Modeling the Synergistic Impact of Yttrium 90 Radioembolization and Immune Checkpoint Inhibitors on Hepatocellular Carcinoma.

The impact of yttrium 90 radioembolization (Y90-RE) in combination with immune checkpoint inhibitors (ICIs) has recently gained attention. However, it is unclear how sequencing and dosage affect therapeutic efficacy. The purpose of this study was to develop a mathematical model to simulate the synergistic effects of Y90-RE and ICI combination therapy and find the optimal treatment sequences and dosages. We generated a hypothetical patient cohort and conducted simulations to apply different treatments to the same patient. The compartment of models is described with ordinary differential equations (ODEs), which represent targeted tumors, non-targeted tumors, and lymphocytes. We considered Y90-RE as a local treatment and ICIs as a systemic treatment. The model simulations show that Y90-RE and ICIs administered simultaneously yield greater benefits than subsequent sequential therapy. In addition, applying Y90-RE before ICIs has more benefits than applying ICIs before Y90-RE. Moreover, we also observed that the median PFS increased up to 31~36 months, and the DM rates at 3 years decreased up to 36~48% as the dosage of the two drugs increased (p < 0.05). The proposed model predicts a significant benefit of Y90-RE with ICIs from the results of the reduced irradiated tumor burden and the associated immune activation and suppression. Our model is expected to help optimize complex strategies and predict the efficacy of clinical trials for HCC patients.

Normal Tissue Complication Probability Modeling of Severe Radiation-Induced Lymphopenia Using Blood Dose for Patients With Hepatocellular Carcinoma.

PURPOSE: This study aimed to develop a normal tissue complication probability (NTCP) model to estimate the risk of severe radiation-induced lymphopenia (SRIL; absolute lymphocyte count [ALC] < 500/µL) by using the blood dose of patients with hepatocellular carcinoma (HCC). METHODS AND MATERIALS: We retrospectively collected data from 75 patients with HCC who received radiation therapy (RT) between 2015 and 2018. The hematological dose framework calculated blood dose-volume histograms (DVHs) using a predefined blood flow model, organ DVHs, the number of treatment fractions, and beam delivery time. A Lyman-Kutcher-Burman model with a generalized equivalent dose was used to establish the NTCP model, reflecting the whole-blood DVHs. Optimization of the Lyman-Kutcher-Burman parameters was conducted by minimizing a negative log-likelihood function. RESULTS: There were 6, 4, 18, 33, and 14 patients in the groups with radiation-induced lymphopenia grades 0, 1, 2, 3, and 4, respectively. The median pre- and post-RT ALC values were 1410/µL (range, 520-3710/µL) and 470/µL (range, 60-1760/µL), respectively. There was a correlation between mean blood dose and ALC depletion (Pearson r = -0.664; P < .001). The average mean blood doses in each radiation-induced lymphopenia group were 2.90 Gy (95% CI, 1.96-3.85 Gy) for grade 0 to 1, 5.29 Gy (95% CI, 4.12-6.45 Gy) for grade 2, 8.81 Gy (95% CI, 7.55-10.07 Gy) for grade 3, and 11.69 Gy (95% CI, 9.82-17.57 Gy) for grade 4. When applying the developed NTCP model to predict SRIL, the area under the receiver operating characteristic curve and Brier score values were 0.89 and 0.12, respectively. CONCLUSIONS: We developed the first NTCP model based on whole-blood DVHs for estimating SRIL after abdominal RT in patients with HCC. Our results showed a strong correlation between blood dose and ALC depletion, suggesting the potential to predict the risk of SRIL occurrence using blood dose.

Mathematical prediction with pretreatment growth rate of metastatic cancer on outcomes: implications for the characterization of oligometastatic disease.

Background: Oligometastatic disease (OMD) represents an indolent cancer status characterized by slow tumor growth and limited metastatic potential. The use of local therapy in the management of the condition continues to rise. This study aimed to investigate the advantage of pretreatment tumor growth rate in addition to baseline disease burden in characterizing OMDs, generally defined by the presence of ≤ 5 metastatic lesions. Methods: The study included patients with metastatic melanoma treated with pembrolizumab. Gross tumor volume of all metastases was contoured on imaging before (TP-1) and at the initiation of pembrolizumab (TP0). Pretreatment tumor growth rate was calculated by an exponential ordinary differential equation model using the sum of tumor volumes at TP-1 and TP0 and the time interval between TP-1. and TP0. Patients were divided into interquartile groups based on pretreatment growth rate. Overall survival, progression-free survival, and subsequent progression-free survival were the study outcomes. Results: At baseline, median cumulative volume and number of metastases were 28.4 cc (range, 0.4-1194.8 cc) and 7 (range, 1-73), respectively. The median interval between TP-1 and TP0 was -90 days and pretreatment tumor growth rate (×10-2 days-1) was median 4.71 (range -0.62 to 44.1). The slow-paced group (pretreatment tumor growth rate ≤ 7.6 ×10-2 days-1, the upper quartile) had a significantly higher overall survival rate, progression-free survival, and subsequent progression-free survival compared to those of the fast-paced group (pretreatment tumor growth rate > 7.6 ×10-2 days-1). Notably, these differences were prominent in the subgroup with >5 metastases. Conclusion: Pretreatment tumor growth rate is a novel prognostic metric associated with overall survival, progression-free survival, and subsequent progression-free survival among metastatic melanoma patients, especially patients with >5 metastases. Future prospective studies should validate the advantage of disease growth rate plus disease burden in better defining OMDs.

Incorporating axillary-lateral thoracic vessel juncture dosimetric variables improves model for predicting lymphedema in patients with breast cancer: A validation analysis.

Background: A relationship between the axillary-lateral thoracic vessel juncture (ALTJ) dose and lymphedema rate has been reported in patients with breast cancer. The purpose of this study was to validate this relationship and explore whether incorporation of the ALTJ dose-distribution parameters improves the prediction model's accuracy. Methods: A total of 1,449 women with breast cancer who were treated with multimodal therapies from two institutions were analyzed. We categorized regional nodal irradiation (RNI) as limited RNI, which excluded level I/II, vs extensive RNI, which included level I/II. The ALTJ was delineated retrospectively, and dosimetric and clinical parameters were analyzed to determine the accuracy of predicting the development of lymphedema. Decision tree and random forest algorithms were used to construct the prediction models of the obtained dataset. We used Harrell's C-index to assess discrimination. Results: The median follow-up time was 77.3 months, and the 5-year lymphedema rate was 6.8 %. According to the decision tree analysis, the lowest lymphedema rate (5-year, 1.2 %) was observed in patients with ≤ six removed lymph nodes and ≤ 66 % ALTJ V35Gy. The highest lymphedema rate was observed in patients with > 15 removed lymph nodes and an ALTJ maximum dose (Dmax) of > 53 Gy (5-year, 71.4 %). Patients with > 15 removed lymph nodes and an ALTJ Dmax ≤ 53 Gy had the second highest rate (5-year, 21.5 %). All other patients had relatively minor differences, with a rate of 9.5 % at 5 years. Random forest analysis revealed that the model's C-index increased from 0.84 to 0.90 if dosimetric parameters were included instead of RNI (P <.001). Conclusion: The prognostic value of ALTJ for lymphedema was externally validated. The estimation of lymphedema risk based on individual dose-distribution parameters of the ALTJ seemed more reliable than that based on the conventional RNI field design.

Absence of Tissue-Sparing Effects in Partial Proton FLASH Irradiation in Murine Intestine.

Ultra-high dose rate irradiation has been reported to protect normal tissues more than conventional dose rate irradiation. This tissue sparing has been termed the FLASH effect. We investigated the FLASH effect of proton irradiation on the intestine as well as the hypothesis that lymphocyte depletion is a cause of the FLASH effect. A 16 × 12 mm2 elliptical field with a dose rate of ~120 Gy/s was provided by a 228 MeV proton pencil beam. Partial abdominal irradiation was delivered to C57BL/6j and immunodeficient Rag1-/-/C57 mice. Proliferating crypt cells were counted at 2 days post exposure, and the thickness of the muscularis externa was measured at 280 days following irradiation. FLASH irradiation did not reduce the morbidity or mortality of conventional irradiation in either strain of mice; in fact, a tendency for worse survival in FLASH-irradiated mice was observed. There were no significant differences in lymphocyte numbers between FLASH and conventional-dose-rate mice. A similar number of proliferating crypt cells and a similar thickness of the muscularis externa following FLASH and conventional dose rate irradiation were observed. Partial abdominal FLASH proton irradiation at 120 Gy/s did not spare normal intestinal tissue, and no difference in lymphocyte depletion was observed. This study suggests that the effect of FLASH irradiation may depend on multiple factors, and in some cases dose rates of over 100 Gy/s do not induce a FLASH effect and can even result in worse outcomes.

A Mathematical Model for Predicting Patient Responses to Combined Radiotherapy with CTLA-4 Immune Checkpoint Inhibitors.

The purpose of this study was to develop a cell-cell interaction model that could predict a tumor's response to radiotherapy (RT) combined with CTLA-4 immune checkpoint inhibition (ICI) in patients with hepatocellular carcinoma (HCC). The previously developed model was extended by adding a new term representing tremelimumab, an inhibitor of CTLA-4. The distribution of the new immune activation term was derived from the results of a clinical trial for tremelimumab monotherapy (NCT01008358). The proposed model successfully reproduced longitudinal tumor diameter changes in HCC patients treated with tremelimumab (complete response = 0%, partial response = 17.6%, stable disease = 58.8%, and progressive disease = 23.6%). For the non-irradiated tumor control group, adding ICI to RT increased the clinical benefit rate from 8% to 32%. The simulation predicts that it is beneficial to start CTLA-4 blockade before RT in terms of treatment sequences. We developed a mathematical model that can predict the response of patients to the combined CTLA-4 blockade with radiation therapy. We anticipate that the developed model will be helpful for designing clinical trials with the ultimate aim of maximizing the efficacy of ICI-RT combination therapy.

Modeling of the FLASH effect for ion beam radiation therapy.

PURPOSE: Normal tissue sparing has been shown in preclinical studies under the ultra-fast dose rate condition, so-called FLASH radiotherapy. The preclinical and clinical FLASH studies are being conducted with various radiation modalities such as photons, protons, and heavy ions. The aim of this study is to propose a model to predict the dependency of the FLASH effect on linear energy transfer (LET) by quantifying the oxygen depletion. METHODS: We develop an analytical model to examine the FLASH sparing effect by incorporating time-varying oxygen depletion equation and oxygen enhancement ratios according to LET. The variations in oxygen enhancement ratio (OER) are quantified over time with different dose rate (Gy/s) and LET (keV/µm). The FLASH sparing effect (FSE) is defined as the ratio of DFLASH/Dconv where Dconv is the reference absorbed dose delivered at the conventional dose rate, and DFLASH is the absorbed dose delivered at a high dose rate that causes the same amount of biological damage. RESULTS: Our model suggests that the FLASH effect is significant only when the oxygen amount is at an intermediate level (10 âˆ¼ 100 mmHg). The FSE is increased as LET decreases, suggesting that LET less than 100 keV/µm is required to induce FLASH sparing effects in normal tissue. CONCLUSIONS: Oxygen depletion and recovery provide a quantitative model to understand the FLASH effect. These results highlight the FLASH sparing effects in normal tissue under the conditions with the intermediate oxygen level and low-LET region.

Prognosis prediction for glioblastoma multiforme patients using machine learning approaches: Development of the clinically applicable model.

BACKGROUND AND PURPOSE: We aimed to develop a clinically applicable prognosis prediction model predicting overall survival (OS) and progression-free survival (PFS) for glioblastoma multiforme (GBM) patients. MATERIALS AND METHODS: All 467 patients treated with concurrent chemoradiotherapy at Yonsei Cancer Center from 2016 to 2020 were included in this study. We developed a conventional linear regression, Cox proportional hazards (COX), and non-linear machine learning algorithms, random survival forest (RSF) and survival support vector machine (SVM) based on 16 clinical variables. After backward feature selection and hyperparameter tuning using grid search, we repeated 100 times of cross-validations to combat overfitting and enhance the model performance. Harrell's concordance index (C-index) and integrated brier score (IBS) were employed as quantitative performance metrics. RESULTS: In both predictions, RSF performed much better than COX and SVM. (For OS prediction: RSF C-index = 0.72 90%CI [0.71-0.72] and IBS = 0.12 90%CI [0.10-0.13]; For PFS prediction: RSF C-index = 0.70 90%CI [0.70-0.71] and IBS = 0.12 90%CI [0.10-0.14]). Permutation feature importance confirmed that MGMT promoter methylation, extent of resection, age, cone down planning target volume, and subventricular zone involvement are significant prognostic factors for OS. The importance of the extent of resection and MGMT promoter methylation was much higher than other selected input factors in PFS. Our final models accurately stratified two risk groups with root mean square errors less than 0.07. The sensitivity analysis revealed that our final models are highly applicable to newly diagnosed GBM patients. CONCLUSION: Our final models can provide a reliable outcome prediction for individual GBM. The final OS and PFS predicting models we developed accurately stratify high-risk groups up to 5-years, and the sensitivity analysis confirmed that both final models are clinically applicable.

Modeling of radiation effects to immune system: a review.

Cancer metastasis is the major cause of cancer mortality and accounts for about 90% of cancer death. Although radiation therapy has been considered to reduce the localized cancer burden, emerging evidence that radiation can potentially turn tumors into an in situ vaccine has raised significant interest in combining radiation with immunotherapy. However, the combination approach might be limited by the radiation-induced immunosuppression. Assessment of radiation effects on the immune system at the patient level is critical to maximize the systemic antitumor response of radiation. In this review, we summarize the developed solutions in three different categories for systemic radiation therapy: blood dose, radiation-induced lymphopenia, and tumor control. Furthermore, we address how they could be combined to optimize radiotherapy regimens and maximize their synergy with immunotherapy.

A Comparison of 2 Disease Burden Assessment Methods (3D Volume Versus the Number of Lesions) for Prognostication of Survival in Metastatic Melanoma: Implications for the Characterization of Oligometastatic Disease.

PURPOSE: Oligometastatic disease (OMD), generally defined by the presence of ≤5 metastatic lesions, represents an intermediate state between localized and widespread metastatic disease. This study aimed to question the conventional definition of OMD and assess the significance of the total volume and loci of metastases in characterizing OMD using an unselected metastatic melanoma cohort. METHODS AND MATERIALS: We identified 86 consecutive patients with metastatic melanoma who received pembrolizumab monotherapy from 2015 to 2020. We retrospectively contoured the gross tumor volumes of all metastatic lesions on baseline and follow-up imaging. The number, total volume, and loci information of metastases was collected. The primary endpoint was overall survival. A density histogram plot was used for tumor characteristic descriptions, and classification analysis using the decision tree and random forest methods was performed to determine the optimal combination of prognostic factors in the clinical setting. RESULTS: A total of 2728 gross tumor volumes were delineated. On baseline imaging, the median number and total volume of metastases was 7 (interquartile range, 3-17) and 28.4 cc (interquartile range, 8.4-88.78), respectively. The lymph node was the most common metastatic site (n = 46, 54%), followed by the lungs (n = 32, 37%), liver (n = 23, 27%), and bones (n = 21, 24%). Two-year overall survival rates of patients with 1 to 5, 6 to 10, 11 to 20, and >20 metastases were 58%, 47%, 31%, and 14%, respectively, and with ≤10, 11 to 30, 31 to 130, and >130 cc of metastatic volume were 64%, 43%, 33%, and 25%, respectively. K-adaptive partitioning revealed that the optimal cutoff was 20 and 37.9 cc. Decision tree and random forest analyses revealed that volume and loci (brain and liver metastases) were the most important factors (Harrell's C-index, 0.78). CONCLUSIONS: The OMD state could represent a continuous spectrum of disease burden instead of a binary phenomenon. We propose integrating the volumetric and spatial information of metastases into the characterization of OMD and the stratification tool of clinical trials in the metastatic setting, although external validation studies are needed.

Quantitative Imaging in Oncology.

The Special Issue of Tomography is a collection of articles focused on the quantitative imaging methods in clinical oncology [...].

Ipsilateral Recurrence of DCIS in Relation to Radiomics Features on Contrast Enhanced Breast MRI.

The purpose of this retrospective study was to investigate the association between ipsilateral recurrence of ductal carcinoma in situ (DCIS) and radiomics features from DCIS and contralateral normal breast on contrast enhanced breast MR imaging. A total of 163 patients with DCIS who underwent preoperative MR imaging between January 2010 and December 2014 were included (training cohort; n = 117, validation cohort; n = 46). Radiomics features were extracted from whole tumor volume of DCIS on early dynamic T1-subtraction images and from the contralateral normal breast on precontrast T1 and early dynamic T1-subtraction images. After feature selection, a Rad-score was established by LASSO Cox regression model. Performance of Rad-score was evaluated by the receiver operating characteristic (ROC) curve and Kaplan Meier curve with log rank test. The Rad-score was significantly associated with ipsilateral recurrence free survival (RFS). The low-risk group with a low Rad-score showed higher ipsilateral RFS than the high-risk group with a high Rad-score in both training and validation cohorts (p < 0.01). The Rad-score based on radiomics features from DCIS and contralateral normal breast on breast MR imaging showed the potential for prediction of ipsilateral RFS of DCIS.

Locoregional Recurrence Prediction Using a Deep Neural Network of Radiological and Radiotherapy Images.

Radiation therapy (RT) is an important and potentially curative modality for head and neck squamous cell carcinoma (HNSCC). Locoregional recurrence (LR) of HNSCC after RT is ranging from 15% to 50% depending on the primary site and stage. In addition, the 5-year survival rate of patients with LR is low. To classify high-risk patients who might develop LR, a deep learning model for predicting LR needs to be established. In this work, 157 patients with HNSCC who underwent RT were analyzed. Based on the National Cancer Institute's multi-institutional TCIA data set containing FDG-PET/CT/dose, a 3D deep learning model was proposed to predict LR without time-consuming segmentation or feature extraction. Our model achieved an averaged area under the curve (AUC) of 0.856. Adding clinical factors into the model improved the AUC to an average of 0.892 with the highest AUC of up to 0.974. The 3D deep learning model could perform individualized risk quantification of LR in patients with HNSCC without time-consuming tumor segmentation.

Mathematical Modeling to Simulate the Effect of Adding Radiation Therapy to Immunotherapy and Application to Hepatocellular Carcinoma.

PURPOSE: To develop a comprehensive framework to simulate the response to immune checkpoint inhibitors (ICIs) in combination with radiation therapy (RT) and to apply the framework for investigating ICI-RT combination regimen in patients with hepatocellular carcinoma (HCC). METHODS AND MATERIALS: The mechanistic mathematical model is based on dynamic biological interactions between the immune system and the tumor using input data from patient blood samples and outcomes of clinical trials. The cell compartments are described by ordinary differential equations and represent irradiated and nonirradiated tumor cells and lymphocytes. The effect of ICI is modeled using an immune activation term that is based on tumor size changes observed in a phase 1/2 clinical trial for HCC. Simulated combination regimen are based on ongoing ICI-RT trials. RESULTS: The proposed framework successfully describes tumor volume trajectories observed in early-stage clinical trials of durvalumab monotherapy in patients with HCC. For ICI-RT treatment regimen the irradiated tumor fraction is the most important parameter for the efficacy. For 90% of the tumor cells being irradiated, adding RT to ICI yields an increase in clinical benefit from 33% to 71% in nonirradiated tumor sites. The model agrees with clinical data showing an association of outcome with initial tumor volume and lymphocyte counts. We demonstrate model application in clinical trial design to predict progression-free survival curves, showing that the cohort size to show significant improvement heavily depends on the irradiated tumor fraction. CONCLUSIONS: We present a framework extending radiation cell kill models to include circulating lymphocytes and the effect of ICIs and enable simulation of combination strategies. The simulations predict that a significant amount of the benefit from RT in combination with ICI stems from the reduction in irradiated tumor burden and associated immune suppression. This aspect needs to be included in the interpretation of outcomes and the design of novel combination trials.

Microdosimetric-Kinetic Model for Radio-enhancement of Gold Nanoparticles: Comparison with LEM.

Numerous studies have strongly supported the application of gold nanoparticles (GNPs) as radio-enhanced agents. In our previous study, the local effect model (LEM I) was adopted to predict the cell survival for MDA-MB-231 cells exposed to 150 kVp X rays after 500 µg/ml GNPs treatment. However, microdosimetric quantities could not be obtained, which were correlated with biological effects on cells. Thus, we developed microdosimetric kinetic model (MKM) for GNP radio-enhancement (GNP-MKM), which uses the microdosimetric quantities such as dose-mean lineal energy with subcellular domain size. Using the Monte Carlo simulation tool Geant4, we estimated the dose-mean lineal energy with secondary radiations from GNPs and absorbed dose in the nucleus. The variations in MKM parameters for different domain sizes, and GNP concentrations, were calculated to compare the survival fractions predicted by both models. With a domain radius of 500 nm and a threshold dose of 20 Gy, the sensitizer enhancement ratio predicted by GNP-MKM and GNP-LEM was 1.41 and 1.29, respectively. The GNP-MKM predictions were much more strongly dependent on the domain size than were the GNP-LEM on the threshold dose. These findings provide another method to predict survival fraction for the GNP radio-enhancement.

A tumor-immune interaction model for hepatocellular carcinoma based on measured lymphocyte counts in patients undergoing radiotherapy.

PURPOSE: The impact of radiation therapy on the immune system has recently gained attention particularly when delivered in combination with immunotherapy. However, it is unclear how different treatment fractionation regimens influence the interaction between the immune system and radiation. The goal of this work was to develop a mathematical model that quantifies both the immune stimulating as well as the immunosuppressive effects of radiotherapy and simulates the effects of different fractionation regimens based on patient data. METHODS AND MATERIALS: The framework describes the temporal evolution of tumor cells, lymphocytes, and inactivated dying tumor cells releasing antigens during radiation therapy, specifically modeling how recruited lymphocytes inhibit tumor progression. The parameters of the model were partly taken from the literature and in part extracted from blood samples (circulating lymphocytes: CLs) collected from hepatocellular carcinoma patients undergoing radiotherapy and their outcomes. The dose volume histograms to circulating lymphocytes were calculated with a probability-based model. RESULTS: Based on the fitted parameters, the model enabled a study into the depletion and recovery of CLs in patients as a function of fractionation regimen. Our results quantify the ability of short fractionation regimens to lead to shorter periods of lymphocyte depletion and predict faster recovery after the end of treatment. The model shows that treatment breaks between fractions can prolong the period of lymphocyte depletion and should be avoided. CONCLUSIONS: This study introduces a mathematical model for tumor-immune interactions using clinically extracted radiotherapy patient data, which can be applied to design trials aimed at minimizing lymphocyte depleting effects in radiation therapy.

Roadmap for metal nanoparticles in radiation therapy: current status, translational challenges, and future directions.

This roadmap outlines the potential roles of metallic nanoparticles (MNPs) in the field of radiation therapy. MNPs made up of a wide range of materials (from Titanium, Z = 22, to Bismuth, Z = 83) and a similarly wide spectrum of potential clinical applications, including diagnostic, therapeutic (radiation dose enhancers, hyperthermia inducers, drug delivery vehicles, vaccine adjuvants, photosensitizers, enhancers of immunotherapy) and theranostic (combining both diagnostic and therapeutic), are being fabricated and evaluated. This roadmap covers contributions from experts in these topics summarizing their view of the current status and challenges, as well as expected advancements in technology to address these challenges.

Modulation of nanoparticle uptake, intracellular distribution, and retention with docetaxel to enhance radiotherapy.

OBJECTIVE: One of the major issues in current radiotherapy (RT) is the normal tissue toxicity. A smart combination of agents within the tumor would allow lowering the RT dose required while minimizing the damage to healthy tissue surrounding the tumor. We chose gold nanoparticles (GNPs) and docetaxel (DTX) as our choice of two radiosensitizing agents. They have a different mechanism of action which could lead to a synergistic effect. Our first goal was to assess the variation in GNP uptake, distribution, and retention in the presence of DTX. Our second goal was to assess the therapeutic results of the triple combination, RT/GNPs/DTX. METHODS: We used HeLa and MDA-MB-231 cells for our study. Cells were incubated with GNPs (0.2 nM) in the absence and presence of DTX (50 nM) for 24 h to determine uptake, distribution, and retention of NPs. For RT experiments, treated cells were given a 2 Gy dose of 6 MV photons using a linear accelerator. RESULTS: Concurrent treatment of DTX and GNPs resulted in over 85% retention of GNPs in tumor cells. DTX treatment also forced GNPs to be closer to the most important target, the nucleus, resulting in a decrease in cell survival and increase in DNA damage with the triple combination of RT/ GNPs/DTX vs RT/DTX. Our experimental therapeutic results were supported by Monte Carlo simulations. CONCLUSION: The ability to not only trap GNPs at clinically feasible doses but also to retain them within the cells could lead to meaningful fractionated treatments in future combined cancer therapy. Furthermore, the suggested triple combination of RT/GNPs/DTX may allow lowering the RT dose to spare surrounding healthy tissue. ADVANCES IN KNOWLEDGE: This is the first study to show intracellular GNP transport disruption by DTX, and its advantage in radiosensitization.

Measuring radioenhancement by gold nanofilms: Comparison with analytical calculations.

PURPOSE: To measure radioenhancement by gold nanoparticles (GNPs) using gold nanofilms (GNFs). METHODS: GNFs of 20-100 nm thicknesses were prepared. The GNF attached to radiochromic film (RCF) was irradiated using 50, 220 kVp, and 6 MV X-rays. The radiation doses to the active layer of RCF with and without GNF were measured using an optical flatbed scanner and Raman spectrometer to estimate the dose enhancement factor (DEF). For verification, analytical calculations of DEF within the thickness of active layer and the ranges of secondary electrons were carried out. RESULTS: The DEFs for GNFs of 20 to 100 nm thicknesses measured by an optical scanner ranged from 2.1 to 6.1 at 50 kVp and 1.6 to 4.9 at 220 kVp. Similarly, the DEFs measured by Raman spectroscopy ranged from 2.6 to 4.6 at 50 kVp and 2.2 to 4.8 at 220 kVp. The calculated DEFs ranged from 1.5 to 3.6 at 50 kVp and from 1.7 to 4.7 at 220 kVp. Almost no dose enhancement was observed in 6 MV X-ray. The analytical DEFs seemed to be underestimated by averaging local enhancement over the entire active layer. However, analytical DEFs within the ranges of secondary electrons was much higher than the measured macroscopic DEFs. CONCLUSIONS: The experimental and analytical approaches developed in this study could quantitatively estimate radioenhancement by GNPs. Due to a short range of low-energy electrons emitted from gold, the microscopic radioenhancement within the ranges of low-energy electrons would be particularly important in a cell.

Modeling scattered radiation from multi-leaf collimators (MLCs) to improve calculation accuracy of in-air output ratio.

This study aims to model an extra-focal source for the scattered radiation from multi-leaf collimators (MLCs), namely an MLC scatter source, and to correct in-air output ratio (Sc) calculated using the conventional dual source model (DSM) to achieve better accuracy of point dose calculation. To develop the MLC scatter source, a 6 MV photon beam from a Varian Clinac® iX linear accelerator with millennium 120 MLCs was used. It was assumed that the position for the MLC scatter source was located at the center of the MLC, consisting of line-based and area-based sources to consider the characteristics of the scattered radiation from the MLCs empirically. Based on the measured Sc values for MLC-defined fields, optimal parameters for the line-based and area-based sources were calculated using optimization process. For evaluation of proposed method, measurements were taken for various MLC-defined square and irregular fields. The Sc values calculated using the proposed MLC scatter source and conventional DSM were compared with the measured data. For MLC-defined square fields, the measured Sc values showed better agreement with those calculated using the MLC scatter source (the mean difference was - 0.03%) compared with those calculated using the DSM (the mean difference was 0.18%). For MLC-defined irregular fields, the maximum dose differences between measurements and calculations using the MLC scatter source and DSM were 0.54% and 1.45%, respectively. The developed MLC scatter source could improve the accuracy of Sc calculation for both square and irregular fields defined by MLCs.