Dose Distribution Map Research Articles

Investigation of Radiation Exposure of Medical Staff During Lateral Fluoroscopy for Posterior Spinal Fusion Surgery

Background/Objectives: In spinal surgery, it is especially crucial to insert implants in the correct location. Intraoperative fluoroscopy is often necessary to safely perform spinal surgery because of serious complications that can occur if the screw deviates. However, the use of intraoperative fluoroscopy comes at the cost of radiation exposure to the surgeons and operating room staff. Therefore, it is desirable for spinal surgeons to understand the characteristics of radiation in order to minimize patient and medical staff exposure. This study aimed to create an aerial radiation dose distribution map for lateral fluoroscopy, a commonly used technique for posterior spinal fusion. Methods: A human body-equivalent phantom was placed in a prone position on the Jackson Table. The measurement method used was a lateral fluoroscopic evaluation, assuming posterior spinal fusion. Measurements were taken at three levels: 80 (gonadal), 100 (thoracoabdominal), and 150 cm (lens and thyroid). Results: The highest radiation doses were received by primary surgeons. The scrub nurse was the next most exposed. Conclusions: We developed an aerial dose distribution map for lateral fluoroscopy in posterior spinal fusion. Radiation exposure was the highest among primary surgeons.

Size-Sorted Superheated Nanodroplets for Dosimetry and Range Verification of Carbon-Ion Radiotherapy.

Nanodroplets have demonstrated potential for the range detection of hadron radiotherapies. Our formulation uses superheated perfluorobutane (C4F10) stabilized by a poly(vinyl-alcohol) shell. High-LET (linear energy transfer) particles vaporize the nanodroplets into echogenic microbubbles. Tailored ultrasound imaging translates the generated echo-contrast into a dose distribution map, enabling beam range retrieval. This work evaluates the response of size-sorted nanodroplets to carbon-ion radiation. We studied how thesize of nanodroplets affects their sensitivity at various beam-doses and energies, as a function of concentration and shell cross-linking. First, we show the physicochemical characterization of size-isolated nanodroplets by differential centrifugation. Then, we report on the irradiations of the nanodroplet samples in tissue-mimicking phantoms. We compared the response of large (≈900 nm) and small (≈400 nm) nanodroplets to different carbon-ions energies and evaluated their dose linearity and concentration detection thresholds by ultrasound imaging. Additionally, we verified the beam range detection accuracy for the nanodroplets samples. All nanodroplets exhibited sensitivity to carbon-ions with high range verification precision. However, smaller nanodroplets required a higher concentration sensitivity threshold. The vaporization yield depends on the carbon-ions energy and dose, which are both related to particle count/spot. These findings confirm the potential of nanodroplets for range detection, with performance depending on nanodroplets' properties and beam parameters.

Comparison of deep-learning multimodality data fusion strategies in mandibular osteoradionecrosis NTCP modelling using clinical variables and radiation dose distribution volumes

Objective.Normal tissue complication probability (NTCP) modelling is rapidly embracing deep learning (DL) methods, acknowledging the importance of spatial dose information. Finding effective ways to combine information from radiation dose distribution maps (dosiomics) and clinical data involves technical challenges and requires domain knowledge. We propose different multi-modality data fusion strategies to facilitate future DL-based NTCP studies.Approach.Early, joint and late DL multi-modality fusion strategies were compared using clinical and mandibular radiation dose distribution volumes. These were contrasted with single-modality models: a random forest trained on non-image data (clinical, demographic and dose-volume metrics) and a 3D DenseNet-40 trained on image data (mandibular dose distribution maps). The study involved a matched cohort of 92 osteoradionecrosis cases and 92 controls from a single institution.Main results.The late fusion model exhibited superior discrimination and calibration performance, while the join fusion achieved a more balanced distribution of the predicted probabilities. Discrimination performance did not significantly differ between strategies. Late fusion, though less technically complex, lacks crucial inter-modality interactions for NTCP modelling. In contrast, joint fusion, despite its complexity, resulted in a single network training process which included intra- and inter-modality interactions in its model parameter optimisation.Significance.This study is a pioneering effort in comparing different strategies for including image data into DL-based NTCP models in combination with lower dimensional data such as clinical variables. The discrimination performance of such multi-modality NTCP models and the choice of fusion strategy will depend on the distribution and quality of both types of data. Multiple data fusion strategies should be compared and reported in multi-modality NTCP modelling using DL.

DSANet: Dual-path segmentation-guided attention network for radiotherapy dose prediction from CT images only

In radiation therapy, an ideal treatment plan always takes the treatment planner a couple of days to tweak repeatedly, owing to the complex dose distribution which requires the prescribed dose in the tumor area but significantly reduced radiation dose in the surrounding normal tissues. To facilitate this process, many deep learning-based studies have been devoted to the automatical prediction of dose distribution. However, besides the computed tomography (CT) image, these methods usually require extra segmentation masks such as tumor target and organs at risk (OARs) as prior knowledge, and acquiring such annotations are quite time-consuming. In this paper, we propose a Dual-path Segmentation-guided Attention Network (DSANet) to directly predict the dose distribution map solely from the CT images. Specifically, considering the high correlation between the tumor anatomical structure and the dose distribution, we develop a dual-path architecture which consists of 1) a primary dose prediction path aiming at generating accurate dose distribution maps, and 2) an auxiliary segmentation path applied to provide attention maps with anatomical information for the primary dose prediction task. Moreover, we design an attention map-guided feature decoupling (AMFD) module to enforce the primary network to focus not only on the high-dose distribution in the tumor area but also on the low-dose distribution in the surrounding regions. Experiments on two in-house datasets demonstrate the effectiveness and generalization of our method.

A 3D imaging system for dosimetry in FLASH radiotherapy

This study examines the feasibility of reconstructing the spatial dose distribution delivered by a 9 MeV electron beam within a plastic scintillator block using optical tomography. The system is designed for use in 3D-dosimetry for clinical applications in quality assurance and beam characterization, especially with FLASH-beam sources. The proposed detector reconstructs the 3D dose distribution map employing a monolith with 10 cm side, imaged in four different projections by scientific cameras. The 3D dose distribution simulated with Geant4 was reconstructed using a maximum likelihood expectation maximization algorithm applied to the lateral projection, resulting in a longitudinal image with a standard deviation of 0.2 Gy. Furthermore, a preliminary experimental test has been performed.

Precision dose prediction for breast cancer patients undergoing IMRT: The Swin-UMamba-Channel Model

BackgroundRadiation therapy is one of the crucial treatment modalities for cancer. An excellent radiation therapy plan relies heavily on an outstanding dose distribution map, which is traditionally generated through repeated trials and adjustments by experienced physicists. However, this process is both time-consuming and labor-intensive, and it comes with a degree of subjectivity. Now, with the powerful capabilities of deep learning, we are able to predict dose distribution maps more accurately, effectively overcoming these challenges. MethodsIn this study, we propose a novel Swin-UMamba-Channel prediction model specifically designed for predicting the dose distribution of patients with left breast cancer undergoing radiotherapy after total mastectomy. This model integrates anatomical position information of organs and ray angle information, significantly enhancing prediction accuracy. Through iterative training of the generator (Swin-UMamba) and discriminator, the model can generate images that closely match the actual dose, assisting physicists in quickly creating DVH curves and shortening the treatment planning cycle. Our model exhibits excellent performance in terms of prediction accuracy, computational efficiency, and practicality, and its effectiveness has been further verified through comparative experiments with similar networks. ResultsThe results of the study indicate that our model can accurately predict the clinical dose of breast cancer patients undergoing intensity-modulated radiation therapy (IMRT). The predicted dose range is from 0 to 50 Gy, and compared with actual data, it shows a high accuracy with an average Dice similarity coefficient of 0.86. Specifically, the average dose change rate for the planning target volume ranges from 0.28 % to 1.515 %, while the average dose change rates for the right and left lungs are 2.113 % and 0.508 %, respectively. Notably, due to their small sizes, the heart and spinal cord exhibit relatively higher average dose change rates, reaching 3.208 % and 1.490 %, respectively. In comparison with similar dose studies, our model demonstrates superior performance. Additionally, our model possesses fewer parameters, lower computational complexity, and shorter processing time, further enhancing its practicality and efficiency. These findings provide strong evidence for the accuracy and reliability of our model in predicting doses, offering significant technical support for IMRT in breast cancer patients. ConclusionThis study presents a novel Swin-UMamba-Channel dose prediction model, and its results demonstrate its precise prediction of clinical doses for the target area of left breast cancer patients undergoing total mastectomy and IMRT. These remarkable achievements provide valuable reference data for subsequent plan optimization and quality control, paving a new path for the application of deep learning in the field of radiation therapy.

Deep learning-based synthetic dose-weighted LET map generation for intensity modulated proton therapy

The advantage of proton therapy as compared to photon therapy stems from the Bragg peak effect, which allows protons to deposit most of their energy directly at the tumor while sparing healthy tissue. However, even with such benefits, proton therapy does present certain challenges. The biological effectiveness differences between protons and photons are not fully incorporated into clinical treatment planning processes. In current clinical practice, the relative biological effectiveness (RBE) between protons and photons is set as constant 1.1. Numerous studies have suggested that the RBE of protons can exhibit significant variability. Given these findings, there is a substantial interest in refining proton therapy treatment planning to better account for the variable RBE. Dose-average linear energy transfer (LETd) is a key physical parameter for evaluating the RBE of proton therapy and aids in optimizing proton treatment plans. Calculating precise LETd distributions necessitates the use of intricate physical models and the execution of specialized Monte-Carlo simulation software, which is a computationally intensive and time-consuming progress. In response to these challenges, we propose a deep learning based framework designed to predict the LETd distribution map using the dose distribution map. This approach aims to simplify the process and increase the speed of LETd map generation in clinical settings. The proposed CycleGAN model has demonstrated superior performance over other GAN-based models. The mean absolute error (MAE), peak signal-to-noise ratio and normalized cross correlation of the LETd maps generated by the proposed method are 0.096 ± 0.019 keV μm−1, 24.203 ± 2.683 dB, and 0.997 ± 0.002, respectively. The MAE of the proposed method in the clinical target volume, bladder, and rectum are 0.193 ± 0.103, 0.277 ± 0.112, and 0.211 ± 0.086 keV μm−1, respectively. The proposed framework has demonstrated the feasibility of generating synthetic LETd maps from dose maps and has the potential to improve proton therapy planning by providing accurate LETd information.

Измерение распределения дозы гамма-излучения с помощью дрона дискретным методом измерения

Several dose distribution maps have been created using a drone-mounted gamma radiation detector. Based on the results and the experiences gained in the experiments, the shortcomings of the system as well as the possibility for further development have also emerged. The primary objective of the development was to create a more compact, easily portable, and deployable system, but one which, in contrast with the previous ones, is more sensitive. Despite background radiation (typically 0.01 Sv/h in the testing areas), a difference of +0.009 Sv/h has reliably been detected in the experiments made so far. In this present development, this value was successfully reduced to +0.005 - +0.007 S/h. The improvement in sensitivity was achieved primarily by increasing the measurement time per point, which was realized using special flight control software. This sensitivity increases either allows higher scanning altitude (approximately +1-2m) or, in case of an identical flight device at an identical altitude, a larger survey area with one take-off. Naturally, scanning height or scanning speed can increase significantly if the activity of the searched source is high. In our experiments, we used a natural uranium mineral (Autunit) with activity far below that of artificially produced isotopes. In this series of experiments, we also covered the identification of several sources, which models the possibility of mapping active sources scattered around the site of a possible accident. The main advantage of the system developed and introduced by us over the survey procedures used in practice is that it is easy to mobilize, a large area can be surveyed at low cost without putting an operator at risk in the field. The purpose of the system is to detect the presence of the source and to localize it to such an extent that the localization can subsequently be easily specified by manual or other ground procedures. Since we do not strive for centimeter positioning accuracy, standard GPS localization is sufficient for measurements. During the measurements, the geographical coordinates are interpreted in accordance with the WGS’84 system. Additionally, the system displays the circles of latitude and longitude coordinates in the figures presented.

DoseDiff: Distance-Aware Diffusion Model for Dose Prediction in Radiotherapy.

Treatment planning, which is a critical component of the radiotherapy workflow, is typically carried out by a medical physicist in a time-consuming trial-and-error manner. Previous studies have proposed knowledge-based or deep-learning-based methods for predicting dose distribution maps to assist medical physicists in improving the efficiency of treatment planning. However, these dose prediction methods usually fail to effectively utilize distance information between surrounding tissues and targets or organs-at-risk (OARs). Moreover, they are poor at maintaining the distribution characteristics of ray paths in the predicted dose distribution maps, resulting in a loss of valuable information. In this paper, we propose a distance-aware diffusion model (DoseDiff) for precise prediction of dose distribution. We define dose prediction as a sequence of denoising steps, wherein the predicted dose distribution map is generated with the conditions of the computed tomography (CT) image and signed distance maps (SDMs). The SDMs are obtained by distance transformation from the masks of targets or OARs, which provide the distance from each pixel in the image to the outline of the targets or OARs. We further propose a multi-encoder and multi-scale fusion network (MMFNet) that incorporates multi-scale and transformer-based fusion modules to enhance information fusion between the CT image and SDMs at the feature level. We evaluate our model on two in-house datasets and a public dataset, respectively. The results demonstrate that our DoseDiff method outperforms state-of-the-art dose prediction methods in terms of both quantitative performance and visual quality.

Dose mapping of hard X-ray radiation due to runaway electrons in Alvand tokamak using combination of high-dose and low-dose level dosimeters

Tokamaks are one of the strongest types of hard x-ray (HXR) sources that always generate energetic hard x-rays because of the bremsstrahlung radiation, when energetic runaway electrons are accelerated in the presence of the toroidal electric field. In addition, the characteristics of small-sized tokamaks make them capable of having a considerable population of runaway electrons during discharge time. In this research, the hard x-ray dose distribution of Alvand tokamak is measured and calculated using EBT3 radiochromic film and GR-200 TLD as a high-dose and low-dose dosimeters, respectively. The purpose of this measurement is to obtain a dose distribution map in the Alvand tokamak laboratory (35 m × 13 m) and its environment. It has been determined that the maximum absorbed dose on the equatorial plane of the vacuum chamber is approximately 73 mSv/shot for a 3 ms discharge. According to the observations, the occurrence of Z instability in the vertical position of the plasma leads to the concentration of a substantial amount of radiation in an upward conical shell, which reaches up to 250 mSv/shot at a 60-degree angle above the horizontal line. Calculating the amount of radiation attenuation of the concrete shield installed in the vicinity of Alvand tokamak Laboratory, which is about 99.3 % ± 0.35, is an important result from the radiation protection point of view. This result indicates that the dose limit in public area is about background level and this area is effectively protected from the intense radiation of Alvand tokamak by the concrete shield.

Dosiomics and radiomics improve the prediction of post-radiotherapy neutrophil-lymphocyte ratio in locally advanced non-small cell lung cancer.

To develop and validate a dosiomics and radiomics model based on three-dimensional (3D) dose distribution map and computed tomography (CT) images for the prediction of the post-radiotherapy (post-RT) neutrophil-to-lymphocyte ratio (NLR). This work retrospectively collected 242 locally advanced non-small cell lung cancer (LA-NSCLC) patients who were treated with definitive radiotherapy from 2012 to 2016. The NLR collected one month after the completion of RT was defined as the primary outcome. Clinical characteristics and two-dimensional dosimetric factors calculated from the dose-volume histogram (DVH) were included. A total of 4165 dosiomics and radiomics features were extracted from the 3D dose maps and CT images within five different anatomical regions of interest (ROIs), respectively. Then, a three-step feature selection method was proposed to progressively filter features from coarse to fine: (i) model-based ranking according to individual feature's performance, (ii) maximum relevance and minimum redundancy (mRMR), (iii) select from model based on feature importance calculated with an ensemble of several decision trees. The selected feature subsets were utilized to develop the prediction model with GBDT. All patients were divided into a development set and an independent testing set (2:1). Five-fold cross-validation was applied to the development set for both feature selection and model training procedure. Finally, a fusion model combining dosiomics, radiomics and clinical features was constructed to further improve the prediction results. The area under receiver operating characteristic curve (ROC) were used to evaluate the modelperformance. The clinical-based and DVH-based models showed limited predictive power with AUCs of 0.632 (95% CI: 0.490-0.773) and 0.634 (95% CI: 0.497-0.771), respectively, in the independent testing set. The 9 feature-based dosiomics and 3 feature-based radiomics models showed improved AUCs of 0.738 (95% CI: 0.628-0.849) and 0.689 (95% CI: 0.566-0.813), respectively. The dosiomics & radiomics & clinical fusion model further improved the model's generalization ability with an AUC of 0.765 (95% CI: 0.656-0.874). Dosiomics and radiomics can benefit the prediction of post-RT NLR of LA-NSCLC patients. This can provide a reference for evaluating radiotherapy-relatedinflammation.

Interpretability of a Deep Learning-Based Prediction Model for Mandibular Osteoradionecrosis.

The development of radiation-induced toxicities is a multifactorial process. Existing DVH-based prediction models use traditional multivariate analysis to combine all the potential risk factors. Recently, deep learning (DL) has been proposed for predicting mandibular osteoradionecrosis (ORN) directly from 3D dose distribution maps [1]. However, with this approach, incorporating non-imaging data such as potential risk factors presents challenges. We investigate the use of DL-based multimodality fusion for the purpose of radiation-induced ORN toxicity prediction. This study explores early and late fusion strategies for combining 3D radiation dose distribution maps and clinical and demographic variables in the prediction of mandibular ORN incidence in head and neck cancer patients treated with radiotherapy. The results are compared to single-modality predictions with a random forest (RF) trained only on clinical variables and a 3D DenseNet40 trained on dose maps alone. We investigate two different fusion approaches. In the first, the image features extracted from the radiation dose maps using a 3D DenseNet40 were concatenated with the clinical variables into one single vector using a type II early fusion strategy. The combined feature vector was input into a fully connected layer for classification of ORN vs. controls. A final softmax activation layer was added to obtain the class predicted probabilities. The second approach used a late fusion strategy, in which the outputs from the 3D DenseNet40 and the RF model were combined by averaging the predicted classification probabilities for each of the two classes (ORN and no ORN) to obtain the final class decision on a case-by-case basis. The AUROC values obtained for the late and early fusion models and the single-modality 3D DenseNet40 and RF models were 0.70, 0.68, 0.69 and 0.60, respectively. The highest AUC ROC was observed with the late fusion approach, which was statistically significantly different to that of the RF single-modality model with a significance level of 0.05. However, after Bonferroni correction (Altman 1999) for multiple comparisons was applied, resulting in a corrected significance level of 0.05/6 = 0.008 for each comparison, no statistically significant difference was observed between any of the models' AUROC values. This is most likely due to the lack of discriminative contribution observed from clinical variables, which on their own resulted in a poorly predictive RF model. To our knowledge, no previous work has been published on the use of multimodal fusion DL methods to combine dose distribution maps and clinical variables in the prediction of mandibular ORN. Although non-conclusive results were obtained, this study demonstrates the potential of DL in the prediction of the multifactorial side effects resulting from radiotherapy treatments. [1] Humbert-Vidan L et al. Prediction of Mandibular ORN Incidence from 3D Radiation Dose Distribution Maps Using DL (2022).

TransDose: Transformer-based radiotherapy dose prediction from CT images guided by super-pixel-level GCN classification.

Radiotherapy is a mainstay treatment for cancer in clinic. An excellent radiotherapy treatment plan is always based on a high-quality dose distribution map which is produced by repeated manual trial-and-errors of experienced experts. To accelerate the radiotherapy planning process, many automatic dose distribution prediction methods have been proposed recently and achieved considerable fruits. Nevertheless, these methods require certain auxiliary inputs besides CT images, such as segmentation masks of the tumor and organs at risk (OARs), which limits their prediction efficiency and application potential. To address this issue, we design a novel approach named as TransDose for dose distribution prediction that treats CT images as the unique input in this paper. Specifically, instead of inputting the segmentation masks to provide the prior anatomical information, we utilize a super-pixel-based graph convolutional network (GCN) to extract category-specific features, thereby compensating the network for the necessary anatomical knowledge. Besides, considering the strong continuous dependency between adjacent CT slices as well as adjacent dose maps, we embed the Transformer into the backbone, and make use of its superior ability of long-range sequence modeling to endow input features with inter-slice continuity message. To our knowledge, this is the first network that specially designed for the task of dose prediction from only CT images without ignoring necessary anatomical structure. Finally, we evaluate our model on two real datasets, and extensive experiments demonstrate the generalizability and advantages of our method.

Unsupervised Domain Adaptive Dose Prediction Via Cross-Attention Transformer and Target-Specific Knowledge Preservation.

Radiotherapy is one of the leading treatments for cancer. To accelerate the implementation of radiotherapy in clinic, various deep learning-based methods have been developed for automatic dose prediction. However, the effectiveness of these methods heavily relies on the availability of a substantial amount of data with labels, i.e. the dose distribution maps, which cost dosimetrists considerable time and effort to acquire. For cancers of low-incidence, such as cervical cancer, it is often a luxury to collect an adequate amount of labeled data to train a well-performing deep learning (DL) model. To mitigate this problem, in this paper, we resort to the unsupervised domain adaptation (UDA) strategy to achieve accurate dose prediction for cervical cancer (target domain) by leveraging the well-labeled high-incidence rectal cancer (source domain). Specifically, we introduce the cross-attention mechanism to learn the domain-invariant features and develop a cross-attention transformer-based encoder to align the two different cancer domains. Meanwhile, to preserve the target-specific knowledge, we employ multiple domain classifiers to enforce the network to extract more discriminative target features. In addition, we employ two independent convolutional neural network (CNN) decoders to compensate for the lack of spatial inductive bias in the pure transformer and generate accurate dose maps for both domains. Furthermore, to enhance the performance, two additional losses, i.e. a knowledge distillation loss (KDL) and a domain classification loss (DCL), are incorporated to transfer the domain-invariant features while preserving domain-specific information. Experimental results on a rectal cancer dataset and a cervical cancer dataset have demonstrated that our method achieves the best quantitative results with [Formula: see text], [Formula: see text], and HI of 1.446, 1.231, and 0.082, respectively, and outperforms other methods in terms of qualitative assessment.

2V-CBCT: Two-Orthogonal-Projection Based CBCT Reconstruction and Dose Calculation from Real CBCT Projection Data

Not all radiation therapy (RT) treatments/fractions have CBCT acquired, but two orthogonal projections (i.e., KV radiography) are always available. This work demonstrates the feasibility of two-orthogonal-projection-based CBCT (2V-CBCT) reconstruction and dose calculation for RT from real CBCT projection data, which is the first 2V-CBCT feasibility study using real projection data, to the best of our knowledge. 2V-CBCT is a severely ill-posed inverse problem for which we propose a coarse-to-fine learning strategy. First, a 3D deep neural network that can extract and exploit the inter-slice and intra-slice information is adopted to predict the initial 3D volumes. Then, a 2D deep neural network is utilized to fine-tune the initial 3D volumes slice-by-slice. During the fine-tuning stage, a perceptual loss based on multi-frequency features is employed to enhance the image reconstruction. Dose calculation results from both photon and proton RT demonstrate that 2V-CBCT provides comparable accuracy with full-view CBCT based on real projection data. The proposed method was evaluated on real HN data acquired from on-board CBCT scanners rather than the low-resolution simulated data or down-sampled data. Both visual assessment and quantitative analysis demonstrate that the proposed coarse-to-fine learning strategy has the potential to produce satisfactory volumetric images from two orthogonal projections. Furthermore, we assessed the utility of 2V-CBCT in RT. The results show that the dose distribution maps, dose-volume histograms, and dose parameters calculated using 2V-CBCT have comparable accuracy with the counterparts calculated using the corresponding full-view CBCT for both photon and proton RT. In the table, the methods under comparison are pCT (planning CT), FV-CBCT (CBCT reconstructed with full-view projection data), and 2V-CBCT (CBCT reconstructed with two orthogonal projections). A new effective 2V-CBCT reconstruction method is proposed and validated using real CBCT projection data, which can potentially provide comparable dose calculation accuracy for both photon and proton RT.

Hybrid Immuno-RT for Bulky Tumors: Standard Fractionation with Partial Tumor SBRT

Bulky tumors remain challenging to be treated. Stereotactic body radiation therapy (SBRT) is effective against radioresistant tumor cells and can induce immunogenic cell death (ICD) that leads to T-cell-mediated antitumor effects. Low-dose radiation (LDRT) can inflame the tumor microenvironment (TME) by recruiting T cells. We designed a novel radiotherapy technique (RT, ERT) whose dose distribution map resembles the "eclipse" by concurrently delivering LDRT to the whole tumor, meanwhile SBRT to only a part of the same tumor. This study examined the safety and efficacy of ERT to bulky lesions with PD-1 inhibitors in mice and patients. In mice with CT26 colon or LLC1 lung bulky tumors (400 - 500 cm3), the whole tumor was irradiated by LDRT (2 Gy x 3), meanwhile the tumor center was irradiated by SBRT (10 Gy x 3); αPD-1 was given weekly. The dependence of therapeutic effects on CD8+ T cells was determined using depleting antibodies. Frequencies of CD8+ T cells and M1 macrophages (Mφ) were determined by flow cytometry. Multiplex Immunohistochemistry (mIHC) was applied to analyze the number and the location of CD8+ T cells and their subpopulations, as well as the phospho-eIF2α level (the ICD marker) of tumor cells in TME. Patients with advanced lung or liver bulky tumors who failed standard treatment or with oncologic emergencies were treated. Kaplan-Meier method was applied to estimate patients' progression-free survival (PFS) and overall survival (OS). ERT/αPD-1 is superior to SBRT/αPD-1 or LDRT/αPD-1 in controlling bulky tumors in both mouse models in a CD8+ T-cell dependent manner. In the CT26 model, ERT/αPD-1 resulted in complete tumor regression in 3/11 mice and induced more CD8+ T cells and M1 Mφ in TME compared to other groups. mIHC analysis showed that ERT/αPD-1 induced higher bulk, stem-like (TCF1+ TIM3- PD-1+), and more differentiated (TCF1- TIM3+ PD-1+) CD8+ T cells infiltration into the tumor center and periphery compared to other groups. Compared to untreated or LDRT-treated tumor centers, tumor centers irradiated with ERT or SBRT showed elevated phospho-eIF2α accompanied by higher dendritic cell infiltration. In total, 39 advanced cancer patients were treated with ERT/αPD-1 or plus chemotherapy. Radiation-induced pneumonitis occurred in 1 of 26 patients receiving thoracic ERT. There were two cases of grade III toxicity associated with PD-1 inhibitors. No toxicity above grade III was observed. The objective response rate was 38.5%. The median PFS was 5.6 months and median OS was not reached at a median follow-up of 11.7 months. ERT/αPD-1 showed superior efficacy in controlling bulky tumor in two mouse models. The hybrid immuno-RT (ERT) combing PD-1 inhibitors was safe and effective in patients with bulky tumors. Further clinical trials in combination with bioimaging to identify the optimal SBRT target region for the bulky tumor are warranted.

P11.27.A RADIOTHERAPY-INDUCED CHANGES IN THE BRAIN: A LONGITUDINAL STUDY IN PATIENTS WITH LOW-GRADE GLIOMAS

Abstract BACKGROUND Low-grade gliomas (LGG) are brain tumors with a comparably moderate malignancy grade and slow progression. Most patients are young to middle-aged adults with a relatively good prognosis given the advancement of multimodal treatment METHODS . Radiotherapy (RT) is an integral component of this treatment. During RT, protons or photons are used to eradicate the tumor while minimizing the damage to surrounding areas. However, healthy brain tissue is inevitably exposed to radiation, which can have adverse effects with a severe impact on the cognition and quality of life of survivors. With the improved survival of patients, understanding RT-induced changes and their consequences becomes more and more important. Here, we investigate the volumetric changes in LGG patients 2.5 years after treatment to gain insights into the neuronal long-term effects of RT. MATERIAL AND METHODS To quantify the volumetric changes in the brain of LGG patients over time, LGG patients (N= 17) were scanned before and 2.5 years after proton RT. For both time points, volume parameters per brain structure were extracted using T1w MRI scans and cNeuro® software. The volumes of the structures in the tumor-contralateral hemispheres before RT were compared with the measures 2.5 years after RT using paired samples t-tests or nonparametric alternatives. RESULTS Analysis of the total grey matter of the contralateral hemisphere indicated a statistically significant volumetric reduction in our cohort 2.5 years after RT. An analysis targeting volumetric changes in the contralateral hippocampus also indicated a statistically significant decrease. In the following steps, additional structures will be analyzed. CONCLUSION Preliminary results suggest a potential RT-induced decrease in the tumor-contralateral grey matter and hippocampal volume 2.5 years after treatment. Volumetric changes will be compared with the received radiation dose per structure using radiation dose distribution maps.

Simultaneous dose distribution and fluence prediction for nasopharyngeal carcinoma IMRT

BackgroundCurrent intensity-modulated radiation therapy (IMRT) treatment planning is still a manual and time/resource consuming task, knowledge-based planning methods with appropriate predictions have been shown to enhance the plan quality consistency and improve planning efficiency. This study aims to develop a novel prediction framework to simultaneously predict dose distribution and fluence for nasopharyngeal carcinoma treated with IMRT, the predicted dose information and fluence can be used as the dose objectives and initial solution for an automatic IMRT plan optimization scheme, respectively.MethodsWe proposed a shared encoder network to simultaneously generate dose distribution and fluence maps. The same inputs (three-dimensional contours and CT images) were used for both dose distribution and fluence prediction. The model was trained with datasets of 340 nasopharyngeal carcinoma patients (260 cases for training, 40 cases for validation, 40 cases for testing) treated with nine-beam IMRT. The predicted fluence was then imported back to treatment planning system to generate the final deliverable plan. Predicted fluence accuracy was quantitatively evaluated within projected planning target volumes in beams-eye-view with 5 mm margin. The comparison between predicted doses, predicted fluence generated doses and ground truth doses were also conducted inside patient body.ResultsThe proposed network successfully predicted similar dose distribution and fluence maps compared with ground truth. The quantitative evaluation showed that the pixel-based mean absolute error between predicted fluence and ground truth fluence was 0.53% ± 0.13%. The structural similarity index also showed high fluence similarity with values of 0.96 ± 0.02. Meanwhile, the difference in the clinical dose indices for most structures between predicted dose, predicted fluence generated dose and ground truth dose were less than 1 Gy. As a comparison, the predicted dose achieved better target dose coverage and dose hot spot than predicted fluence generated dose compared with ground truth dose.ConclusionWe proposed an approach to predict 3D dose distribution and fluence maps simultaneously for nasopharyngeal carcinoma patients. Hence, the proposed method can be potentially integrated in a fast automatic plan generation scheme by using predicted dose as dose objectives and predicted fluence as a warm start.

Deep learning-based dose prediction in radiotherapy planning for head and neck cancer

To propose an deep learning-based algorithm for automatic prediction of dose distribution in radiotherapy planning for head and neck cancer. We propose a novel beam dose decomposition learning (BDDL) method designed on a cascade network. The delivery matter of beam through the planning target volume (PTV) was fitted with the pre-defined beam angles, which served as an input to the convolution neural network (CNN). The output of the network was decomposed into multiple sub-fractions of dose distribution along the beam directions to carry out a complex task by performing multiple simpler sub-tasks, thus allowing the model more focused on extracting the local features. The subfractions of dose distribution map were merged into a distribution map using the proposed multi-voting mechanism. We also introduced dose distribution features of the regions-of-interest (ROIs) and boundary map as the loss function during the training phase to serve as constraining factors of the network when extracting features of the ROIs and areas of dose boundary. Public datasets of radiotherapy planning for head and neck cancer were used for obtaining the accuracy of dose distribution of the BDDL method and for implementing the ablation study of the proposed method. The BDDL method achieved a Dose score of 2.166 and a DVH score of 1.178 (P < 0.05), demonstrating its superior prediction accuracy to that of current state-ofthe-art (SOTA) methods. Compared with the C3D method, which was in the first place in OpenKBP-2020 Challenge, the BDDL method improved the Dose score and DVH score by 26.3% and 30%, respectively. The results of the ablation study also demonstrated the effectiveness of each key component of the BDDL method. The BDDL method utilizes the prior knowledge of the delivery matter of beam and dose distribution in the ROIs to establish a dose prediction model. Compared with the existing methods, the proposed method is interpretable and reliable and can be potentially applied in clinical radiotherapy.

Validation of the TOPAS Monte Carlo toolkit for LDR brachytherapy simulations

Purpose:This work has the purpose of validating the Monte Carlo toolkit TOol for PArticle Simulation (TOPAS) for low-dose-rate (LDR) brachytherapy uses. Methods and Materials:Simulations of 12 LDR sources and 2 COMS eye plaques (10 mm and 20 mm in diameter) and comparisons with published reference data from the Carleton Laboratory for Radiotherapy Physics (CLRP), the TG-43 consensus data and the TG-129 consensus data were performed. Sources from the IROC Houston Source Registry were modeled. The OncoSeed 6711 and the SelectSeed 130.002 were also modeled for historical reasons. For each source, the dose rate constant, the radial dose function and the anisotropy functions at 0.5, 1 and 5 cm were extracted. For the eye plaques (loaded with 125I sources), dose distribution maps, dose profiles along the central axis and transverse axis were calculated. Results:Dose rate constants for 11 of the 12 sources are within 4% of the consensus data and within 2% of the CLRP data. The radial dose functions and anisotropy functions are mostly within 2% of the CLRP data. In average, 92% of all voxels are within 1% of the CLRP data for the eye plaques dose distributions. The dose profiles are within 0.5% (central axis) and 1% (transverse axis) of the reference data. Conclusion:The TOPAS MC toolkit was validated for LDR brachytherapy applications. Single-seed and multi-seed results agree with the published reference data. TOPAS has several benefits such as a simplified approach to MC simulations and an accessible brachytherapy package including comprehensive learning resources.