the mechanism of radiosensitization induced by MNP is warranted, considering both its physical dose enhancement and biological sensitization, as related research is lacking. Thus, we aimed to examine the intricate dynamics involved in MNP-induced radiosensitization. We conducted specifically designed clonogenic assays for the A549 lung cancer cell line with MNP irradiated by 6 MV and 300 kVp X-rays. Two types of MNP were employed: one based on iron oxide, promoting ferroptosis, and the other on gold nanoparticles known for inducing a significant dose enhancement, particularly at low-energy X-rays. We introduced the lethality enhancement factor (LEF) as the fraction in the cell killing attributed to biological sensitization. Subsequently, Monte Carlo simulations were conducted to evaluate the radial dose profiles for each MNP, corresponding to the physical enhancement. Finally, the local effect model was applied to the clonogenic assay results on real cell images. The LEF and the dose enhancement in the cytoplasm were incorporated to increase the accuracy in the average lethal events and, consequently, in the survival fraction. The results reveal an increased cell killing for both of the MNP under MV and kV X-ray irradiation. In both types of MNP, the LEF reveals a biological sensitization evident. The sensitizer enhancement ratio, derived from the calculations, exhibited only 3% and 1% relative differences compared to the conventional linear-quadratic model for gold and ferroptosis inducer nanoparticles, respectively. These findings indicate that MNPs sensitize cells via radiation through mechanisms akin to ferroptosis inducers, not exclusively relying on a physical dose enhancement. Their own contributions to survival fractions were successfully integrated into computational modeling.

An in-silico planning study of stereotactic body radiation therapy for polymetastatic patients with more than ten extra-cranial lesions

Background and purpose

Limited data is available about the feasibility of stereotactic body radiation therapy (SBRT) for treating more than five extra-cranial metastases, and almost no data for treating more than ten. The aim of this study was to investigate the feasibility of SBRT in this polymetatstatic setting.

Materials and methods

Consecutive metastatic melanoma patients with more than ten extra-cranial metastases and a maximum lesion diameter below 11 cm were selected from a single-center prospective registry for this in-silico planning study. For each patient, SBRT plans were generated to treat all metastases with a prescribed dose of 5x7Gy, and dose-limiting organs (OARs) were analyzed. A cell-kill based inverse planning approach was used to automatically determine the maximum deliverable dose to each lesion individually, while respecting all OARs constraints.

Results

A total of 23 polymetastatic patients with a medium of 17 metastases (range, 11-51) per patient were selected. SBRT plans with sufficient target coverage and respected OARs dose constraints were achieved in 16 out of 23 patients. In the remaining seven patients, the lungs V5Gy < 80 % and the liver D700 cm3 < 15Gy were most frequently the dose-limiting constraints. The cell-kill based planning approach allowed optimizing the dose administration depending on metastases total volume and location.

Conclusion

This retrospective planning study shows the feasibility of definitive SBRT for 70% of polymetastatic patients with more than ten extra-cranial lesions and proposes the cell-killing planning approach as an approach to individualize treatment planning in polymetastatic patients'.

Are We Ready for Life in the Fast Lane? A Critical Review of Preoperative Hypofractionated Radiotherapy for Localized Soft Tissue Sarcoma

This critical review aims to summarize the relevant published data regarding hypofractionation regimens for preoperative radiation therapy (RT) prior to surgery for soft tissue sarcoma (STS) of the extremity or superficial trunk. We identified peer-reviewed publications using a PubMed search on the MeSH headings of "soft tissue sarcoma" AND "hypofractionated radiation therapy." To obtain complication data on similar anatomical radiotherapeutic scenarios we also searched "hypofractionated radiation therapy" AND "melanoma" as well as "hypofractionated radiation therapy" AND "breast cancer." We then used reference lists from relevant articles to obtain additional pertinent publications. We also incorporated relevant abstracts presented at international sarcoma meetings and relevant clinical trials as listed on the ClinicalTrials.gov website. Detailed data are presented and contextualized for ultra-hypofractionated and moderately hypofractionated regimens with respect to local control, wound complications, and amputation rates. Comparative data are also presented for late toxicities including: fibrosis, joint limitation, edema, skin integrity, and bone fracture or necrosis. These data are compared to a standard regimen of 50 Gy in 25 daily fractions delivered over 5 weeks. This analysis supports the continued use of a standard regimen for preoperative RT for STS of 25 × 2 Gy over 5 weeks without concurrent chemotherapy. Use of concurrent chemotherapy with preoperative RT for STS should be reserved for well-designed clinical trials. A randomized trial of ultra-hypofractionated and moderately hypofractionated pre op RT for STS is warranted, but it is critical for the primary endpoint (or co-primary endpoint) to be late toxicity to: bone, soft tissue, joint, and skin.

Interplay of immune modulation, adaptive response and hormesis: Suggestive of threshold for clinical manifestation of effects of ionizing radiation at low doses?

The health impacts of low-dose ionizing radiation exposures have been a subject of debate over the last three to four decades. While there has been enough evidence of "no adverse observable" health effects at low doses and low dose rates, the hypothesis of "Linear No Threshold" continues to rule and govern the principles of radiation protection and the formulation of regulations and public policies. In adopting this conservative approach, the role of the biological processes underway in the human body is kept at abeyance. This review consolidates the available studies that discuss all related biological pathways and repair mechanisms that inhibit the progression of deleterious effects at low doses and low dose rates of ionizing radiation. It is pertinent that, taking cognizance of these processes, there is a need to have a relook at policies of radiation protection, which as of now are too stringent, leading to undue economic losses and negative public perception about radiation.

Response of Cancer Stem Cells and Human Skin Fibroblasts to Picosecond-Scale Electron Irradiation at 1010 to 1011 Gy/s

PURPOSE

This study aimed to demonstrate for the first time the possibility of irradiating biological cells with gray (Gy)-scale doses delivered over single bursts of picosecond-scale electron beams, resulting in unprecedented dose rates of 1010 to 1011 Gy/s.

METHODS AND MATERIALS

Cancer stem cells and human skin fibroblasts were irradiated with MeV-scale electron beams from a laser-driven source. Doses up to 3 Gy per pulse with a high spatial uniformity (coefficient of variance, 3%-6%) and within a timescale range of 10 to 20 picoseconds were delivered. Doses were characterized during irradiation and were found to be in agreement with Monte Carlo simulations. Cell survival and DNA double-strand break repair dynamics were studied for both cell lines using clonogenic assay and 53BP1 foci formation. The results were compared with reference x-rays at a dose rate of 0.49 Gy/min.

RESULTS

Results from clonogenic assays of both cell lines up to 3 Gy were well fitted by a linear quadratic model with α = (0.68 ± 0.08) Gy-1 and β = (0.01 ± 0.01) Gy-2 for human skin fibroblasts and α = (0.51 ± 0.14) Gy-1 and β = (0.01 ± 0.01) Gy-2 for cancer stem cells. Compared with irradiation at 0.49 Gy/min, our experimental results indicate no statistically significant difference in cell survival rate for doses up to 3 Gy despite a significant increase in the α parameter, which may reflect more complex damage. Foci measurements showed no significant difference between irradiation at 1011 Gy/s and at 0.49 Gy/min.

CONCLUSIONS

This study demonstrates the possibility of performing radiobiological studies with picosecond-scale laser-generated electron beams at ultrahigh dose rates of 1010 to1011 Gy/s. Preliminary results indicate, within statistical uncertainties, a significant increase of the α parameter, a possible indication of more complex damage induced by a higher density of ionizing tracks.

Theoretical evaluation of the impact of diverse treatment conditions by calculation of the tumor control probability (TCP) of simulated cervical cancer Hyperthermia-Radiotherapy (HT-RT) treatments in-silico

INTRODUCTION

Hyperthermia (HT) induces various cellular biological processes, such as repair impairment and direct HT cell killing. In this context, in-silico biophysical models that translate deviations in the treatment conditions into clinical outcome variations may be used to study the extent of such processes and their influence on combined hyperthermia plus radiotherapy (HT + RT) treatments under varying conditions.

METHODS

An extended linear-quadratic model calibrated for SiHa and HeLa cell lines (cervical cancer) was used to theoretically study the impact of varying HT treatment conditions on radiosensitization and direct HT cell killing effect. Simulated patients were generated to compute the Tumor Control Probability (TCP) under different HT conditions (number of HT sessions, temperature and time interval), which were randomly selected within margins based on reported patient data.

RESULTS

Under the studied conditions, model-based simulations suggested a treatment improvement with a total CEM43 thermal dose of approximately 10 min. Additionally, for a given thermal dose, TCP increased with the number of HT sessions. Furthermore, in the simulations, we showed that the TCP dependence on the temperature/time interval is more correlated with the mean value than with the minimum/maximum value and that comparing the treatment outcome with the mean temperature can be an excellent strategy for studying the time interval effect.

CONCLUSION

The use of thermoradiobiological models allows us to theoretically study the impact of varying thermal conditions on HT + RT treatment outcomes. This approach can be used to optimize HT treatments, design clinical trials, and interpret patient data.

Dosiomics and radiomics-based prediction of pneumonitis after radiotherapy and immune checkpoint inhibition: The relevance of fractionation

OBJECTIVES

Post-therapy pneumonitis (PTP) is a relevant side effect of thoracic radiotherapy and immunotherapy with checkpoint inhibitors (ICI). The influence of the combination of both, including dose fractionation schemes on PTP development is still unclear. This study aims to improve the PTP risk estimation after radio(chemo)therapy (R(C)T) for lung cancer with and without ICI by investigation of the impact of dose fractionation on machine learning (ML)-based prediction.

MATERIALS AND METHODS

Data from 100 patients who received fractionated R(C)T were collected. 39 patients received additional ICI therapy. Computed Tomography (CT), RT segmentation and dose data were extracted and physical doses were converted to 2-Gy equivalent doses (EQD2) to account for different fractionation schemes. Features were reduced using Pearson intercorrelation and the Boruta algorithm within 1000-fold bootstrapping. Six single (clinics, Dose Volume Histogram (DVH), ICI, chemotherapy, radiomics, dosiomics) and four combined models (radiomics + dosiomics, radiomics + DVH + Clinics, dosiomics + DVH + Clinics, radiomics + dosiomics + DVH + Clinics) were trained to predict PTP. Dose-based models were tested using physical dose and EQD2. Four ML-algorithms (random forest (rf), logistic elastic net regression, support vector machine, logitBoost) were trained and tested using 5-fold nested cross validation and Synthetic Minority Oversampling Technique (SMOTE) for resampling in R. Prediction was evaluated using the area under the receiver operating characteristic curve (AUC) on the test sets of the outer folds.

RESULTS

The combined model of all features using EQD2 surpassed all other models (AUC = 0.77, Confidence Interval CI 0.76-0.78). DVH, clinical data and ICI therapy had minor impact on PTP prediction with AUC values between 0.42 and 0.57. All EQD2-based models outperformed models based on physical dose.

CONCLUSIONS

Radiomics + dosiomics based ML models combined with clinical and dosimetric models were found to be suited best for PTP prediction after R(C)T and could improve pre-treatment decision making. Different RT dose fractionation schemes should be considered for dose-based ML approaches.

Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

Mechanistic model of radiotherapy-induced lung fibrosis using coupled 3D agent-based and Monte Carlo simulations

BACKGROUND

Mechanistic modelling of normal tissue toxicities is unfolding as an alternative to the phenomenological normal tissue complication probability models. The latter, currently used in the clinics, rely exclusively on limited patient data and neglect spatial dose distribution information. Among the various approaches, agent-based models are appealing as they provide the means to include patient-specific parameters and simulate long-term effects in complex systems. However, Monte Carlo tools remain the state-of-the-art for modelling radiation transport and provide measurements of the delivered dose with unmatched precision.

METHODS

In this work, we develop and characterize a coupled 3D agent-based - Monte Carlo model that mechanistically simulates the onset of the radiation-induced lung fibrosis in an alveolar segment. To the best of our knowledge, this is the first such model.

RESULTS

Our model replicates extracellular matrix patterns, radiation-induced lung fibrosis severity indexes and functional subunits survivals that show qualitative agreement with experimental studies and are consistent with our past results. Moreover, in accordance with experimental results, higher functional subunits survival and lower radiation-induced lung fibrosis severity indexes are achieved when a 5-fractions treatment is simulated. Finally, the model shows increased sensitivity to more uniform protons dose distributions with respect to more heterogeneous ones from photon irradiation.

CONCLUSIONS

This study lays thus the groundwork for further investigating the effects of different radiotherapeutic treatments on the onset of radiation-induced lung fibrosis via mechanistic modelling.

Integrating microdosimetricin vitroRBE models for particle therapy into TOPAS MC using the MicrOdosimetry-based modeliNg for RBE ASsessment (MONAS) tool

Objective.In this paper, we present MONAS (MicrOdosimetry-based modelliNg for relative biological effectiveness (RBE) ASsessment) toolkit. MONAS is a TOPAS Monte Carlo extension, that combines simulations of microdosimetric distributions with radiobiological microdosimetry-based models for predicting cell survival curves and dose-dependent RBE.Approach.MONAS expands TOPAS microdosimetric extension, by including novel specific energy scorers to calculate the single- and multi-event specific energy microdosimetric distributions at different micrometer scales. These spectra are used as physical input to three different formulations of themicrodosimetric kinetic model, and to thegeneralized stochastic microdosimetric model(GSM2), to predict dose-dependent cell survival fraction and RBE. MONAS predictions are then validated against experimental microdosimetric spectra andin vitrosurvival fraction data. To show the MONAS features, we present two different applications of the code: (i) the depth-RBE curve calculation from a passively scattered proton SOBP and monoenergetic12C-ion beam by using experimentally validated spectra as physical input, and (ii) the calculation of the 3D RBE distribution on a real head and neck patient geometry treated with protons.Main results.MONAS can estimate dose-dependent RBE and cell survival curves from experimentally validated microdosimetric spectra with four clinically relevant radiobiological models. From the radiobiological characterization of a proton SOBP and12C fields, we observe the well-known trend of increasing RBE values at the distal edge of the radiation field. The 3D RBE map calculated confirmed the trend observed in the analysis of the SOBP, with the highest RBE values found in the distal edge of the target.Significance.MONAS extension offers a comprehensive microdosimetry-based framework for assessing the biological effects of particle radiation in both research and clinical environments, pushing closer the experimental physics-based description to the biological damage assessment, contributing to bridging the gap between a microdosimetric description of the radiation field and its application in proton therapy treatment with variable RBE.

Mechanistic modelling of relative biological effectiveness of carbon ion beams and comparison with experiments

Objective.Relative biological effectiveness (RBE) plays a vital role in carbon ion radiotherapy, which is a promising treatment method for reducing toxic effects on normal tissues and improving treatment efficacy. It is important to have an effective and precise way of obtaining RBE values to support clinical decisions. A method of calculating RBE from a mechanistic perspective is reported.Approach.Ratio of dose to obtain the same number of double strand breaks (DSBs) between different radiation types was used to evaluate RBE. Package gMicroMC was used to simulate DSB yields. The DSB inductions were then analyzed to calculate RBE. The RBE values were compared with experimental results.Main results.Furusawa's experiment yielded RBE values of 1.27, 2.22, 3.00 and 3.37 for carbon ion beam with dose-averaged LET of 30.3 keVμm-1, 54.5 keVμm-1, 88 keVμm-1and 137 keVμm-1, respectively. RBE values computed from gMicroMC simulations were 1.75, 2.22, 2.87 and 2.97. When it came to a more sophisticated carbon ion beam with 6 cm spread-out Bragg peak, RBE values were 1.61, 1.63, 2.19 and 2.36 for proximal, middle, distal and distal end part, respectively. Values simulated by gMicroMC were 1.50, 1.87, 2.19 and 2.34. The simulated results were in reasonable agreement with the experimental data.Significance.As a mechanistic way for the evaluation of RBE for carbon ion radiotherapy by combining the macroscopic simulation of energy spectrum and microscopic simulation of DNA damages, this work provides a promising tool for RBE calculation supporting clinical applications such as treatment planning.

In Vivo Microbeam Radiation Therapy at a Conventional Small Animal Irradiator

Microbeam radiation therapy (MRT) is a still pre-clinical form of spatially fractionated radiotherapy, which uses an array of micrometer-wide, planar beams of X-ray radiation. The dose modulation in MRT has proven effective in the treatment of tumors while being well tolerated by normal tissue. Research on understanding the underlying biological mechanisms mostly requires large third-generation synchrotrons. In this study, we aimed to develop a preclinical treatment environment that would allow MRT independent of synchrotrons. We built a compact microbeam setup for pre-clinical experiments within a small animal irradiator and present in vivo MRT application, including treatment planning, dosimetry, and animal positioning. The brain of an immobilized mouse was treated with MRT, excised, and immunohistochemically stained against γH2AX for DNA double-strand breaks. We developed a comprehensive treatment planning system by adjusting an existing dose calculation algorithm to our setup and attaching it to the open-source software 3D-Slicer. Predicted doses in treatment planning agreed within 10% with film dosimetry readings. We demonstrated the feasibility of MRT exposures in vivo at a compact source and showed that the microbeam pattern is observable in histological sections of a mouse brain. The platform developed in this study will be used for pre-clinical research of MRT.

A Review of Numerical Models of Radiation Injury and Repair Considering Subcellular Targets and the Extracellular Microenvironment

Astronauts in space are subject to continuous exposure to ionizing radiation. There is concern about the acute and late-occurring adverse health effects that astronauts could incur following a protracted exposure to the space radiation environment. Therefore, it is vital to consider the current tools and models used to describe and study the organic consequences of ionizing radiation exposure. It is equally important to see where these models could be improved. Historically, radiobiological models focused on how radiation damages nuclear deoxyribonucleic acid (DNA) and the role DNA repair mechanisms play in resulting biological effects, building on the hypotheses of Crowther and Lea from the 1940s and 1960s, and they neglected other subcellular targets outside of nuclear DNA. The development of these models and the current state of knowledge about radiation effects impacting astronauts in orbit, as well as how the radiation environment and cellular microenvironment are incorporated into these radiobiological models, aid our understanding of the influence space travel may have on astronaut health. It is vital to consider the current tools and models used to describe the organic consequences of ionizing radiation exposure and identify where they can be further improved.

Simulating tumor volume dynamics in response to radiotherapy: Implications of model selection

From the beginning of the usage of radiotherapy (RT) for cancer treatment, mathematical modeling has been integral to understanding radiobiology and for designing treatment approaches and schedules. There has been extensive modeling of response to RT with the inclusion of various degrees of biological complexity. In this study, we compare three models of tumor volume dynamics: (1) exponential growth with RT directly reducing tumor volume, (2) logistic growth with direct tumor volume reduction, and (3) logistic growth with RT reducing the tumor carrying capacity with the objective of understanding the implications of model selection and informing the process of model calibration and parameterization. For all three models, we: examined the rates of change in tumor volume during and RT treatment course; performed parameter sensitivity and identifiability analyses; and investigated the impact of the parameter sensitivity on the tumor volume trajectories. In examining the tumor volume dynamics trends, we coined a new metric - the point of maximum reduction of tumor volume (MRV) - to quantify the magnitude and timing of the expected largest impact of RT during a treatment course. We found distinct timing differences in MRV, dependent on model selection. The parameter identifiability and sensitivity analyses revealed the interdependence of the different model parameters and that it is only possible to independently identify tumor growth and radiation response parameters if the underlying tumor growth rate is sufficiently large. Ultimately, the results of these analyses help us to better understand the implications of model selection while simultaneously generating falsifiable hypotheses about MRV timing that can be tested on longitudinal measurements of tumor volume from pre-clinical or clinical data with high acquisition frequency. Although, our study only compares three particular models, the results demonstrate that caution is necessary in selecting models of response to RT, given the artifacts imposed by each model.

Functional information guided adaptive radiation therapy

Introduction

Functional informaton is introduced as the mechanism to adapt cancer therapies uniquely to individual patients based on changes defined by qualified tumor biomarkers.

Methods

To demonstrate the methodology, a tumor volume biomarker model, characterized by a tumor volume reduction rate coefficient, is used to adapt a tumor cell survival bioresponse radiotherapy model in terms of therapeutic radiation dose. Tumor volume, acquired from imaging data, serves as a surrogate measurement for tumor cell death, but the biomarker model derived from this data cannot be used to calculate the radiation dose absorbed by the target tumor. However, functional information does provide a mathematical connection between the tumor volume biomarker model and the tumor cell survival bioresponse model by quantifying both data sets in the units of information, thus creating an analytic conduit from bioresponse to biomarker.

Results

The information guided process for individualized dose adaptations using information values acquired from the tumor cell survival bioresponse model and the tumor volume biomarker model are presented in detailed form by flowchart and tabular data. Clinical data are used to generate a presentation that assists investigator application of the information guided methodology to adaptive cancer therapy research.

Conclusions

Information guided adaptation of bioresponse using surrogate data is extensible across multiple research fields because functional information mathematically connects disparate bioresponse and biomarker data sets. Thus, functional information offers adaptive cancer therapy by mathematically connecting immunotherapy, chemotherapy, and radiotherapy cancer treatment processes to implement individualized treatment plans.

Role of the mechanical microenvironment on CD-44 expression of breast adenocarcinoma in response to radiotherapy

The biological effects of ionizing radiation are exploited in the clinical practice of radiotherapy to destroy tumour cells while sparing the surrounding normal tissue. While most of the radiotherapy research focused on DNA damage and repair, recently a great attention is going to cells' interactions with the mechanical microenvironment of both malignant and healthy tissues after exposure. In fact, the stiffness of the extracellular matrix can modify cells' motility and spreading through the modulation of transmembrane proteins and surface receptors' expression, such as CD-44. CD-44 receptor has held much interest also in targeted-therapy due to its affinity with hyaluronic acid, which can be used to functionalize biodegradable nanoparticles loaded with chemotherapy drugs for targeted therapy. We evaluated changes in CD-44 expression in two mammary carcinoma cell lines (MCF10A and MDA-MB-231) after exposure to X-ray (2 or 10 Gy). To explore the role of the mechanical microenvironment, we mimicked tissues' stiffness with polyacrylamide's substrates producing two different elastic modulus values (0.5 and 15 kPa). We measured a dose dependent increase in CD-44 relative expression in tumour cells cultured in a stiffer microenvironment. These findings highlight a crucial connection between the mechanical properties of the cell's surroundings and the post-radiotherapy expression of surface receptors.

"Primer shot" fractionation with an early treatment break is theoretically superior to consecutive weekday fractionation schemes for early-stage non-small cell lung cancer

PURPOSE

Radiotherapy is traditionally given in equally spaced weekday fractions. We hypothesize that heterogeneous interfraction intervals can increase radiosensitivity via reoxygenation. Through modeling, we investigate whether this minimizes local failures and toxicity for early-stage non-small cell lung cancer (NSCLC).

METHODS

Previously, a tumor dose-response model based on resource competition and cell-cycle-dependent radiosensitivity accurately predicted local failure rates for early-stage NSCLC cohorts. Here, the model mathematically determined non-uniform inter-fraction intervals minimizing local failures at similar normal tissue toxicity risk, i.e., iso-BED3 (iso-NTCP) for fractionation schemes 18Gyx3, 12Gyx4, 10Gyx5, 7.5Gyx8, 5Gyx12, 4Gyx15. Next, we used these optimized schedules to reduce toxicity risk (BED3) while maintaining stable local failures (TCP).

RESULTS

Optimal schedules consistently favored a "primer shot" fraction followed by a 2-week break, allowing tumor reoxygenation. Increasing or decreasing the assumed baseline hypoxia extended or shortened this optimal break by up to one week. Fraction sizes of 7.5 Gy and up required a single primer shot, while smaller fractions needed one or two extra fractions for full reoxygenation. The optimized schedules, versus consecutive weekday fractionation, predicted absolute LF reductions of 4.6%-7.4%, except for the already optimal LF rate seen for 18Gyx3. Primer shot schedules could also reduce BED3 at iso-TCP with the biggest improvements for the shortest schedules (94.6Gy reduction for 18Gyx3).

CONCLUSION

A validated simulation model clearly supports non-standard "primer shot" fractionation, reducing the impact of hypoxia-induced radioresistance. A limitation of this study is that primer-shot fractionation is outside prior clinical experience and therefore will require clinical studies for definitive testing.

Computational approaches in the estimation of radiobiological damage for human-malignant cells irradiated with clinical proton and carbon beams

PURPOSE

The use of Monte Carlo (MC) simulations capable of reproducing radiobiological effects of ionising radiation on human cell lines is of great importance, especially for cases involving protons and heavier ion beams. In the latter, huge uncertainties can arise mainly related to the effects of the secondary particles produced in the beam-tissue interaction. This paper reports on a detailed MC study performed using Geant4-based approach on three cancer cell lines, the HTB-177, CRL-5876 and MCF-7, that were previously irradiated with therapeutic proton and carbon ion beams.

METHODS

A Geant4-based approach used jointly with analytical calculations has been developed to provide a more realistic estimation of the radiobiological damage produced by proton and carbon beams in tissues, reproducing available data obtained from in vitro cell irradiations. The MC "Hadrontherapy" Geant4 application and the Local Effect Model: LEM I, LEM II and LEM III coupled with the different numerical approaches: RapidRusso (RR) and RapidScholz (RS) were used in the study.

RESULTS

Experimental survival curves are compared with those evaluated using the highlighted Geant4 MC-based approach via chi-square statistical analysis, for the combinations of radiobiological models and numerical approaches, as outlined above.

CONCLUSION

This study has presented a comparison of the survival data from MC simulations to experimental survival data for three cancer cell lines. An overall best level of agreement was obtained for the HTB-177 cells.

Variable RBE in proton radiotherapy: a comparative study with the predictive Mayo Clinic Florida microdosimetric kinetic model and phenomenological models of cell survival

Objectives. (1) To examine to what extent the cell- and exposure- specific information neglected in the phenomenological proton relative biological effectiveness (RBE) models could influence the computed RBE in proton therapy. (2) To explore similarities and differences in the formalism and the results between the linear energy transfer (LET)-based phenomenological proton RBE models and the microdosimetry-based Mayo Clinic Florida microdosimetric kinetic model (MCF MKM). (3) To investigate how the relationship between the RBE and the dose-mean proton LET is affected by the proton energy spectrum and the secondary fragments.Approach. We systematically compared six selected phenomenological proton RBE models with the MCF MKM in track-segment simulations, monoenergetic proton beams in a water phantom, and two spread-out Bragg peaks. A representative comparison within vitrodata for human glioblastoma cells (U87 cell line) is also included.Main results. Marked differences were observed between the results of the phenomenological proton RBE models, as reported in previous studies. The dispersion of these models' results was found to be comparable to the spread in the MCF MKM results obtained by varying the cell-specific parameters neglected in the phenomenological models. Furthermore, while single cell-specific correlation between RBE and the dose-mean proton LET seems reasonable above 2 keVμm-1, caution is necessary at lower LET values due to the relevant contribution of secondary fragments. The comparison within vitrodata demonstrates comparable agreement between the MCF MKM predictions and the results of the phenomenological models.Significance. The study highlights the importance of considering cell-specific characteristics and detailed radiation quality information for accurate RBE calculations in proton therapy. Furthermore, these results provide confidence in the use of the MCF MKM for clonogenic survival RBE calculations in proton therapy, offering a more mechanistic approach compared to phenomenological models.

Impact of Modern Low Dose Involved Site Radiation Therapy on Normal Tissue Toxicity in Cervicothoracic Non-Hodgkin Lymphomas: A Biophysical Study

This biophysical study aimed to determine fitting parameters for the Lyman-Kutcher-Burman (LKB) dose-response model for normal tissue complication probability (NTCP) calculations of acute side effects and to investigate the impact of reduced radiation doses on the probability of their occurrence in supradiaphragmatic non-Hodgkin lymphoma (NHL) irradiation. A cohort of 114 patients with NHL in the cervicothoracic region, treated between 2015 and 2021 at the University Hospitals of Münster, Hamburg, and Essen, with involved site radiation therapy (ISRT), were included. Among them, 68 patients with aggressive NHL (a-NHL) received consolidative radiation therapy with 24-54 Gy following (R-)CHOP chemotherapy. Additionally, 46 patients with indolent NHL (i-NHL) underwent radiotherapy with 22.5-45.0 Gy. Two treatment plans were prospectively created for each patient (a-NHL: 30.0/40.0 Gy; i-NHL: 24.0/30.0 Gy). NTCP were then calculated using the optimized LKB model. The adapted dose-response models properly predicted the patient's probability of developing acute side effects when receiving doses ≤ 50 Gy. In addition, it was shown that reduced radiation doses can influence the NTCP of acute side effects depending on the aggressiveness of NHL significantly. This study provided a foundation to prospectively assess the probability of adverse side effects among today's reduced radiation doses in the treatment of NHL.

X-ray radio-enhancement by Ti3C2Tx MXenes in soft tissue sarcoma

Radiotherapy is a cornerstone of cancer treatment. However, due to the low tissue specificity of ionizing radiation, damage to the surrounding healthy tissue of the tumor remains a significant challenge. In recent years, radio-enhancers based on inorganic nanomaterials have gained considerable interest. Beyond the widely explored metal and metal oxide nanoparticles, 2D materials, such as MXenes, could present potential benefits because of their inherently large specific surface area. In this study, we highlight the promising radio-enhancement properties of Ti3C2Tx MXenes. We demonstrate that atomically thin layers of titanium carbides (Ti3C2Tx MXenes) are efficiently internalized and well-tolerated by mammalian cells. Contrary to MXenes suspended in aqueous buffers, which fully oxidize within days, yielding rice-grain shaped rutile nanoparticles, the MXenes internalized by cells oxidize at a slower rate. This is consistent with cell-free experiments that have shown slower oxidation rates in cell media and lysosomal buffers compared to dispersants without antioxidants. Importantly, the MXenes exhibit robust radio-enhancement properties, with dose enhancement factors reaching up to 2.5 in human soft tissue sarcoma cells, while showing no toxicity to healthy human fibroblasts. When compared to oxidized MXenes and commercial titanium dioxide nanoparticles, the intact 2D titanium carbide flakes display superior radio-enhancement properties. In summary, our findings offer evidence for the potent radio-enhancement capabilities of Ti3C2Tx MXenes, marking them as a promising candidate for enhancing radiotherapy.

Effects of Differing Underlying Assumptions in In Silico Models on Predictions of DNA Damage and Repair

The induction and repair of DNA double-strand breaks (DSBs) are critical factors in the treatment of cancer by radiotherapy. To investigate the relationship between incident radiation and cell death through DSB induction many in silico models have been developed. These models produce and use custom formats of data, specific to the investigative aims of the researchers, and often focus on particular pairings of damage and repair models. In this work we use a standard format for reporting DNA damage to evaluate combinations of different, independently developed, models. We demonstrate the capacity of such inter-comparison to determine the sensitivity of models to both known and implicit assumptions. Specifically, we report on the impact of differences in assumptions regarding patterns of DNA damage induction on predicted initial DSB yield, and the subsequent effects this has on derived DNA repair models. The observed differences highlight the importance of considering initial DNA damage on the scale of nanometres rather than micrometres. We show that the differences in DNA damage models result in subsequent repair models assuming significantly different rates of random DSB end diffusion to compensate. This in turn leads to disagreement on the mechanisms responsible for different biological endpoints, particularly when different damage and repair models are combined, demonstrating the importance of inter-model comparisons to explore underlying model assumptions.

Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes

Rhenium-186 (186Re) labeled nanoliposome (RNL) therapy for recurrent glioblastoma patients has shown promise to improve outcomes by locally delivering radiation to affected areas. To optimize the delivery of RNL, we have developed a framework to predict patient-specific response to RNL using image-guided mathematical models.

Methods

We calibrated a family of reaction-diffusion type models with multi-modality imaging data from ten patients (NCR01906385) to predict the spatio-temporal dynamics of each patient's tumor. The data consisted of longitudinal magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT) to estimate tumor burden and local RNL activity, respectively. The optimal model from the family was selected and used to predict future growth. A simplified version of the model was used in a leave-one-out analysis to predict the development of an individual patient's tumor, based on cohort parameters.

Results

Across the cohort, predictions using patient-specific parameters with the selected model were able to achieve Spearman correlation coefficients (SCC) of 0.98 and 0.93 for tumor volume and total cell number, respectively, when compared to the measured data. Predictions utilizing the leave-one-out method achieved SCCs of 0.89 and 0.88 for volume and total cell number across the population, respectively.

Conclusion

We have shown that patient-specific calibrations of a biology-based mathematical model can be used to make early predictions of response to RNL therapy. Furthermore, the leave-one-out framework indicates that radiation doses determined by SPECT can be used to assign model parameters to make predictions directly following the conclusion of RNL treatment.

Statement of Significance

This manuscript explores the application of computational models to predict response to radionuclide therapy in glioblastoma. There are few, to our knowledge, examples of mathematical models used in clinical radionuclide therapy. We have tested a family of models to determine the applicability of different radiation coupling terms for response to the localized radiation delivery. We show that with patient-specific parameter estimation, we can make accurate predictions of future glioblastoma response to the treatment. As a comparison, we have shown that population trends in response can be used to forecast growth from the moment the treatment has been delivered.In addition to the high simulation and prediction accuracy our modeling methods have achieved, the evaluation of a family of models has given insight into the response dynamics of radionuclide therapy. These dynamics, while different than we had initially hypothesized, should encourage future imaging studies involving high dosage radiation treatments, with specific emphasis on the local immune and vascular response.

Evaluation of in vitro intrinsic radiosensitivity and characterization of five canine high-grade glioma cell lines

Glioma is the most common primary brain tumor in dogs and predominantly affects brachycephalic breeds. Diagnosis relies on CT or MRI imaging, and the proposed treatments include surgical resection, chemotherapy, and radiotherapy depending on the tumor's location. Canine glioma from domestic dogs could be used as a more powerful model to study radiotherapy for human glioma than the murine model. Indeed, (i) contrary to mice, immunocompetent dogs develop spontaneous glioma, (ii) the canine brain structure is closer to human than mice, and (iii) domestic dogs are exposed to the same environmental factors than humans. Moreover, imaging techniques and radiation therapy used in human medicine can be applied to dogs, facilitating the direct transposition of results. The objective of this study is to fully characterize 5 canine glioma cell lines and to evaluate their intrinsic radiosensitivity. Canine cell lines present numerous analogies between the data obtained during this study on different glioma cell lines in dogs. Cell morphology is identical, such as doubling time, clonality test and karyotype. Immunohistochemical study of surface proteins, directly on cell lines and after stereotaxic injection in mice also reveals close similarity. Radiosensitivity profile of canine glial cells present high profile of radioresistance.

Association of Radiation Dose to the Amygdala-Orbitofrontal Network with Emotion Recognition Task Performance in Patients with Low-Grade and Benign Brain Tumors

BACKGROUND

Although data are limited, difficulty in social cognition occurs in up to 83% of patients with brain tumors. It is unknown whether cranial radiation therapy (RT) dose to the amygdala-orbitofrontal network can impact social cognition.

METHODS

We prospectively enrolled 51 patients with low-grade and benign brain tumors planned for cranial RT. We assessed longitudinal changes on an emotion recognition task (ERT) that measures the ability to recognize emotional states by displaying faces expressing six basic emotions and their association with the RT dose to the amygdala-orbitofrontal network. ERT outcomes included the median time to choose a response (ERTOMDRT) or correct response (ERTOMDCRT) and total correct responses (ERTHH).

RESULTS

The RT dose to the amygdala-orbitofrontal network was significantly associated with longer median response times on the ERT. Increases in median response times occurred at lower doses than decreases in total correct responses. The medial orbitofrontal cortex was the most important variable on regression trees predicting change in the ERTOMDCRT.

DISCUSSION

This is, to our knowledge, the first study to show that off-target RT dose to the amygdala-orbitofrontal network is associated with performance on a social cognition task, a facet of cognition that has previously not been mechanistically studied after cranial RT.

Risk of Second Tumors in Retinoblastoma Survivors after Ionizing Radiation: A Review

Retinoblastoma (RB) is the most common ocular neoplasm in children, whose development depends on two mutational events that occur in both alleles of the retinoblastoma susceptibility gene (RB1). Regarding the nature of these mutational events, RB can be classified as hereditary if the first event is a germline mutation and the second one is a somatic mutation in retina cells or nonhereditary if both mutational events occur in somatic cells. Although the rate of survival of RB is significantly elevated, the incidence of second malignant neoplasms (SMNs) is a concern, since SMNs are the main cause of death in these patients. Effectively, RB patients present a higher risk of SMN incidence compared to other oncology patients. Furthermore, evidence confirms that hereditary RB survivors are at a higher risk for SMNs than nonhereditary RB survivors. Over the decades, some studies have been performed to better understand this subject, evaluating the risk of the development of SMNs in RB patients. Furthermore, this risk seems to increase with the use of ionizing radiation in some therapeutic approaches commonly used in the treatment of RB. This review aims to clarify the effect of ionizing radiation in RB patients and to understand the association between the risk of SMN incidence in patients that underwent radiation therapy, especially in hereditary RB individuals.

Lesion Dosimetry for [177Lu]Lu-PSMA-617 Radiopharmaceutical Therapy Combined with Stereotactic Body Radiotherapy in Patients with Oligometastatic Castration-Sensitive Prostate Cancer

A single-institution prospective pilot clinical trial was performed to demonstrate the feasibility of combining [177Lu]Lu-PSMA-617 radiopharmaceutical therapy (RPT) with stereotactic body radiotherapy (SBRT) for the treatment of oligometastatic castration-sensitive prostate cancer. Methods: Six patients with 9 prostate-specific membrane antigen (PSMA)-positive oligometastases received 2 cycles of [177Lu]Lu-PSMA-617 RPT followed by SBRT. After the first intravenous infusion of [177Lu]Lu-PSMA-617 (7.46 ± 0.15 GBq), patients underwent SPECT/CT at 3.2 ± 0.5, 23.9 ± 0.4, and 87.4 ± 12.0 h. Voxel-based dosimetry was performed with calibration factors (11.7 counts per second/MBq) and recovery coefficients derived from in-house phantom experiments. Lesions were segmented on baseline PSMA PET/CT (50% SUVmax). After a second cycle of [177Lu]Lu-PSMA-617 (44 ± 3 d; 7.50 ± 0.10 GBq) and an interim PSMA PET/CT scan, SBRT (27 Gy in 3 fractions) was delivered to all PSMA-avid oligometastatic sites, followed by post-PSMA PET/CT. RPT and SBRT voxelwise dose maps were scaled (α/β = 3 Gy; repair half-time, 1.5 h) to calculate the biologically effective dose (BED). Results: All patients completed the combination therapy without complications. No grade 3+ toxicities were noted. The median of the lesion SUVmax as measured on PSMA PET was 16.8 (interquartile range [IQR], 11.6) (baseline), 6.2 (IQR, 2.7) (interim), and 2.9 (IQR, 1.4) (post). PET-derived lesion volumes were 0.4-1.7 cm3 The median lesion-absorbed dose (AD) from the first cycle of [177Lu]Lu-PSMA-617 RPT (ADRPT) was 27.7 Gy (range, 8.3-58.2 Gy; corresponding to 3.7 Gy/GBq, range, 1.1-7.7 Gy/GBq), whereas the median lesion AD from SBRT was 28.1 Gy (range, 26.7-28.8 Gy). Spearman rank correlation, ρ, was 0.90 between the baseline lesion PET SUVmax and SPECT SUVmax (P = 0.005), 0.74 (P = 0.046) between the baseline PET SUVmax and the lesion ADRPT, and -0.81 (P = 0.022) between the lesion ADRPT and the percent change in PET SUVmax (baseline to interim). The median for the lesion BED from RPT and SBRT was 159 Gy (range, 124-219 Gy). ρ between the BED from RPT and SBRT and the percent change in PET SUVmax (baseline to post) was -0.88 (P = 0.007). Two cycles of [177Lu]Lu-PSMA-617 RPT contributed approximately 40% to the maximum BED from RPT and SBRT. Conclusion: Lesional dosimetry in patients with oligometastatic castration-sensitive prostate cancer undergoing [177Lu]Lu-PSMA-617 RPT followed by SBRT is feasible. Combined RPT and SBRT may provide an efficient method to maximize the delivery of meaningful doses to oligometastatic disease while addressing potential microscopic disease reservoirs and limiting the dose exposure to normal tissues.

Importance of tumor volume, overall treatment time and fractionation sensitivity for p16-positive and p16-negative oropharyngeal tumors

BACKGROUND

Analyses of clinical outcomes following radiotherapy (RT) have advanced our understanding of fundamental radiobiological characteristics in head and neck squamous cell carcinoma (HNSCC). Low fractionation sensitivity appears to be a common feature, as well as susceptibility to changes in overall treatment time (OTT). Large tumors should be harder to cure if a successful RT requires the sterilization of all clonogenic cells. Congruently, primary tumor volume has proven to be an important parameter. However, most findings come from an era when p16-negative HNSCC was the dominant tumor type. HPV-associated, p16-positive, oropharyngeal tumors (OPSCC) are more radiosensitive and have better outcome. The current study aims to investigate the role of primary tumor volume, OTT and estimate α/ β -ratio for p16-positive OPSCC, and to quantify the differences in radiosensitivity depending on p16-status.

METHODS

A cohort of 523 patients treated with RT was studied using a tumor control probability (TCP)-model that incorporates primary tumor volume (V) raised to an exponent c, OTT and α/ β -estimation. The significance of V was also investigated in Cox-regression models.

RESULTS

In the p16-positive cohort (n = 433), the volume exponent c was 1.44 (95%CI 1.06-1.91), compared to 0.90 (0.54-1.32) for p16-negative tumors (n = 90). Hazard ratios per tumor volume doubling were 2.37 (1.72-3.28) and 1.83 (1.28-2.62) for p16-positive and p16-negative, respectively. The estimated α/ β -ratio was 9.7 Gy (-2.3-21.6), and a non-significant daily loss of 0.30 Gy (-0.17-0.92) was found. An additional dose of 6.8 Gy (interquartile range 4.8-9.1) may theoretically counteract the more radioresistant behavior of p16-negative tumors.

CONCLUSION

Primary tumor volume plays a crucial role in predicting local tumor response, particularly in p16-positive OPSCC. The estimated α/β-ratio for p16-positive oropharyngeal tumors aligns with previous HNSCC studies, whereas the impact of prolonged OTT was slightly less than previously reported. The differences in radiosensitivity depending on p16-status were quantified. The findings should be validated in independent cohorts.

Single fraction and hypofractionated radiosurgery for perioptic meningiomas-tumor control and visual outcomes: a systematic review and meta-analysis

Perioptic meningiomas, defined as those that are less than 3 mm from the optic apparatus, are challenging to treat with stereotactic radiosurgery (SRS). Tumor control must be weighed against the risk of radiation-induced optic neuropathy (RION), as both tumor progression and RION can lead to visual decline. We performed a systematic review and meta-analysis of single fraction SRS and hypofractionated radiosurgery (hfRS) for perioptic meningiomas, evaluating tumor control and visual preservation rates. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we reviewed articles published between 1968 and December 8, 2022. We retained 5 studies reporting 865 patients, 438 cases treated in single fraction, while 427 with hfRS. For single fraction SRS, the overall rate of tumor control was 95.1%, with actuarial rates at 5 and 10 years of 96% and 89%, respectively; tumor progression was 7.7%. The rate of visual stability was 90.4%, including visual improvement in 29.3%. The rate of visual decline was 9.6%, including blindness in 1.2%. For hfRS, the overall rate of tumor control was 95.6% (range 92.1-99.1, p < 0.001); tumor progression was 4.4% (range 0.9-7.9, p = 0.01). Overall rate of visual stability was 94.9% (range 90.9-98.9, p < 0.001), including visual improvement in 22.7% (range 5.0-40.3, p = 0.01); visual decline was 5.1% (range 1.1-9.1, p = 0.013). SRS is an effective and safe treatment option for perioptic meningiomas. Both hypofractionated regimens and single fraction SRS can be considered.

Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas

We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipatory personalized treatment that accounts for uncertainties in the underlying tumor biology in high-grade gliomas, where heterogeneity in the response to standard-of-care (SOC) radiotherapy contributes to sub-optimal patient outcomes. The digital twin is initialized through prior distributions derived from population-level clinical data in the literature for a mechanistic model's parameters. Then the digital twin is personalized using Bayesian model calibration for assimilating patient-specific magnetic resonance imaging data. The calibrated digital twin is used to propose optimal radiotherapy treatment regimens by solving a multi-objective risk-based optimization under uncertainty problem. The solution leads to a suite of patient-specific optimal radiotherapy treatment regimens exhibiting varying levels of trade-off between the two competing clinical objectives: (i) maximizing tumor control (characterized by minimizing the risk of tumor volume growth) and (ii) minimizing the toxicity from radiotherapy. The proposed digital twin framework is illustrated by generating an in silico cohort of 100 patients with high-grade glioma growth and response properties typically observed in the literature. For the same total radiation dose as the SOC, the personalized treatment regimens lead to median increase in tumor time to progression of around six days. Alternatively, for the same level of tumor control as the SOC, the digital twin provides optimal treatment options that lead to a median reduction in radiation dose by 16.7% (10 Gy) compared to SOC total dose of 60 Gy. The range of optimal solutions also provide options with increased doses for patients with aggressive cancer, where SOC does not lead to sufficient tumor control.

Mathematical modeling of radiotherapy: impact of model selection on estimating minimum radiation dose for tumor control

Introduction

Radiation therapy (RT) is one of the most common anticancer therapies. Yet, current radiation oncology practice does not adapt RT dose for individual patients, despite wide interpatient variability in radiosensitivity and accompanying treatment response. We have previously shown that mechanistic mathematical modeling of tumor volume dynamics can simulate volumetric response to RT for individual patients and estimation personalized RT dose for optimal tumor volume reduction. However, understanding the implications of the choice of the underlying RT response model is critical when calculating personalized RT dose.

Methods

In this study, we evaluate the mathematical implications and biological effects of 2 models of RT response on dose personalization: (1) cytotoxicity to cancer cells that lead to direct tumor volume reduction (DVR) and (2) radiation responses to the tumor microenvironment that lead to tumor carrying capacity reduction (CCR) and subsequent tumor shrinkage. Tumor growth was simulated as logistic growth with pre-treatment dynamics being described in the proliferation saturation index (PSI). The effect of RT was simulated according to each respective model for a standard schedule of fractionated RT with 2 Gy weekday fractions. Parameter sweeps were evaluated for the intrinsic tumor growth rate and the radiosensitivity parameter for both models to observe the qualitative impact of each model parameter. We then calculated the minimum RT dose required for locoregional tumor control (LRC) across all combinations of the full range of radiosensitvity and proliferation saturation values.

Results

Both models estimate that patients with higher radiosensitivity will require a lower RT dose to achieve LRC. However, the two models make opposite estimates on the impact of PSI on the minimum RT dose for LRC: the DVR model estimates that tumors with higher PSI values will require a higher RT dose to achieve LRC, while the CCR model estimates that higher PSI values will require a lower RT dose to achieve LRC.

Discussion

Ultimately, these results show the importance of understanding which model best describes tumor growth and treatment response in a particular setting, before using any such model to make estimates for personalized treatment recommendations.