Mechanisms of Action in FLASH Radiotherapy: A Comprehensive Review of Physicochemical and Biological Processes on Cancerous and Normal Cells.

The advent of FLASH radiotherapy (FLASH-RT) has brought forth a paradigm shift in cancer treatment, showcasing remarkable normal cell sparing effects with ultra-high dose rates (>40 Gy/s). This review delves into the multifaceted mechanisms underpinning the efficacy of FLASH effect, examining both physicochemical and biological hypotheses in cell biophysics. The physicochemical process encompasses oxygen depletion, reactive oxygen species, and free radical recombination. In parallel, the biological process explores the FLASH effect on the immune system and on blood vessels in treatment sites such as the brain, lung, gastrointestinal tract, skin, and subcutaneous tissue. This review investigated the selective targeting of cancer cells and the modulation of the tumor microenvironment through FLASH-RT. Examining these mechanisms, we explore the implications and challenges of integrating FLASH-RT into cancer treatment. The potential to spare normal cells, boost the immune response, and modify the tumor vasculature offers new therapeutic strategies. Despite progress in understanding FLASH-RT, this review highlights knowledge gaps, emphasizing the need for further research to optimize its clinical applications. The synthesis of physicochemical and biological insights serves as a comprehensive resource for cell biology, molecular biology, and biophysics researchers and clinicians navigating the evolution of FLASH-RT in cancer therapy.

A user-friendly deep learning application for accurate lung cancer diagnosis.

BACKGROUND: Accurate diagnosis and subsequent delineated treatment planning require the experience of clinicians in the handling of their case numbers. However, applying deep learning in image processing is useful in creating tools that promise faster high-quality diagnoses, but the accuracy and precision of 3-D image processing from 2-D data may be limited by factors such as superposition of organs, distortion and magnification, and detection of new pathologies. The purpose of this research is to use radiomics and deep learning to develop a tool for lung cancer diagnosis. METHODS: This study applies radiomics and deep learning in the diagnosis of lung cancer to help clinicians accurately analyze the images and thereby provide the appropriate treatment planning. 86 patients were recruited from Bach Mai Hospital, and 1012 patients were collected from an open-source database. First, deep learning has been applied in the process of segmentation by U-NET and cancer classification via the use of the DenseNet model. Second, the radiomics were applied for measuring and calculating diameter, surface area, and volume. Finally, the hardware also was designed by connecting between Arduino Nano and MFRC522 module for reading data from the tag. In addition, the displayed interface was created on a web platform using Python through Streamlit. RESULTS: The applied segmentation model yielded a validation loss of 0.498, a train loss of 0.27, a cancer classification validation loss of 0.78, and a training accuracy of 0.98. The outcomes of the diagnostic capabilities of lung cancer (recognition and classification of lung cancer from chest CT scans) were quite successful. CONCLUSIONS: The model provided means for storing and updating patients' data directly on the interface which allowed the results to be readily available for the health care providers. The developed system will improve clinical communication and information exchange. Moreover, it can manage efforts by generating correlated and coherent summaries of cancer diagnoses.

Dosimetric and radiobiological comparison between conventional and hypofractionated breast treatment plans using the Halcyon system.

Purpose: The objective of this research is to compare the efficacy of conventional and hypofractionated radiotherapy treatment plans for breast cancer patients, with a specific focus on the unique features of the Halcyon system. Methods and materials: The study collected and analyzed dose volume histogram (DVH) data for two groups of treatment plans implemented using the Halcyon system. The first group consisted of 19 patients who received conventional fractionated (CF) treatment with a total dose of 50 Gy in 25 fractions, while the second group comprised 9 patients who received hypofractionated (HF) treatment with a total dose of 42.56 Gy in 16 fractions. The DVH data was used to calculate various parameters, including tumor control probability (TCP), normal tissue complication probability (NTCP), and equivalent uniform dose (EUD), using radiobiological models. Results: The results indicated that the CF plan resulted in higher TCP but lower NTCP for the lungs compared to the HF plan. The EUD for the HF plan was approximately 49 Gy (114% of its total dose) while that for the CF plan was around 53 Gy (107% of its total dose). Conclusions: The analysis suggests that while the CF plan is better at controlling tumors, it is not as effective as the HF plan in minimizing side effects. Additionally, it is suggested that there may be an optimal configuration for the HF plan that can provide the same or higher EUD than the CF plan.

Developing an AI-Assisted Educational Chatbot for Radiotherapy Using the IBM Watson Assistant Platform.

Objectives: This study aims to make radiotherapy knowledge regarding healthcare accessible to the general public by developing an AI-powered chatbot. The interactive nature of the chatbot is expected to facilitate better understanding of information on radiotherapy through communication with users. Methods: Using the IBM Watson Assistant platform on IBM Cloud, the chatbot was constructed following a pre-designed flowchart that outlines the conversation flow. This approach ensured the development of the chatbot with a clear mindset and allowed for effective tracking of the conversation. The chatbot is equipped to furnish users with information and quizzes on radiotherapy to assess their understanding of the subject. Results: By adopting a question-and-answer approach, the chatbot can engage in human-like communication with users seeking information about radiotherapy. As some users may feel anxious and struggle to articulate their queries, the chatbot is designed to be user-friendly and reassuring, providing a list of questions for the user to choose from. Feedback on the chatbot's content was mostly positive, despite a few limitations. The chatbot performed well and successfully conveyed knowledge as intended. Conclusions: There is a need to enhance the chatbot's conversation approach to improve user interaction. Including translation capabilities to cater to individuals with different first languages would also be advantageous. Lastly, the newly launched ChatGPT could potentially be developed into a medical chatbot to facilitate knowledge transfer.

FLASH Radiotherapy and the Use of Radiation Dosimeters.

Radiotherapy (RT) using ultra-high dose rate (UHDR) radiation, known as FLASH RT, has shown promising results in reducing normal tissue toxicity while maintaining tumor control. However, implementing FLASH RT in clinical settings presents technical challenges, including limited depth penetration and complex treatment planning. Monte Carlo (MC) simulation is a valuable tool for dose calculation in RT and has been investigated for optimizing FLASH RT. Various MC codes, such as EGSnrc, DOSXYZnrc, and Geant4, have been used to simulate dose distributions and optimize treatment plans. Accurate dosimetry is essential for FLASH RT, and radiation detectors play a crucial role in measuring dose delivery. Solid-state detectors, including diamond detectors such as microDiamond, have demonstrated linear responses and good agreement with reference detectors in UHDR and ultra-high dose per pulse (UHDPP) ranges. Ionization chambers are commonly used for dose measurement, and advancements have been made to address their response nonlinearities at UHDPP. Studies have proposed new calculation methods and empirical models for ion recombination in ionization chambers to improve their accuracy in FLASH RT. Additionally, strip-segmented ionization chamber arrays have shown potential for the experimental measurement of dose rate distribution in proton pencil beam scanning. Radiochromic films, such as GafchromicTM EBT3, have been used for absolute dose measurement and to validate MC simulation results in high-energy X-rays, triggering the FLASH effect. These films have been utilized to characterize ionization chambers and measure off-axis and depth dose distributions in FLASH RT. In conclusion, MC simulation provides accurate dose calculation and optimization for FLASH RT, while radiation detectors, including diamond detectors, ionization chambers, and radiochromic films, offer valuable tools for dosimetry in UHDR environments. Further research is needed to refine treatment planning techniques and improve detector performance to facilitate the widespread implementation of FLASH RT, potentially revolutionizing cancer treatment.

Exploring the merits of research performance measures that comply with the San Francisco Declaration on Research Assessment and strategies to overcome barriers of adoption: qualitative interviews with administrators and researchers.

BACKGROUND: In prior research, we identified and prioritized ten measures to assess research performance that comply with the San Francisco Declaration on Research Assessment, a principle adopted worldwide that discourages metrics-based assessment. Given the shift away from assessment based on Journal Impact Factor, we explored potential barriers to implementing and adopting the prioritized measures. METHODS: We identified administrators and researchers across six research institutes, conducted telephone interviews with consenting participants, and used qualitative description and inductive content analysis to derive themes. RESULTS: We interviewed 18 participants: 6 administrators (research institute business managers and directors) and 12 researchers (7 on appointment committees) who varied by career stage (2 early, 5 mid, 5 late). Participants appreciated that the measures were similar to those currently in use, comprehensive, relevant across disciplines, and generated using a rigorous process. They also said the reporting template was easy to understand and use. In contrast, a few administrators thought the measures were not relevant across disciplines. A few participants said it would be time-consuming and difficult to prepare narratives when reporting the measures, and several thought that it would be difficult to objectively evaluate researchers from a different discipline without considerable effort to read their work. Strategies viewed as necessary to overcome barriers and support implementation of the measures included high-level endorsement of the measures, an official launch accompanied by a multi-pronged communication strategy, training for both researchers and evaluators, administrative support or automated reporting for researchers, guidance for evaluators, and sharing of approaches across research institutes. CONCLUSIONS: While participants identified many strengths of the measures, they also identified a few limitations and offered corresponding strategies to address the barriers that we will apply at our organization. Ongoing work is needed to develop a framework to help evaluators translate the measures into an overall assessment. Given little prior research that identified research assessment measures and strategies to support adoption of those measures, this research may be of interest to other organizations that assess the quality and impact of research.

Depth Dose Enhancement in Orthovoltage Nanoparticle-Enhanced Radiotherapy: A Monte Carlo Phantom Study.

BACKGROUND: This study was to examine the depth dose enhancement in orthovoltage nanoparticle-enhanced radiotherapy for skin treatment by investigating the impact of various photon beam energies, nanoparticle materials, and nanoparticle concentrations. METHODS: A water phantom was utilized, and different nanoparticle materials (gold, platinum, iodine, silver, iron oxide) were added to determine the depth doses through Monte Carlo simulation. The clinical 105 kVp and 220 kVp photon beams were used to compute the depth doses of the phantom at different nanoparticle concentrations (ranging from 3 mg/mL to 40 mg/mL). The dose enhancement ratio (DER), which represents the ratio of the dose with nanoparticles to the dose without nanoparticles at the same depth in the phantom, was calculated to determine the dose enhancement. RESULTS: The study found that gold nanoparticles outperformed the other nanoparticle materials, with a maximum DER value of 3.77 at a concentration of 40 mg/mL. Iron oxide nanoparticles exhibited the lowest DER value, equal to 1, when compared to other nanoparticles. Additionally, the DER value increased with higher nanoparticle concentrations and lower photon beam energy. CONCLUSIONS: It is concluded in this study that gold nanoparticles are the most effective in enhancing the depth dose in orthovoltage nanoparticle-enhanced skin therapy. Furthermore, the results suggest that increasing nanoparticle concentration and decreasing photon beam energy lead to increased dose enhancement.

Learning the Treatment Process in Radiotherapy Using an Artificial Intelligence-Assisted Chatbot: Development Study.

BACKGROUND: In knowledge transfer for educational purposes, most cancer hospital or center websites have existing information on cancer health. However, such information is usually a list of topics that are neither interactive nor customized to offer any personal touches to people facing dire health crisis and to attempt to understand the concerns of the users. Patients with cancer, their families, and the general public accessing the information are often in challenging, stressful situations, wanting to access accurate information as efficiently as possible. In addition, there is seldom any comprehensive information specifically on radiotherapy, despite the large number of older patients with cancer, to go through the treatment process. Therefore, having someone with professional knowledge who can listen to them and provide the medical information with good will and encouragement would help patients and families struggling with critical illness, particularly during the lingering pandemic. OBJECTIVE: This study created a novel virtual assistant, a chatbot that can explain the radiation treatment process to stakeholders comprehensively and accurately, in the absence of any similar software. This chatbot was created using the IBM Watson Assistant with artificial intelligence and machine learning features. The chatbot or bot was incorporated into a resource that can be easily accessed by the general public. METHODS: The radiation treatment process in a cancer hospital or center was described by the radiotherapy process: patient diagnosis, consultation, and prescription; patient positioning, immobilization, and simulation; 3D-imaging for treatment planning; target and organ contouring; radiation treatment planning; patient setup and plan verification; and treatment delivery. The bot was created using IBM Watson (IBM Corp) assistant. The natural language processing feature in the Watson platform allowed the bot to flow through a given conversation structure and recognize how the user responds based on recognition of similar given examples, referred to as intents during development. Therefore, the bot can be trained using the responses received, by recognizing similar responses from the user and analyzing using Watson natural language processing. RESULTS: The bot is hosted on a website by the Watson application programming interface. It is capable of guiding the user through the conversation structure and can respond to simple questions and provide resources for requests for information that was not directly programmed into the bot. The bot was tested by potential users, and the overall averages of the identified metrics are excellent. The bot can also acquire users' feedback for further improvements in the routine update. CONCLUSIONS: An artificial intelligence-assisted chatbot was created for knowledge transfer regarding radiation treatment process to the patients with cancer, their families, and the general public. The bot that is supported by machine learning was tested, and it was found that the bot can provide information about radiotherapy effectively.

Dosimetric Impact on the Flattening Filter and Addition of Gold Nanoparticles in Radiotherapy: A Monte Carlo Study on Depth Dose Using the 6 and 10 MV FFF Photon Beams.

PURPOSE: This phantom study investigated through Monte Carlo simulation how the dose enhancement varied with depth, when gold nanoparticles (NPs) were added using the flattening filter-free (FFF) photon beams in gold NP-enhanced radiotherapy. METHOD: A phantom with materials varying from pure water to a mixture of water and gold NPs at different concentrations (3-40 mg/mL) were irradiated by the 6 and 10 MV flattening filter (FF) and FFF photon beams. Monte Carlo simulations were carried out to determine the depth doses along the central beam axis of the phantom up to a depth of 40 cm. The dose enhancement ratio (DER) and FFF enhancement ratio (FFFER) were calculated based on the Monte Carlo results. RESULTS: The DER values were found decreased with an increase of depth and increase of NP concentration in the phantom. For the maximum NP concentration of 40 mg/mL, the DER values decreased 6.9, 12, 4.6 and 7.2% at a phantom depth from 2 to 40 cm, using the 6 MV FF, 6 MV FFF, 10 MV FF and 10 MV FFF photon beams, respectively. The maximum DER values for the 6 MV beams were 1.08 (FF) and 1.14 (FFF), while those for the 10 MV beams were 1.04 (FF) and 1.07 (FFF). When the FF was removed from the linear accelerator head, the FFFER showed a more significant increase of dose enhancement for the 6 MV beams (1.057) than the 10 MV (1.031). CONCLUSION: From the DER and FFFER values based on the Monte Carlo results, it is concluded that the dose enhancement with depth was dependent on the NP and beam variables, namely, NP concentration, presence of FF in the beam and beam energy. Dose enhancement was more significant when using the lower photon beam energy (i.e., 6 MV), FFF photon beam and higher NP concentration in the study.

Evaluation of Dosimetric Effect of Bone Scatter on Nanoparticle-Enhanced Orthovoltage Radiotherapy: A Monte Carlo Phantom Study.

In nanoparticle (NP)-enhanced orthovoltage radiotherapy, bone scatter affected dose enhancement at the skin lesion in areas such as the forehead, chest wall, and knee. Since each of these treatment sites have a bone, such as the frontal bone, rib, or patella, underneath the skin lesion and this bone is not considered in dose delivery calculations, uncertainty arises in the evaluation of dose enhancement with the addition of NPs in radiotherapy. To investigate the impact of neglecting the effect of bone scatter, Monte Carlo simulations based on heterogeneous phantoms were carried out to determine and compare the dose enhancement ratio (DER), when a bone was and was not present underneath the skin lesion. For skin lesions with added NPs, Monte Carlo simulations were used to calculate the DER values using different elemental NPs (gold, platinum, silver, iodine, as well as iron oxide), in varying NP concentrations (3−40 mg/mL), at two different photon beam energies (105 and 220 kVp). It was found that DER values at the skin lesion increased with the presence of bone when there was a higher atomic number of NPs, a higher NP concentration, and a lower photon beam energy. When comparing DER values with and without bone, using the same NP elements, NP concentration, and beam energy, differences were found in the range 0.04−3.55%, and a higher difference was found when the NP concentration increased. By considering the uncertainty in the DER calculation, the effect of bone scatter became significant to the dose enhancement (>2%) when the NP concentration was higher than 18 mg/mL. This resulted in an underestimation of dose enhancement at the skin lesion, when the bone underneath the tumour was neglected during orthovoltage radiotherapy.

Recent Advances in Functionalized Nanoparticles in Cancer Theranostics.

Cancer theranostics is the combination of diagnosis and therapeutic approaches for cancer, which is essential in personalized cancer treatment. The aims of the theranostics application of nanoparticles in cancer detection and therapy are to reduce delays in treatment and hence improve patient care. Recently, it has been found that the functionalization of nanoparticles can improve the efficiency, performance, specificity and sensitivity of the structure, and increase stability in the body and acidic environment. Moreover, functionalized nanoparticles have been found to possess a remarkable theranostic ability and have revolutionized cancer treatment. Each cancer treatment modality, such as MRI-guided gene therapy, MRI-guided thermal therapy, magnetic hyperthermia treatment, MRI-guided chemotherapy, immunotherapy, photothermal and photodynamic therapy, has its strengths and weaknesses, and combining modalities allows for a better platform for improved cancer control. This is why cancer theranostics have been investigated thoroughly in recent years and enabled by functionalized nanoparticles. In this topical review, we look at the recent advances in cancer theranostics using functionalized nanoparticles. Through understanding and updating the development of nanoparticle-based cancer theranostics, we find out the future challenges and perspectives in this novel type of cancer treatment.

Dosimetric and radiobiological comparison in head-and-neck radiotherapy using JO-IMRT and 3D-CRT.

Introduction: Dosimetric and radiobiological evaluations for the Jaws-only Intensity-modulated radiotherapy (JO-IMRT) technique for head and neck jaws-only intensity-modulated radiation therapy (JO-IMRT) and 3D conformal radiation therapy (3D-CRT). To compare the head-and-neck therapeutic approaches utilizing JO-IMRT and 3D-CRT techniques, different radiation dose indices were calculated, including: conformity index (CI), homogeneity index (HI), and radiobiological variables like Niemierko's equivalent uniform dose based tumor control probability (TCP) of planning target volume (PTV), normal tissue complication probability (NTCP) of organs at risk (OAR) (brainstem, spinal cord, and parotid grand). Materials and methods: Twenty-five nasopharynx patients were studied using the Prowess Panther Treatment Planning System (Prowess Inc). The results were compared with the dose distribution obtained using 3D-CRT. Results: Regarding tumor coverage and CI, JO-IMRT showed better results than 3D-CRT. The average doses received by the PTVs were quite similar: 72.1 ± 0.8 Gy by 3D-CRT and 72.5 ± 0.6 Gy by JO-IMRT plans (p > 0.05). The mean doses received by the parotid gland were 56.7 ± 0.7 Gy by 3D-CRT and 26.8 ± 0.3 Gy by JO-IMRT (p > 0.05). The HI and CI were 0.13 ± 0.01 and 0.14 ± 0.05 and (p > 0.05) by 3D-CRT and 0.83 ± 0.05 and 0.73 ± 0.10 by JO-IMRT (p < 0.05). The average TCP of PTV was 0.82 ± 0.08 by 3D-CRT and 0.92 ± 0.02 by JO-IMRT. Moreover, the NTCP of the parotid glands, brain stem, and spinal cord were lower using the JO-IMRT than 3D-CRT plans. In comparison to the 3D-CRT approach, the JO-IMRT technique was able to boost dose coverage to the PTV, improve the target's CI and HI, and spare the parotid glands. This suggests the power of the JO-IMRT over 3D-CRT in head-and-neck radiotherapy.

Special Issue: Application of Nanomaterials in Biomedical Imaging and Cancer Therapy.

Nanomaterials of different types-namely, inorganic-based, organic-based, carbon-based, and composite-based ones, with various structures such as nanoparticles, nanofibers, nanorods, nanoshells, and nanostars, all have demonstrated a wide range of medical biophysical and chemical properties [...].

Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review.

BACKGROUND: Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility. OBJECTIVE: This review article aims to report on the recent advances and current trends in chatbot technology in medicine. A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation. METHODS: A search of the literature published in the past 20 years was conducted using the IEEE Xplore, PubMed, Web of Science, Scopus, and OVID databases. The screening of chatbots was guided by the open-access Botlist directory for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion. RESULTS: Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes. Other applications in pandemic support, global health, and education are yet to be fully explored. CONCLUSIONS: Further research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine.

Gold Nanoparticle DNA Damage by Photon Beam in a Magnetic Field: A Monte Carlo Study.

Ever since the emergence of magnetic resonance (MR)-guided radiotherapy, it is important to investigate the impact of the magnetic field on the dose enhancement in deoxyribonucleic acid (DNA), when gold nanoparticles are used as radiosensitizers during radiotherapy. Gold nanoparticle-enhanced radiotherapy is known to enhance the dose deposition in the DNA, resulting in a double-strand break. In this study, the effects of the magnetic field on the dose enhancement factor (DER) for varying gold nanoparticle sizes, photon beam energies and magnetic field strengths and orientations were investigated using Geant4-DNA Monte Carlo simulations. Using a Monte Carlo model including a single gold nanoparticle with a photon beam source and DNA molecule on the left and right, it is demonstrated that as the gold nanoparticle size increased, the DER increased. However, as the photon beam energy decreased, an increase in the DER was detected. When a magnetic field was added to the simulation model, the DER was found to increase by 2.5-5% as different field strengths (0-2 T) and orientations (x-, y- and z-axis) were used for a 100 nm gold nanoparticle using a 50 keV photon beam. The DNA damage reflected by the DER increased slightly with the presence of the magnetic field. However, variations in the magnetic field strength and orientation did not change the DER significantly.

Application of Nanomaterials in Biomedical Imaging and Cancer Therapy.

Nanomaterials, such as nanoparticles, nanorods, nanosphere, nanoshells, and nanostars, are very commonly used in biomedical imaging and cancer therapy. They make excellent drug carriers, imaging contrast agents, photothermal agents, photoacoustic agents, and radiation dose enhancers, among other applications. Recent advances in nanotechnology have led to the use of nanomaterials in many areas of functional imaging, cancer therapy, and synergistic combinational platforms. This review will systematically explore various applications of nanomaterials in biomedical imaging and cancer therapy. The medical imaging modalities include magnetic resonance imaging, computed tomography, positron emission tomography, single photon emission computerized tomography, optical imaging, ultrasound, and photoacoustic imaging. Various cancer therapeutic methods will also be included, including photothermal therapy, photodynamic therapy, chemotherapy, and immunotherapy. This review also covers theranostics, which use the same agent in diagnosis and therapy. This includes recent advances in multimodality imaging, image-guided therapy, and combination therapy. We found that the continuous advances of synthesis and design of novel nanomaterials will enhance the future development of medical imaging and cancer therapy. However, more resources should be available to examine side effects and cell toxicity when using nanomaterials in humans.

Artificial intelligence in radiotherapy.

Artificial intelligence (AI) has already been implemented widely in the medical field in the recent years. This paper first reviews the background of AI and radiotherapy. Then it explores the basic concepts of different AI algorithms and machine learning methods, such as neural networks, that are available to us today and how they are being implemented in radiotherapy and diagnostic processes, such as medical imaging, treatment planning, patient simulation, quality assurance and radiation dose delivery. It also explores the ongoing research on AI methods that are to be implemented in radiotherapy in the future. The review shows very promising progress and future for AI to be widely used in various areas of radiotherapy. However, basing on various concerns such as availability and security of using big data, and further work on polishing and testing AI algorithms, it is found that we may not ready to use AI primarily in radiotherapy at the moment.

Dose Enhancement for the Flattening-Filter-Free and Flattening-Filter Photon Beams in Nanoparticle-Enhanced Radiotherapy: A Monte Carlo Phantom Study.

Monte Carlo simulations were used to predict the dose enhancement ratio (DER) using the flattening-filter-free (FFF) and flattening-filter (FF) photon beams in prostate nanoparticle-enhanced radiotherapy, with multiple variables such as nanoparticle material, nanoparticle concentration, prostate size, pelvic size, and photon beam energy. A phantom mimicking the patient's pelvis with various prostate and pelvic sizes was used. Macroscopic Monte Carlo simulation using the EGSnrc code was used to predict the dose at the prostate or target using the 6 MV FFF, 6 MV FF, 10 MV FFF, and 10 MV FF photon beams produced by a Varian TrueBeam linear accelerator (Varian Medical System, Palo Alto, CA, USA). Nanoparticle materials of gold, platinum, iodine, silver, and iron oxide with concentration varying in the range of 3-40 mg/ml were used in simulations. Moreover, the prostate and pelvic size were varied from 2.5 to 5.5 cm and 20 to 30 cm, respectively. The DER was defined as the ratio of the target dose with nanoparticle addition to the target dose without nanoparticle addition in the simulation. From the Monte Carlo results of DER, the best nanoparticle material with the highest DER was gold, based on all the nanoparticle concentrations and photon beams. Smaller prostate size, smaller pelvic size, and a higher nanoparticle concentration showed better DER results. When comparing energies, the 6 MV beams always had the greater enhancement ratio. In addition, the FFF photon beams always had a better DER when compared to the FF beams. It is concluded that gold nanoparticles were the most effective material in nanoparticle-enhanced radiotherapy. Moreover, lower photon beam energy (6 MV), FFF photon beam, higher nanoparticle concentration, smaller pelvic size, and smaller prostate size would all increase the DER in prostate nanoparticle-enhanced radiotherapy.