LymphoDose: a lymphocyte dose estimation framework-application to brain radiotherapy.

Objective. Severe radiation-induced lymphopenia occurs in 40% of patients treated for primary brain tumors and is an independent risk factor of poor survival outcomes. We developed anin-silicoframework that estimates the radiation doses received by lymphocytes during volumetric modulated arc therapy brain irradiation.Approach. We implemented a simulation consisting of two interconnected compartmental models describing the slow recirculation of lymphocytes between lymphoid organs (M1) and the bloodstream (M2). We used dosimetry data from 33 patients treated with chemo-radiation for glioblastoma to compare three cases of the model, corresponding to different physical and biological scenarios: (H1) lymphocytes circulation only in the bloodstream i.e. circulation inM2only; (H2) lymphocytes recirculation between lymphoid organs i.e. circulation inM1andM2interconnected; (H3) lymphocytes recirculation between lymphoid organs and deep-learning computed out-of-field (OOF) dose to head and neck (H&N) lymphoid structures. A sensitivity analysis of the model's parameters was also performed.Main results. For H1, H2 and H3 cases respectively, the irradiated fraction of lymphocytes was 99.8 ± 0.7%, 40.4 ± 10.2% et 97.6 ± 2.5%, and the average dose to irradiated pool was 309.9 ± 74.7 mGy, 52.6 ± 21.1 mGy and 265.6 ± 48.5 mGy. The recirculation process considered in the H2 case implied that irradiated lymphocytes were irradiated in the field only 1.58 ± 0.91 times on average after treatment. The OOF irradiation of H&N lymphoid structures considered in H3 was an important contribution to lymphocytes dose. In all cases, the estimated doses are low compared with lymphocytes radiosensitivity, and other mechanisms could explain high prevalence of RIL in patients with brain tumors.Significance. Our framework is the first to take into account OOF doses and recirculation in lymphocyte dose assessment during brain irradiation. Our results demonstrate the need to clarify the indirect effects of irradiation on lymphopenia, in order to potentiate the combination of radio-immunotherapy or the abscopal effect.

Deep-Learning for Rapid Estimation of the Out-of-Field Dose in External Beam Photon Radiation Therapy - A Proof of Concept.

PURPOSE: The dose deposited outside of the treatment field during external photon beam radiation therapy treatment, also known as out-of-field dose, is the subject of extensive study as it may be associated with a higher risk of developing a second cancer and could have deleterious effects on the immune system that compromise the efficiency of combined radio-immunotherapy treatments. Out-of-field dose estimation tools developed today in research, including Monte Carlo simulations and analytical methods, are not suited to the requirements of clinical implementation because of their lack of versatility and their cumbersome application. We propose a proof of concept based on deep learning for out-of-field dose map estimation that addresses these limitations. METHODS AND MATERIALS: For this purpose, a 3D U-Net, considering as inputs the in-field dose, as computed by the treatment planning system, and the patient's anatomy, was trained to predict out-of-field dose maps. The cohort used for learning and performance evaluation included 3151 pediatric patients from the FCCSS database, treated in 5 clinical centers, whose whole-body dose maps were previously estimated with an empirical analytical method. The test set, composed of 433 patients, was split into 5 subdata sets, each containing patients treated with devices unseen during the training phase. Root mean square deviation evaluated only on nonzero voxels located in the out-of-field areas was computed as performance metric. RESULTS: Root mean square deviations of 0.28 and 0.41 cGy/Gy were obtained for the training and validation data sets, respectively. Values of 0.27, 0.26, 0.28, 0.30, and 0.45 cGy/Gy were achieved for the 6 MV linear accelerator, 16 MV linear accelerator, Alcyon cobalt irradiator, Mobiletron cobalt irradiator, and betatron device test sets, respectively. CONCLUSIONS: This proof-of-concept approach using a convolutional neural network has demonstrated unprecedented generalizability for this task, although it remains limited, and brings us closer to an implementation compatible with clinical routine.

Clinical transfer of AGuIX®-based radiation treatments for locally advanced cervical cancer: MR quantification and in vitro insights in the NANOCOL clinical trial framework.

Clinical trials incorporating metallic nanoparticles (NPs) have recently begun. Radiotherapy planning does not take into account NPs concentrations observed in the patients' target volumes. In the framework of the NANOCOL clinical trial including patients treated for locally advanced cervical cancers, this study proposes a complete method to evaluate the radiation-induced biological effects of NPs. For this, calibration phantom was developed and MRI sequences with variable flip angles were acquired. This process allowed the quantification of NPs in the tumor of 4 patients, which was compared to the results of mass spectrometry obtained from 3 patient biopsies. The concentration of the NPs was reproduced in 3D cell models. Based on clonogenic assays, the radio-enhancement effects were quantified for radiotherapy and brachytherapy, and the impact in terms of local control was evaluated. T1 signal change in GTVs revealed NPs accumulation ∼12.4 µmol/L, in agreement with mass spectrometry. Radio-enhancement effects of about 15 % at 2 Gy were found for both modalities, with a positive impact on local tumor control. Even if further follow-up of patients in this and subsequent clinical trials will be necessary to assess the reliability of this proof of concept, this study opens the way to the integration of a dose modulation factor to better take into account the impact of NPs in radiotherapy treatment.

Rapid Evaluation of Novel Therapeutic Strategies Using a 3D Collagen-Based Tissue-Like Model.

2D cell cultures are commonly used to rapidly evaluate the therapeutic potential of various treatments on living cells. However, the effects of the extracellular matrix (ECM) including the 3D arrangement of cells and the complex physiology of native environment are missing, which makes these models far from in vivo conditions. 3D cell models have emerged in preclinical studies to simulate the impact of the ECM and partially bridge the gap between monolayer cultures and in vivo tissues. To date, the difficulty to handle the existing 3D models, the cost of their production and their poor reproducibility have hindered their use. Here, we present a reproducible and commercially available "3D cell collagen-based model" (3D-CCM) that allows to study the influence of the matrix on nanoagent uptake and radiation effects. The cell density in these samples is homogeneous. The oxygen concentration in the 3D-CCM is tunable, which opens the opportunity to investigate hypoxic effects. In addition, thanks to the intrinsic properties of the collagen, the second harmonic imaging microscopy may be used to probe the whole volume and visualize living cells in real-time. Thus, the architecture and composition of 3D-CCMs as well as the impact of various therapeutic strategies on cells embedded in the ECM is observed directly. Moreover, the disaggregation of the collagen matrix allows recovering of cells without damaging them. It is a major advantage that makes possible single cell analysis and quantification of treatment effects using clonogenic assay. In this work, 3D-CCMs were used to evaluate the correlative efficacies of nanodrug exposure and medical radiation on cells contained in a tumor like sample. A comparison with monolayer cell cultures was performed showing the advantageous outcome and the higher potential of 3D-CCMs. This cheap and easy to handle approach is more ethical than in vivo experiments, thus, giving a fast evaluation of cellular responses to various treatments.

Fluorescent Radiosensitizing Gold Nanoparticles.

Ultrasmall polyaminocarboxylate-coated gold nanoparticles (NPs), Au@DTDTPA and Au@TADOTAGA, that have been recently developed exhibit a promising potential for image-guided radiotherapy. In order to render the radiosensitizing effect of these gold nanoparticles even more efficient, the study of their localization in cells is required to better understand the relation between the radiosensitizing properties of the agents and their localization in cells and in tumors. To achieve this goal, post-functionalization of Au@DTDTPA nanoparticles by near-infrared (NIF) organic dyes (aminated derivative of cyanine 5, Cy5-NH2) was performed. The immobilization of organic Cy5-NH2 dyes onto the gold nanoparticles confers to these radiosensitizers fluorescence properties which can be exploited for monitoring their internalization in cancerous cells, for determining their localization in cells by fluorescence microscopy (a common and powerful imaging tool in biology), and for following up on their accumulation in tumors after intravenous injection.