A denoising diffusion probabilistic model for metal artifact reduction in CT.

The presence of metal objects leads to corrupted CT projection measurements, resulting in metal artifacts in the reconstructed CT images. AI promises to offer improved solutions to estimate missing sinogram data for metal artifact reduction (MAR), as previously shown with convolutional neural networks (CNNs) and generative adversarial networks (GANs). Recently, denoising diffusion probabilistic models (DDPM) have shown great promise in image generation tasks, potentially outperforming GANs. In this study, a DDPM-based approach is proposed for inpainting of missing sinogram data for improved MAR. The proposed model is unconditionally trained, free from information on metal objects, which can potentially enhance its generalization capabilities across different types of metal implants compared to conditionally trained approaches. The performance of the proposed technique was evaluated and compared to the state-of-the-art normalized MAR (NMAR) approach as well as to CNN-based and GAN-based MAR approaches. The DDPM-based approach provided significantly higher SSIM and PSNR, as compared to NMAR (SSIM: p < 10-26; PSNR: p < 10-21), the CNN (SSIM: p < 10-25; PSNR: p < 10-9) and the GAN (SSIM: p < 10-6; PSNR: p < 0.05) methods. The DDPM-MAR technique was further evaluated based on clinically relevant image quality metrics on clinical CT images with virtually introduced metal objects and metal artifacts, demonstrating superior quality relative to the other three models. In general, the AI-based techniques showed improved MAR performance compared to the non-AI-based NMAR approach. The proposed methodology shows promise in enhancing the effectiveness of MAR, and therefore improving the diagnostic accuracy of CT.

Particle arc therapy: Status and potential.

There is a rising interest in developing and utilizing arc delivery techniques with charged particle beams, e.g., proton, carbon or other ions, for clinical implementation. In this work, perspectives from the European Society for Radiotherapy and Oncology (ESTRO) 2022 physics workshop on particle arc therapy are reported. This outlook provides an outline and prospective vision for the path forward to clinically deliverable proton, carbon, and other ion arc treatments. Through the collaboration among industry, academic, and clinical research and development, the scientific landscape and outlook for particle arc therapy are presented here to help our community understand the physics, radiobiology, and clinical principles. The work is presented in three main sections: (i) treatment planning, (ii) treatment delivery, and (iii) clinical outlook.

Dose-volume metric-based prediction of radiotherapy-induced lymphocyte loss in patients with non-small-cell lung cancer treated with modern radiotherapy techniques.

Background and Purpose: Radiation-induced lymphopenia (RIL) is a common side effect of radiotherapy (RT) that may negatively impact survival. We aimed to identify RIL predictors in patients with non-small-cell lung cancer (NSCLC) treated intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT). Materials and Methods: We retrospectively analysed data of 306 patients who underwent radical RT for NSCLC. Absolute lymphocyte count (ALC) loss was evaluated for each patient by fitting an exponential decay curve to data from first 45 days since treatment start, and percentage ALC loss relative to baseline was calculated based on area under the decay curve and baseline ALC. We compared IMRT and VMAT treatment plans and used linear regression to predict ALC loss. Results: ALC decreased during RT in the whole patient group, while neutrophil counts remained stable and decreased only in those treated with concurrent chemoradiotherapy (CRT). Percentage ALC loss ranged between 11 and 78 % and was more strongly than lymphocyte nadir correlated with dose-volume metrics for relevant normal structures. We found evidence for the association of high radiation dose to the lungs, heart and body with percentage ALC loss, with lung volume exposed to 20-30 Gy being most important predictors in patients treated with IMRT. A multivariable model based on CRT use, baseline ALC and first principal component (PC1) of the dose-volume predictors showed good predictive performance (bias-corrected R2 of 0.40). Conclusion: Percentage lymphocyte loss is a robust measure of RIL that is predicted by baseline ALC, CRT use and dose-volume parameters to the lungs, heart and body.

Radiation Therapy for Stage IIA/B Seminoma: Modeling Secondary Cancer Risk for Protons and VMAT versus 3D Photons.

Radiation therapy (RT) is an effective treatment for stage IIA and select stage IIB seminomas. However, given the long life expectancy of seminoma patients, there are concerns about the risk of secondary cancers from RT. This study assessed differences in secondary cancer risk for stage II seminoma patients following proton pencil-beam scanning (PBS) and photon VMAT, compared to 3D conformal photon RT. Ten seminoma patients, five with a IIA staging who received 30 GyRBE and five with a IIB staging who received 36 GyRBE, had three RT plans generated. Doses to organs at risk (OAR) were evaluated, and secondary cancer risks were calculated as the Excess Absolute Risk (EAR) and Lifetime Attributable Risk (LAR). PBS reduced the mean OAR dose by 60% on average compared to 3D, and reduced the EAR and LAR for all OAR, with the greatest reductions seen for the bowel, liver, and stomach. VMAT reduced high doses but increased the low-dose bath, leading to an increased EAR and LAR for some OAR. PBS provided superior dosimetric sparing of OAR compared to 3D and VMAT in stage II seminoma cases, with models demonstrating that this may reduce secondary cancer risk. Therefore, proton therapy shows the potential to reduce acute and late side effects of RT for this population.

The Role of Proton Therapy for Prostate Cancer in the Setting of Hip Prosthesis.

PURPOSE: Given that the current standard of proton therapy (PT) for prostate cancer is through bilateral beams, this modality is typically avoided when it comes to treatment of patients with hip prosthesis. The purpose of this study was to evaluate whether novel PT methods, i.e., anterior proton beams and proton arc therapy (PArc), could be feasible options to treat this patient subpopulation. We evaluate PT methods in the context of dosimetry and robustness and compare with standard of practice volumetric modulated arc therapy (VMAT) to explore any potential benefits. METHODS: Two PT and one VMAT treatment plans were retrospectively created for 10 patients who participated in a clinical trial with a weekly repeat CT (rCT) imaging component. All plans were robustly optimized and featured: (1) combination anterior oblique and lateral proton beams (AoL), (2) PArc, and (3) VMAT. All patients had hydrogel spacers in place, which enabled safe application of anterior proton beams. The planned dose was 70 Gy (RBE) to the entire prostate gland and 50 Gy (RBE) to the proximal seminal vesicles in 28 fractions. Along with plan dose-volume metrics, robustness to setup and interfractional variations were evaluated using the weekly rCT images. The linear energy transfer (LET)-weighted dose was evaluated for PArc plans to ensure urethra sparing given the typical high-LET region at the end of range. RESULTS: Both PT methods were dosimetrically feasible and provided reduction of some key OAR metrics compared to VMAT except for penile bulb, while providing equally good target coverage. Significant differences in median rectum V35 (22-25%), penile bulb Dmean (5 Gy), rectum V61 (2%), right femoral head Dmean (5 Gy), and bladder V39 (4%) were found between PT and VMAT. All plans were equally robust to variations. LET-weighted dose in urethra was equivalent to the physical dose for PArc plans and hence no added urethral toxicity was expected. CONCLUSIONS: PT for treatment of prostate cancer patients with hip prosthesis is feasible and equivalent or potentially superior to VMAT in quality in some cases. The choice of radiotherapy regimen can be personalized based on patient characteristics to achieve the best treatment outcome.

MIDOS: a novel stochastic model towards a treatment planning system for microsphere dosimetry in liver tumors.

PURPOSE: Transarterial radioembolization (TARE) procedures treat liver tumors by injecting radioactive microspheres into the hepatic artery. Currently, there is a critical need to optimize TARE towards a personalized dosimetry approach. To this aim, we present a novel microsphere dosimetry (MIDOS) stochastic model to estimate the activity delivered to the tumor(s), normal liver, and lung. METHODS: MIDOS incorporates adult male/female liver computational phantoms with the hepatic arterial, hepatic portal venous, and hepatic venous vascular trees. Tumors can be placed in both models at user discretion. The perfusion of microspheres follows cluster patterns, and a Markov chain approach was applied to microsphere navigation, with the terminal location of microspheres determined to be in either normal hepatic parenchyma, hepatic tumor, or lung. A tumor uptake model was implemented to determine if microspheres get lodged in the tumor, and a probability was included in determining the shunt of microspheres to the lung. A sensitivity analysis of the model parameters was performed, and radiation segmentectomy/lobectomy procedures were simulated over a wide range of activity perfused. Then, the impact of using different microspheres, i.e., SIR-Sphere®, TheraSphere®, and QuiremSphere®, on the tumor-to-normal ratio (TNR), lung shunt fraction (LSF), and mean absorbed dose was analyzed. RESULTS: Highly vascularized tumors translated into increased TNR. Treatment results (TNR and LSF) were significantly more variable for microspheres with high particle load. In our scenarios with 1.5 GBq perfusion, TNR was maximum for TheraSphere® at calibration time in segmentectomy/lobar technique, for SIR-Sphere® at 1-3 days post-calibration, and regarding QuiremSphere® at 3 days post-calibration. CONCLUSION: This novel approach is a decisive step towards developing a personalized dosimetry framework for TARE. MIDOS assists in making clinical decisions in TARE treatment planning by assessing various delivery parameters and simulating different tumor uptakes. MIDOS offers evaluation of treatment outcomes, such as TNR and LSF, and quantitative scenario-specific decisions.

The Pediatric Proton and Photon Therapy Comparison Cohort: Study Design for a Multicenter Retrospective Cohort to Investigate Subsequent Cancers After Pediatric Radiation Therapy.

Purpose: The physical properties of protons lower doses to surrounding normal tissues compared with photons, potentially reducing acute and long-term adverse effects, including subsequent cancers. The magnitude of benefit is uncertain, however, and currently based largely on modeling studies. Despite the paucity of directly comparative data, the number of proton centers and patients are expanding exponentially. Direct studies of the potential risks and benefits are needed in children, who have the highest risk of radiation-related subsequent cancers. The Pediatric Proton and Photon Therapy Comparison Cohort aims to meet this need. Methods and Materials: We are developing a record-linkage cohort of 10,000 proton and 10,000 photon therapy patients treated from 2007 to 2022 in the United States and Canada for pediatric central nervous system tumors, sarcomas, Hodgkin lymphoma, or neuroblastoma, the pediatric tumors most frequently treated with protons. Exposure assessment will be based on state-of-the-art dosimetry facilitated by collection of electronic radiation records for all eligible patients. Subsequent cancers and mortality will be ascertained by linkage to state and provincial cancer registries in the United States and Canada, respectively. The primary analysis will examine subsequent cancer risk after proton therapy compared with photon therapy, adjusting for potential confounders and accounting for competing risks. Results: For the primary aim comparing overall subsequent cancer rates between proton and photon therapy, we estimated that with 10,000 patients in each treatment group there would be 80% power to detect a relative risk of 0.8 assuming a cumulative incidence of subsequent cancers of 2.5% by 15 years after diagnosis. To date, 9 institutions have joined the cohort and initiated data collection; additional centers will be added in the coming year(s). Conclusions: Our findings will affect clinical practice for pediatric patients with cancer by providing the first large-scale systematic comparison of the risk of subsequent cancers from proton compared with photon therapy.

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.

A stochastic model of blood flow to calculate blood dose during radiotherapy.

Purpose. Lymphopenia is a common side effect in patients treated with radiotherapy, potentially caused by direct cell killing of circulating lymphocytes in the blood. To investigate this hypothesis, a method to assess dose to circulating lymphocytes is needed.Methods. A stochastic model to simulate systemic blood flow in the human body was developed based on a previously designed compartment model. Blood dose was obtained by superimposing the spatiotemporal distribution of blood particles with a time-varying dose rate field, and used as a surrogate for dose to circulating lymphocytes. We discuss relevant theory on compartmental modeling and how to combine it with models of explicit organ vasculature.Results. A general workflow was established which can be used for any anatomical site. Stochastic compartments can be replaced by explicit models of organ vasculatures for improved spatial resolution, and tumor compartments can be dynamically assigned. Generating a patient-specific blood flow distribution takes about one minute, fast enough to investigate the effect of varying treatment parameters such as the dose rate. Furthermore, the anatomical structures contributing most to the overall blood dose can be identified, which could potentially be used for lymphocyte-sparing treatment planning.Conclusion. The ability to report the blood dose distribution during radiotherapy is imperative to test and act upon the current paradigm that radiation-induced lymphopenia is caused by direct cell killing of lymphocytes in the blood. We have built a general model that can do so for various treatment sites. The presented framework is publicly available athttp://github.com/mghro/hedos.

Comparing Predicted Toxicities between Hypofractionated Proton and Photon Radiotherapy of Liver Cancer Patients with Different Adaptive Schemes.

With the availability of MRI linacs, online adaptive intensity modulated radiotherapy (IMRT) has become a treatment option for liver cancer patients, often combined with hypofractionation. Intensity modulated proton therapy (IMPT) has the potential to reduce the dose to healthy tissue, but it is particularly sensitive to changes in the beam path and might therefore benefit from online adaptation. This study compares the normal tissue complication probabilities (NTCPs) for liver and duodenal toxicity for adaptive and non-adaptive IMRT and IMPT treatments of liver cancer patients. Adaptive and non-adaptive IMRT and IMPT plans were optimized to 50 Gy (RBE = 1.1 for IMPT) in five fractions for 10 liver cancer patients, using the original MRI linac images and physician-drawn structures. Three liver NTCP models were used to predict radiation-induced liver disease, an increase in albumin-bilirubin level, and a Child-Pugh score increase of more than 2. Additionally, three duodenal NTCP models were used to predict gastric bleeding, gastrointestinal (GI) toxicity with grades >3, and duodenal toxicity grades 2-4. NTCPs were calculated for adaptive and non-adaptive IMRT and IMPT treatments. In general, IMRT showed higher NTCP values than IMPT and the differences were often significant. However, the differences between adaptive and non-adaptive treatment schemes were not significant, indicating that the NTCP benefit of adaptive treatment regimens is expected to be smaller than the expected difference between IMRT and IMPT.

A review on lymphocyte radiosensitivity and its impact on radiotherapy.

It is well known that radiation therapy causes lymphopenia in patients and that this is correlated with a negative outcome. The mechanism is not well understood because radiation can have both immunostimulatory and immunosuppressive effects. How tumor dose conformation, dose fractionation, and selective lymph node irradiation in radiation therapy does affect lymphopenia and immune response is an active area of research. In addition, understanding the impact of radiation on the immune system is important for the design and interpretation of clinical trials combining radiation with immune checkpoint inhibitors, both in terms of radiation dose and treatment schedules. Although only a few percent of the total lymphocyte population are circulating, it has been speculated that their increased radiosensitivity may contribute to, or even be the primary cause of, lymphopenia. This review summarizes published data on lymphocyte radiosensitivity based on human, small animal, and in vitro studies. The data indicate differences in radiosensitivity among lymphocyte subpopulations that affect their relative contribution and thus the dynamics of the immune response. In general, B cells appear to be more radiosensitive than T cells and NK cells appear to be the most resistant. However, the reported dose-response data suggest that in the context of lymphopenia in patients, aspects other than cell death must also be considered. Not only absolute lymphocyte counts, but also lymphocyte diversity and activity are likely to be affected by radiation. Taken together, the reviewed data suggest that it is unlikely that radiation-induced cell death in lymphocytes is the sole factor in radiation-induced lymphopenia.

The complexity of DNA damage by radiation follows a Gamma distribution: insights from the Microdosimetric Gamma Model.

Introduction: DNA damage is the main predictor of response to radiation therapy for cancer. Its Q8 quantification and characterization are paramount for treatment optimization, particularly in advanced modalities such as proton and alpha-targeted therapy. Methods: We present a novel approach called the Microdosimetric Gamma Model (MGM) to address this important issue. The MGM uses the theory of microdosimetry, specifically the mean energy imparted to small sites, as a predictor of DNA damage properties. MGM provides the number of DNA damage sites and their complexity, which were determined using Monte Carlo simulations with the TOPAS-nBio toolkit for monoenergetic protons and alpha particles. Complexity was used together with a illustrative and simplistic repair model to depict the differences between high and low LET radiations. Results: DNA damage complexity distributions were were found to follow a Gamma distribution for all monoenergetic particles studied. The MGM functions allowed to predict number of DNA damage sites and their complexity for particles not simulated with microdosimetric measurements (yF) in the range of those studied. Discussion: Compared to current methods, MGM allows for the characterization of DNA damage induced by beams composed of multi-energy components distributed over any time configuration and spatial distribution. The output can be plugged into ad hoc repair models that can predict cell killing, protein recruitment at repair sites, chromosome aberrations, and other biological effects, as opposed to current models solely focusing on cell survival. These features are particularly important in targeted alpha-therapy, for which biological effects remain largely uncertain. The MGM provides a flexible framework to study the energy, time, and spatial aspects of ionizing radiation and offers an excellent tool for studying and optimizing the biological effects of these radiotherapy modalities.

Evaluating the effect of setup uncertainty reduction and adaptation to geometric changes on normal tissue complication probability using online adaptive head and neck intensity modulated proton therapy.

Objective. To evaluate the impact of setup uncertainty reduction (SUR) and adaptation to geometrical changes (AGC) on normal tissue complication probability (NTCP) when using online adaptive head and neck intensity modulated proton therapy (IMPT).Approach.A cohort of ten retrospective head and neck cancer patients with daily scatter corrected cone-beam CT (CBCT) was studied. For each patient, two IMPT treatment plans were created: one with a 3 mm setup uncertainty robustness setting and one with no explicit setup robustness. Both plans were recalculated on the daily CBCT considering three scenarios: the robust plan without adaptation, the non-robust plan without adaptation and the non-robust plan with daily online adaptation. Online-adaptation was simulated using an in-house developed workflow based on GPU-accelerated Monte Carlo dose calculation and partial spot-intensity re-optimization. Dose distributions associated with each scenario were accumulated on the planning CT, where NTCP models for six toxicities were applied. NTCP values from each scenario were intercompared to quantify the reduction in toxicity risk induced by SUR alone, AGC alone and SUR and AGC combined. Finally, a decision tree was implemented to assess the clinical significance of the toxicity reduction associated with each mechanism.Main results. For most patients, clinically meaningful NTCP reductions were only achieved when SUR and AGC were performed together. In these conditions, total reductions in NTCP of up to 30.48 pp were obtained, with noticeable NTCP reductions for aspiration, dysphagia and xerostomia (mean reductions of 8.25, 5.42 and 5.12 pp respectively). While SUR had a generally larger impact than AGC on NTCP reductions, SUR alone did not induce clinically meaningful toxicity reductions in any patient, compared to only one for AGC alone.SignificanceOnline adaptive head and neck proton therapy can only yield clinically significant reductions in the risk of long-term side effects when combining the benefits of SUR and AGC.

Large anatomical changes in head-and-neck cancers - A dosimetric comparison of online and offline adaptive proton therapy.

Purpose: This work evaluates an online adaptive (OA) workflow for head-and-neck (H&N) intensity-modulated proton therapy (IMPT) and compares it with full offline replanning (FOR) in patients with large anatomical changes. Methods: IMPT treatment plans are created retrospectively for a cohort of eight H&N cancer patients that previously required replanning during the course of treatment due to large anatomical changes. Daily cone-beam CTs (CBCT) are acquired and corrected for scatter, resulting in 253 analyzed fractions. To simulate the FOR workflow, nominal plans are created on the planning-CT and delivered until a repeated-CT is acquired; at this point, a new plan is created on the repeated-CT. To simulate the OA workflow, nominal plans are created on the planning-CT and adapted at each fraction using a simple beamlet weight-tuning technique. Dose distributions are calculated on the CBCTs with Monte Carlo for both delivery methods. The total treatment dose is accumulated on the planning-CT. Results: Daily OA improved target coverage compared to FOR despite using smaller target margins. In the high-risk CTV, the median D98 degradation was 1.1 % and 2.1 % for OA and FOR, respectively. In the low-risk CTV, the same metrics yield 1.3 % and 5.2 % for OA and FOR, respectively. Smaller setup margins of OA reduced the dose to all OARs, which was most relevant for the parotid glands. Conclusion: Daily OA can maintain prescription doses and constraints over the course of fractionated treatment, even in cases of large anatomical changes, reducing the necessity for manual replanning in H&N IMPT.

TOPAS-imaging: extensions to the TOPAS simulation toolkit for medical imaging systems.

Objective. The TOol for PArticle Simulation (TOPAS) is a Geant4-based Monte Carlo software application that has been used for both research and clinical studies in medical physics. So far, most users of TOPAS have focused on radiotherapy-related studies, such as modeling radiation therapy delivery systems or patient dose calculation. Here, we present the first set of TOPAS extensions to make it easier for TOPAS users to model medical imaging systems.Approach. We used the extension system of TOPAS to implement pre-built, user-configurable geometry components such as detectors (e.g. flat-panel and multi-planar detectors) for various imaging modalities and pre-built, user-configurable scorers for medical imaging systems (e.g. digitizer chain).Main results. We developed a flexible set of extensions that can be adapted to solve research questions for a variety of imaging modalities. We then utilized these extensions to model specific examples of cone-beam CT (CBCT), positron emission tomography (PET), and prompt gamma (PG) systems. The first of these new geometry components, the FlatImager, was used to model example CBCT and PG systems. Detected signals were accumulated in each detector pixel to obtain the intensity of x-rays penetrating objects or prompt gammas from proton-nuclear interaction. The second of these new geometry components, the RingImager, was used to model an example PET system. Positron-electron annihilation signals were recorded in crystals of the RingImager and coincidences were detected. The simulated data were processed using corresponding post-processing algorithms for each modality and obtained results in good agreement with the expected true signals or experimental measurement.Significance. The newly developed extension is a first step to making it easier for TOPAS users to build and simulate medical imaging systems. Together with existing TOPAS tools, this extension can help integrate medical imaging systems with radiotherapy simulations for image-guided radiotherapy.

Neural network based ensemble model to predict radiation induced lymphopenia after concurrent chemo-radiotherapy for non-small cell lung cancer from two institutions.

The use of adjuvant Immune Checkpoint Inhibitors (ICI) after concurrent chemo-radiation therapy (CCRT) has become the standard of care for locally advanced non-small cell lung cancer (LA-NSCLC). However, prolonged radiotherapy regimens are known to cause radiation-induced lymphopenia (RIL), a long-neglected toxicity that has been shown to correlate with response to ICIs and survival of patients treated with adjuvant ICI after CCRT. In this study, we aim to develop a novel neural network (NN) approach that integrates patient characteristics, treatment related variables, and differential dose volume histograms (dDVH) of lung and heart to predict the incidence of RIL at the end of treatment. Multi-institutional data of 139 LA-NSCLC patients from two hospitals were collected for training and validation of our suggested model. Ensemble learning was combined with a bootstrap strategy to stabilize the model, which was evaluated internally using repeated cross validation. The performance of our proposed model was compared to conventional models using the same input features, such as Logistic Regression (LR) and Random Forests (RF), using the Area Under the Curve (AUC) of Receiver Operating Characteristics (ROC) curves. Our suggested model (AUC=0.77) outperformed the comparison models (AUC=0.72, 0.74) in terms of absolute performance, indicating that the convolutional structure of the network successfully abstracts additional information from the differential DVHs, which we studied using Gradient-weighted Class Activation Map. This study shows that clinical factors combined with dDVHs can be used to predict the risk of RIL for an individual patient and shows a path toward preventing lymphopenia using patient-specific modifications of the radiotherapy plan.

Intra-brain vascular models within the ICRP mesh-type adult reference phantoms for applications to internal dosimetry.

Objective. Phantoms of the International Commission on Radiological Protection provide a framework for standardized dosimetry. The modeling of internal blood vessels-essential to tracking circulating blood cells exposed during external beam radiotherapy and to account for radiopharmaceutical decays while still in blood circulation-is, however, limited to the major inter-organ arteries and veins. Intra-organ blood is accounted for only through the assignment of a homogeneous mixture of parenchyma and blood [single-region (SR) organs]. Our goal was to develop explicit dual-region (DR) models of intra-organ blood vasculature of the adult male brain (AMB) and adult female brain (AFB).Approach. A total of 4000 vessels were created amongst 26 vascular trees. The AMB and AFB models were then tetrahedralized for coupling to the PHITS radiation transport code. Absorbed fractions were computed for monoenergetic alpha particles, electrons, positrons, and photons for both decay sites within the blood vessels and for tissues outside these vessels. RadionuclideS-values were computed for 22 and 10 radionuclides commonly employed in radiopharmaceutical therapy and nuclear medicine diagnostic imaging, respectively.Main results. For radionuclide decays, values ofS(brain tissue ← brain blood) assessed in the traditional manner (SR) were higher than those computed using our DR models by factors of 1.92, 1.49, and 1.57 for therapeutic alpha-emitters, beta-emitters, and Auger electron-emitters, respectively in the AFB and by factors of 1.65, 1.37, and 1.42 for these same radionuclide categories in the AMB. Corresponding ratios of SR and DR values ofS(brain tissue ← brain blood) were 1.34 (AFB) and 1.26 (AMB) for four SPECT radionuclides, and were 1.32 (AFB) and 1.24 (AMB) for six common PET radionuclides.Significance. The methodology employed in this study can be explored in other organs of the body for proper accounting of blood self-dose for that fraction of the radiopharmaceutical still in general circulation.

Predicting Severity of Radiation Induced Lymphopenia in Individual Proton Therapy Patients for Varying Dose Rate and Fractionation Using Dynamic 4-Dimensional Blood Flow Simulations.

PURPOSE: Radiation-induced lymphopenia has gained attention recently as the result of its correlation with survival in a range of indications, particularly when combining radiation therapy (RT) with immunotherapy. The purpose of this study is to use a dynamic blood circulation model combined with observed lymphocyte depletion in patients to derive the in vivo radiosensitivity of circulating lymphocytes and study the effect of RT delivery parameters. METHODS AND MATERIALS: We assembled a cohort of 17 patients with hepatocellular carcinoma treated with proton RT alone in 15 fractions (fx) using conventional dose rates (beam-on time [BOT], 120 seconds) for whom weekly absolute lymphocyte counts (ALCs) during RT and follow-up were available. We used HEDOS, a time-dependent, whole-body, blood flow computational framework, in combination with explicit liver blood flow modeling, to calculate the dose volume histograms for circulating lymphocytes for changing BOTs (1 second-300 seconds) and fractionations (5 fx, 15 fx). From this, we used the linear cell survival model and an exponential model to determine patient-specific lymphocyte radiation sensitivity, α, and recovery, σ, respectively. RESULTS: The in vivo-derived patient-specific α had a median of 0.65 Gy-1 (range, 0.30-1.38). Decreasing BOT to 1 second led to an increased average end-of-treatment ALC of 27.5%, increasing to 60.3% when combined with the 5-fx regimen. Decreasing to 5 fx at the conventional dose rate led to an increase of 17.0% on average. The benefit of both increasing dose rate and reducing the number of fractions was patient specificࣧpatients with highly sensitive lymphocytes benefited most from decreasing BOT, whereas patients with slow lymphocyte recovery benefited most from the shorter fractionation regimen. CONCLUSIONS: We observed that increasing dose rate at the same fractionation reduced ALC depletion more significantly than reducing the number of fractions. High-dose-rates led to an increased sparing of lymphocytes when shortening the fractionation regimen, particularly for patients with radiosensitive lymphocytes at elevated risk.

Predictive Model of Liver Toxicity to Aid the Personalized Selection of Proton Versus Photon Therapy in Hepatocellular Carcinoma.

PURPOSE: Our objective was to develop an externally validated model for predicting liver toxicity after radiation therapy in patients with hepatocellular carcinoma (HCC) that can integrate both photon and proton dose distributions with patient-specific characteristics. METHODS AND MATERIALS: Training data consisted of all patients with HCC treated between 2008 and 2019 at our institution (n = 117, 60%/40% photon/proton). We developed a shallow convolutional neural network (CNN) to predict posttreatment liver dysfunction from the differential dose-volume histogram (DVH) and baseline liver metrics. To reduce bias and improve robustness, we used ensemble learning (CNNE). After a preregistered study analysis plan, we evaluated stability using internal bootstrap resampling and generalizability using a data set from a different institution (n = 88). Finally, we implemented a class activation map method to characterize the critical DVH subregions and benchmarked the model against logistic regression and XGBoost. The models were evaluated using the area under the receiver operating characteristic curve and area under the precision-recall curve. RESULTS: The CNNE model showed similar internal performance and robustness compared with the benchmarks. CNNE exceeded the benchmark models in external validation, with an area under the receiver operating characteristic curve of 0.78 versus 0.55 to 0.70, and an area under the precision-recall curve of 0.6 versus 0.43 to 0.52. The model showed improved predictive power in the photon group, excellent specificity in both modalities, and high sensitivity in the photon high-risk group. Models built solely on DVHs confirm outperformance of the CNNE and indicate that the proposed structure efficiently abstracts features from both proton and photon dose distributions. The activation map method demonstrates the importance of the low-dose bath and its interaction with low liver function at baseline. CONCLUSIONS: We developed and externally validated a patient-specific prediction model for hepatic toxicity based on the entire DVH and clinical factors that can integrate both photon and proton therapy cohorts. This model complements the new American Society for Radiation Oncology clinical practice guidelines and could support value-driven integration of proton therapy into the management of HCC.

Optically stimulated luminescence dosimeters for simultaneous measurement of point dose and dose-weighted LET in an adaptive proton therapy workflow.

Purpose: To demonstrate the suitability of optically stimulated luminescence detectors (OSLDs) for accurate simultaneous measurement of the absolute point dose and dose-weighted linear energy transfer (LETD) in an anthropomorphic phantom for experimental validation of daily adaptive proton therapy. Methods: A clinically realistic intensity-modulated proton therapy (IMPT) treatment plan was created based on a CT of an anthropomorphic head-and-neck phantom made of tissue-equivalent material. The IMPT plan was optimized with three fields to deliver a uniform dose to the target volume covering the OSLDs. Different scenarios representing inter-fractional anatomical changes were created by modifying the phantom. An online adaptive proton therapy workflow was used to recover the daily dose distribution and account for the applied geometry changes. To validate the adaptive workflow, measurements were performed by irradiating Al2O3:C OSLDs inside the phantom. In addition to the measurements, retrospective Monte Carlo simulations were performed to compare the absolute dose and dose-averaged LET (LETD) delivered to the OSLDs. Results: The online adaptive proton therapy workflow was shown to recover significant degradation in dose conformity resulting from large anatomical and positioning deviations from the reference plan. The Monte Carlo simulations were in close agreement with the OSLD measurements, with an average relative error of 1.4% for doses and 3.2% for LETD. The use of OSLDs for LET determination allowed for a correction for the ionization quenched response. Conclusion: The OSLDs appear to be an excellent detector for simultaneously assessing dose and LET distributions in proton irradiation of an anthropomorphic phantom. The OSLDs can be cut to almost any size and shape, making them ideal for in-phantom measurements to probe the radiation quality and dose in a predefined region of interest. Although we have presented the results obtained in the experimental validation of an adaptive proton therapy workflow, the same approach can be generalized and used for a variety of clinical innovations and workflow developments that require accurate assessment of point dose and/or average LET.