The purpose of this work is to calculate individualized dose distributions in patients undergoing 18 F-FDG PET/CT studies through a methodology based on full Monte Carlo (MC) simulations and PET/CT patient images, and to compare such values with those obtained by employing nonindividualized phantom-based methods. Methods: We developed a MC-based methodology for individualized internal dose calculations, which relies on CT images (for organ segmentation and dose deposition), PET images (for organ segmentation and distributions of activities), and a biokinetic model (which works with information provided by PET and CT images) to obtain cumulated activities. The software vGATE version 8.1. was employed to carry out the Monte Carlo calculations. We also calculated deposited doses with nonindividualized phantom-based methods (Cristy-Eckerman, Stabin, and ICRP-133).

There is increasing evidence that high doses of radiotherapy, like those delivered in stereotactic body radiotherapy (SBRT), trigger indirect mechanisms of cell death. Such effect seems to be two-fold. High doses may trigger an immune response and may cause vascular damage, leading to cell starvation and death. Development of mathematical response models, including indirect death, may help clinicians to design SBRT optimal schedules. Despite increasing experimental literature on indirect tumor cell death caused by vascular damage, efforts on modeling this effect have been limited. In this work, we present a biomathematical model of this effect. In our model, tumor oxygenation is obtained by solving the reactiondiffusion equation; radiotherapy kills tumor cells according to the linear-quadratic model, and also endothelial cells (EC), which can trigger loss of functionality of capillaries. Capillary death will affect tumor oxygenation, driving nearby tumor cells into severe hypoxia. Capillaries can recover functionality due to EC proliferation. Tumor cells entering a predetermined severe hypoxia status die according to a hypoxia-death model. This model fits recently published experimental data showing the effect of vascular damage on surviving fractions. It fits surviving fraction curves and qualitatively reproduces experimental values of percentages of functional capillaries 48 hours postirradiation, and hypoxic cells pre-and 48 hours postirradiation. This model is useful for exploring aspects of tumor and EC response to radiotherapy and constitutes a stepping stone toward modeling indirect tumor cell death caused by vascular damage and accounting for this effect during SBRT planning.Significance: A novel biomathematical model of indirect tumor cell death caused by vascular radiation damage could potentially help clinicians interpret experimental data and design better radiotherapy schedules.

Background and purpose: To investigate the possible contribution of indirect damage and damage saturation to tumour control obtained with SBRT/SRS treatments for early-stage NSCLC and brain metastases. Methods and materials: We have constructed a dataset of early-stage NSCLC and brain metastases doseresponse. These data were fitted to models based on the linear-quadratic (LQ), the linear-quadratic-linear (LQL), and phenomenological modifications of the LQ-model to account for indirect cell damage. We use the Akaike-Information-Criterion formalism to compare performance, and studied the stability of the results with changes in fitting parameters and perturbations on dose/TCP values.Results: In NSCLC, a modified LQ-model with a beta-term increasing with dose yields the best-fits for a/ b = 10 Gy. Only the inclusion of very fast accelerated proliferation or low a/b values can eliminate such superiority. In brain, the LQL model yields the best-fits, and the ranking is not affected by variations of fitting parameters or dose/TCP perturbations.Conclusions: For a/b = 10 Gy, a modified LQ-model with a beta-term increasing with dose provides better fits to NSCLC dose-response curves. For brain metastases, the LQL provides the best fit. This might be interpreted as a hint of indirect damage in NSCLC, and damage saturation in brain metastases. The results for NSCLC are strongly dependent on the value of a/b and may require further investigation, while those for brain seem to be clearly significant. Our results can assist in the design of improved radiotherapy for NSCLC and brain metastases, aiming at avoiding over/under-treatment.

The linear-quadratic (LQ) model to describe the survival of irradiated cells may be the most frequently used biomathematical model in radiotherapy. There has been an intense debate on the mechanistic origin of the LQ model. An interesting approach is that of obtaining LQ-like behavior from kinetic models, systems of differential equations that model the induction and repair of damage. Development of such kinetic models is particularly interesting for application to continuous dose rate therapies, such as molecular radiotherapy or brachytherapy. In this work, we present a simple kinetic model that describes the kinetics of populations of tumor cells, rather than lethal/sub-lethal lesions, which may be especially useful for application to continuous dose rate therapies, as in molecular radiotherapy. The multi-compartment model consists of a set of three differential equations. The model incorporates in an easy way different cross-interacting compartments of cells forming a tumor, and may be of especial interest for studying dynamics of treated tumors. In the fast dose delivery limit, the model can be analytically solved, obtaining a simple closed-form expression. Fitting of several surviving curves with both this solution and the LQ model shows that they produce similar fits, despite being functionally different. We have also investigated the operation of the model in the continuous dose rate scenario, firstly by fitting preclinical data of tumor response to 131 I-CLR1404 therapy, and secondly by showing how damage repair and proliferation rates can cause a treatment to achieve control or not. Kinetic models like the one presented in this work may be of special interest when modeling response to molecular radiotherapy.

Diodes dosimeters present a complex response to pulsed beams, with diode sensitivity varying with dose-per-pulse, monitor unit rate (time between pulses) or number of integrated pulses. Such a response is caused by the complex kinetics of the interplay among charge carriers, recombination-generation centers, which capture excess minority charge carriers and facilitate recombination with a majority charge carrier, and traps with energy levels close to the conduction/valence band, which can trap and release charge carriers. This behavior has been well characterized experimentally, and modeled with phenomenological models. In this work we present a kinetic multi-compartment model of the response of diode detectors, which includes the interplay among charge carriers, recombination-generation centers, and traps. The model can qualitatively fit experimental data extracted from the literature on diode response versus dose-per-pulse, monitor unit rate (time between pulses), or number of integrated pulses. In this regard, our work provides further insight on the response of diode detectors, and a theoretical framework for the development of simple phenomenological models.

Purpose To obtain individualized internal doses with a Monte Carlo (MC) method in patients undergoing diagnostic [18F]FCH‐PET studies and to compare such doses with the MIRD method calculations. Methods A patient cohort of 17 males were imaged after intravenous administration of a mean [18F]FCH activity of 244.3 MBq. The resulting PET/CT images were processed in order to generate individualized input source and geometry files for dose computation with the MC tool GATE. The resulting dose estimates were studied and compared to the MIRD method with two different computational phantoms. Mass correction of the S‐factors was applied when possible. Potential sources of uncertainty were closely examined: the effect of partial body images, urinary bladder emptying, and biokinetic modeling. Results Large differences in doses between our methodology and the MIRD method were found, generally in the range ±25%, and up to ±120% for some cases. The mass scaling showed improvements, especially for non‐walled and high‐uptake tissues. Simulations of the urinary bladder emptying showed negligible effects on doses to other organs, with the exception of the prostate. Dosimetry based on partial PET/CT images (excluding the legs) resulted in an overestimation of mean doses to bone, skin, and remaining tissues, and minor differences in other organs/tissues. Estimated uncertainties associated with the biokinetics of FCH introduce variations of cumulated activities in the range of ±10% in the high‐uptake organs. Conclusions The MC methodology allows for a higher degree of dosimetry individualization than the MIRD methodology, which in some cases leads to important differences in dose values. Dosimetry of FCH‐PET based on a single partial PET study seems viable due to the particular biokinetics of FCH, even though some correction factors may need to be applied to estimate mean skin/bone doses.

No abstract

Background: In this work we aim to investigate the possible contribution of indirect damage and damage saturation to tumor control probabilities (TCP) obtained with SBRT/SRS treatments for early-stage NSCLC and brain metastases. Methods: We have constructed a dataset of early-stage NSCLC and brain metastases response to different fractionations. Dose-response curves were fitted to models based on the linear-quadratic (LQ), the linear-quadratic-linear (LQL), and phenomenological modifications of the LQ model to account for indirect cell damage. We used the Akaike-Information-Criterion formalism to compare performance, and studied the stability of the results with changes in fitting parameters and perturbations on dose/TCP values. Results: In NSCLC, a modified LQ model with a beta-term increasing with dose yields better results than the LQ model. This rank remains consistent when different fitting parameters are changed, and only the inclusion of very fast accelerated proliferation can eliminate the superiority of the modified LQ. In brain, the LQL model yields the best-fits, and the ranking is not affected by variations of fitting parameters or dose/TCP perturbations. Conclusions: A modification of the LQ model with a beta-term increasing with dose provides better fits to NSCLC dose-response curves. For brain metastases, the LQL provides the best fit. This may be interpreted as a net contribution of indirect damage in NSCLC, and damage saturation in brain metastases. The results for NSCLC are borderline significant, while those for brain are clearly significant. Our results can assist on the design of optimal radiotherapy for NSCLC and brain metastases, aiming at avoiding over/under-treatment. Dose prescription to such tumors may be re-evaluated according to the reported evidence.