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.

Computer-assisted and robotic implant surgery: Assessing the outcome measures of accuracy and educational implications.

OBJECTIVE: This scoping review aimed to (1) critically evaluate the outcomes measures used to assess the accuracy of implant placement with Computer Assisted Implant Surgery (CAIS) and (2) review the evidence supporting the efficient implementation of CAIS in training and education of clinicians. METHODS: A scoping literature review was conducted aiming to identify (a) clinical trials assessing accuracy of implant placement with CAIS, and (b) clinical trials or simulation/cadaver studies where CAIS was utilised and assessed for the training/education of clinicians. Studies since 1995 were assessed for suitability and data related to the outcomes measures of accuracy and educational efficacy were extracted and synthesised. RESULTS: Accuracy of CAIS has been mainly assessed through surrogate measures. Individual clinical trials have not shown any difference between static and dynamic CAIS, but recent meta-analyses suggest an advantage of dynamic CAIS in reducing angular deviation. The combination of static and dynamic CAIS might offer higher accuracy than each of the two used alone. Dynamic CAIS is suitable for novice surgeons and might even have added value as an education tool for implant surgery, although mastering the technique requires longer training than static. CONCLUSION: Meta-analyses of large samples, new and diverse outcomes measures, as well as benchmarking of levels of accuracy with specific clinical outcomes will help to better understand the potential and limitations of CAIS. Dynamic CAIS is suitable for novice operators, but educational interventions distributed over longer periods of time will be required for mastery of the process.

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.

Explainable machine learning via intra-tumoral radiomics feature mapping for patient stratification in adjuvant chemotherapy for locoregionally advanced nasopharyngeal carcinoma.

PURPOSE: This study aimed to discover intra-tumor heterogeneity signature and validate its predictive value for adjuvant chemotherapy (ACT) following concurrent chemoradiotherapy (CCRT) in locoregionally advanced nasopharyngeal carcinoma (LA-NPC). MATERIALS AND METHODS: 397 LA-NPC patients were retrospectively enrolled. Pre-treatment contrast-enhanced T1-weighted (CET1-w) MR images, clinical variables, and follow-up were retrospectively collected. We identified single predictive radiomic feature from primary gross tumor volume (GTVnp) and defined predicted subvolume by calculating voxel-wised feature mapping and within GTVnp. We independently validate predictive value of identified feature and associated predicted subvolume. RESULTS: Only one radiomic feature, gldm_DependenceVariance in 3 mm-sigma LoG-filtered image, was discovered as a signature. In the high-risk group determined by the signature, patients received CCRT + ACT achieved 3-year disease free survival (DFS) rate of 90% versus 57% (HR, 0.20; 95%CI, 0.05-0.94; P = 0.007) for CCRT alone. The multivariate analysis showed patients receiving CCRT + ACT had a HR of 0.21 (95%CI: 0.06-0.68, P = 0.009) for DFS compared to those receiving CCRT alone. The predictive value can also be generalized to the subvolume with multivariate HR of 0.27 (P = 0.017) for DFS. CONCLUSION: The signature with its heterogeneity mapping could be a reliable and explainable ACT decision-making tool in clinical practice.

Component placement accuracy in two generations of handheld robotics-assisted knee arthroplasty.

INTRODUCTION: Total knee arthroplasty (TKA) is the gold standard for treatment of end-stage osteoarthritis. Previous studies have shown that successful outcomes following TKA depend on accurate implant alignment and soft tissue balancing. Robotic-assisted TKA have demonstrated improved accuracy in component placement and have been associated with better outcomes and patient satisfaction. This study aims to report on the execution accuracy of two generations of handheld robotic-assisted surgical systems. METHODS: This was a retrospective analysis of TKA procedures with two sequential generations of the same handheld robotic-assisted surgical system. Intra-operative data captured included pre-operative limb deformity, limb axes, range of motion, kinematic balance, and the resulting plan for component placement in three-dimensional space. Patients were stratified based on their preoperative coronal lower limb mechanical alignment (> 3° varus, < 3° varus, < 3° valgus, and ≥ 3° valgus). Measurements of component placement (overall lower limb alignment, medial and lateral flexion gaps, and tibial and femoral resection depths) were assessed using descriptive statistics. RESULTS: A total of 435 patients were included and stratified based on preoperative lower limb alignment: 229 with ≥ 3° varus, 78 with varus < 3° and 58 with valgus < 3°, and 70 with valgus > 3°. The mean difference between planned and achieved alignment in the lower limb valgus patients was < 1° across all groups. Mean differences between planned and achieved medial flexion gap was higher in the > 3° subgroup in the varus patient cohort ([< 3°]: 1.15 ± 1.92 vs. [> 3°]: 1.90 ± 2.57); this value was higher in the < 3° subgroup for valgus patients ([< 3°]: 1.34 ± 1.83 vs. [> 3°]: 0.956 ± 1.65). Average resection depth ranged from 9.46 to 10.4 mm in the posterior medial femur, 9.25 to 9.95 mm in the posterior lateral femur, 7.45 to 8.79 mm in the distal medial femur, 8.22 to 9.18 mm in the distal lateral femur, 6.70 to 7.07 mm in the medial tibial condyle and 6.40 to 7.19 mm in the lateral tibial condyle. Non-inferiority testing demonstrated the newer generation is non-inferior to the older generation. CONCLUSION: Robotic-assisted knee replacement using handheld image-free systems is able to maintain accuracy of component placement. Further investigation of patient reported outcomes as well as long-term implant longevity are needed.

Treating an edentulous mandible with an implant-supported prosthesis with a shape-memory alloy abutment system.

This clinical report describes a treatment protocol for completely edentulous patients using digital implant planning for an all-on-4 treatment of both the maxilla and mandible as well as the use of a shape-memory alloy retention system to secure a complete-arch restoration to the mandible.

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.

Dosimetric verification of lung phantom calculated by collapsed cone convolution: A Monte Carlo and experimental evaluation.

OBJECTIVE: To evaluate the dose calculation accuracy in the Prowess Panther treatment planning system (TPS) using the collapsed cone convolution (CCC) algorithm. METHODS: The BEAMnrc Monte Carlo (MC) package was used to predict the dose distribution of photon beams produced by the Oncor® linear accelerator (linac). The MC model of an 18 MV photon beam was verified by measurement using a p-type diode dosimeter. Percent depth dose (PDD) and dose profiles were used for comparison based on three field sizes: 5×5, 10×10, and 20×20cm2. The accuracy of the CCC dosimetry was also evaluated using a plan composed of a simple parallel-opposed field (11×16cm2) in a lung phantom comprised of four tissue simulating media namely, lung, soft tissue, bone and spinal cord. The CCC dose calculation accuracy was evaluated by MC simulation and measurements according to the dose difference and 3D gamma analysis. Gamma analysis was carried out through comparison of the Monte Carlo simulation and the TPS calculated dose. RESULTS: Compared to the dosimetric results measured by the Farmer chamber, the CCC algorithm underestimated dose in the planning target volume (PTV), right lung and lung-tissue interface regions by about -0.11%, -1.6 %, and -2.9%, respectively. Moreover, the CCC algorithm underestimated the dose at the PTV, right lung and lung-tissue interface regions in the order of -0.34%, -0.4% and -3.5%, respectively, when compared to the MC simulation. Gamma analysis results showed that the passing rates within the PTV and heterogeneous region were above 59% and 76%. For the right lung and spinal cord, the passing rates were above 80% for all gamma criteria. CONCLUSIONS: This study demonstrates that the CCC algorithm has potential to calculate dose with sufficient accuracy for 3D conformal radiotherapy within the thorax where a significant amount of tissue heterogeneity exists.

Benchmarking of Monte Carlo model of Siemens Oncor® linear accelerator for 18MV photon beam: Determination of initial electron beam parameters.

OBJECTIVE: This study aims to benchmark a Monte Carlo (MC) model of the 18 MV photon beam produced by the Siemens Oncor® linac using the BEAMnrc and DOSXYZnrc codes. METHODS: By matching the percentage depth doses and beam profiles calculated by MC simulations with measurements, the initial electron beam parameters including electron energy, full width at half maximum (spatial FWHM), and mean angular spread were derived for the 10×10 cm2 and 20×20 cm2 field sizes. The MC model of the 18 MV photon beam was then validated against the measurements for different field sizes (5×5, 30×30 and 40×40 cm2) by gamma index analysis. RESULTS: The optimum values for electron energy, spatial FWHM and mean angular spread were 14.2 MeV, 0.08 cm and 0.8 degree, respectively. The MC simulations yielded the comparable measurement results of these optimum parameters. The gamma passing rates (with acceptance criteria of 1% /1 mm) for percentage depth doses were found to be 100% for all field sizes. For cross-line profiles, the gamma passing rates were 100%, 97%, 95%, 96% and 95% for 5×5, 10×10, 20×20, 30×30 and 40×40 cm2 field sizes, respectively. CONCLUSIONS: By validation of the MC model of Siemens Oncor® linac using various field sizes, it was found that both dose profiles of small and large field sizes were very sensitive to the changes in spatial FWHM and mean angular spread of the primary electron beam from the bending magnet. Hence, it is recommended that both small and large field sizes of the 18 MV photon beams should be considered in the Monte Carlo linac modeling.

Measuring the impact of simulation practice on the spatial representation ability of dentists by means of Impacted Mandibular Third Molar (IMTM) Surgery on 3D printed models.

INTRODUCTION: The ability of spatial representation is one of the most important competences of an oral surgeon, but also a challenging competence to assess in educational settings. The aim of this pilot study was to investigate the impact of a hands-on simulation practice on the development of the Dental Anatomy Spatial Representation Ability (DASRA) of recent dental graduates, using a novel assessment instrument based on CBCT radiographic data. MATERIALS AND METHODS: A novel instrument for the measurement of DASRA for oral surgery was developed, utilising panoramic, sagittal and horizontal slices from 3D radiographic imaging of impacted wisdom teeth. Twenty-one young dentists (nine males and 12 females) were enrolled in this study. After measurement of dentists' perceptual ability (PA), all dentists completed a DASRA drawing test and thereafter practiced surgical extraction on 3D printed models, which were based on actual patients' anatomy. A second DASRA was administered after the hands-on exercise, followed by a feedback survey. RESULTS: A slight increase in DASRA scores was revealed after practicing on models, which however did not reach statistical significance. However, statistically significant decrease in drawing time was observed after hands-on experiences (P < 0.001), while important qualitative improvements were noted, such as dentists' ability to position the mandibular nerve. Dentists perceived 3D printed model as a very effective and efficient manner in planning and practicing of surgical interventions. CONCLUSIONS: An instrument for the measurement of DASRA based on 3D radiographic imaging might support educators in the assessment of spatial skills of novice surgeons. Practice on 3D printed, anatomically precise models can benefit dentists on pre-clinical surgical training and has the potential of improving their dental anatomy spatial representation ability.

Dosimetric and Monte Carlo verification of jaws-only IMRT plans calculated by the Collapsed Cone Convolution algorithm for head and neck cancers.

AIM: The aim of this study is to verify the Prowess Panther jaws-only intensity modulated radiation therapy (JO-IMRT) treatment planning (TP) by comparing the TP dose distributions for head-and-neck (H&N) cancer with the ones simulated by Monte Carlo (MC). BACKGROUND: To date, dose distributions planned using JO-IMRT for H&N patients were found superior to the corresponding three-dimensional conformal radiotherapy (3D-CRT) plans. Dosimetry of the JO-IMRT plans were also experimentally verified using an ionization chamber, MapCHECK 2, and Octavius 4D and good agreements were shown. MATERIALS AND METHODS: Dose distributions of 15 JO-IMRT plans of nasopharyngeal patients were recalculated using the EGSnrc Monte Carlo code. The clinical photon beams were simulated using the BEAMnrc. The absorbed dose to patients treated by fixed-field IMRT was computed using the DOSXYZnrc. The simulated dose distributions were then compared with the ones calculated by the Collapsed Cone Convolution (CCC) algorithm on the TPS, using the relative dose error comparison and the gamma index using global methods implemented in PTW-VeriSoft with 3%/3 mm, 2%/2 mm, 1%/1 mm criteria. RESULTS: There is a good agreement between the MC and TPS dose. The average gamma passing rates were 93.3 ± 3.1%, 92.8 ± 3.2%, 92.4 ± 3.4% based on the 3%/3 mm, 2%/2 mm, 1%/1 mm criteria, respectively. CONCLUSIONS: According to the results, it is concluded that the CCC algorithm was adequate for most of the IMRT H&N cases where the target was not immediately adjacent to the critical structures.

Driving After Microinvasive Total Hip Arthroplasty.

BACKGROUND: Patients undergoing total hip arthroplasty (THA) are often advised to avoid driving for 6 weeks postoperation. This is based on patients having to maintain postoperative hip precautions and studies investigating brake reaction time (BRT) following THA using conventional techniques. The aim of this study was to assess patients' ability to drive in the early postoperative period following microinvasive THA by assessing BRT. METHODS: Hundred consecutive patients undergoing SuperPATH® THA in 2015 who drove automobiles preoperatively were included in this prospective cohort study. BRT was measured preoperatively and at day 1 or 2 postoperation using a driving simulator. A subset of 25 consecutive patients had repeat BRT testing at 2 weeks postoperation. Five BRT measures were taken at each time point. Differences in the patient's mean and best BRT at each time point were assessed using the paired t-test. RESULTS: The study cohort included 50 men and 50 women with mean age 63 years (range 25-86). The mean preoperative BRT was 0.63 s (range 0.43-1.44), with a mean difference of -0.1 s (range -0.57 to 0.33, P < .0001) at day 1 or 2 postoperation. The 2-week mean and best BRTs were also better than paired preoperative readings with a mean improvement of 0.15 s (range -0.78 to -0.004, P < .0001). CONCLUSION: BRT reaches preoperative values by day 2 following microinvasive THA. Patients may be suitable to drive earlier than the previously recommended 6 weeks postoperation.

In-hospital costs for total hip replacement performed using the supercapsular percutaneously-assisted total hip replacement surgical technique.

PURPOSE: The supercapsular percutaneously-assisted total hip (SuperPath) surgical technique for total hip replacement (THR) is a tissue-sparing approach that has been shown to improve key variables associated with the economic burden of THR (e.g., length of stay, readmissions). To date, no studies have examined the economic impact of using this technique in the United States. The objective of this study was to compare the in-hospital costs of this technique to all other THRs performed in a large hospital system in the United States. METHODS: The costing database for a large hospital system was retrospectively searched for all in-hospital costs associated with primary THRs performed between January 2013 and September 2015. Data for all SuperPath THRs (group A) were compared to that of all other THRs performed at centres within the hospital system (group B). RESULTS: Use of the SuperPath technique resulted in significant overall in-hospital cost reductions of 15.0 % (p < 0.000), including reductions in operating room costs of 17.3 % (p < 0.000), physical/occupational therapy costs of 26.8 % (p = 0.005), and pharmacy costs of 25.3 % (p < 0.000). Length of stay (1.2 vs. 2.6 days), transfusion rates (1.9 vs. 15.8 %), and 30-day readmission rates (0.4 vs. 2.9 %) were also lower in group A. CONCLUSIONS: The use of this tissue-sparing surgical technique resulted in reductions in in-hospital costs, length of stay, and readmissions when compared to all other THRs performed in a large hospital system in the United States.

Internet-based computer technology on radiotherapy.

Recent rapid development of Internet-based computer technologies has made possible many novel applications in radiation dose delivery. However, translational speed of applying these new technologies in radiotherapy could hardly catch up due to the complex commissioning process and quality assurance protocol. Implementing novel Internet-based technology in radiotherapy requires corresponding design of algorithm and infrastructure of the application, set up of related clinical policies, purchase and development of software and hardware, computer programming and debugging, and national to international collaboration. Although such implementation processes are time consuming, some recent computer advancements in the radiation dose delivery are still noticeable. In this review, we will present the background and concept of some recent Internet-based computer technologies such as cloud computing, big data processing and machine learning, followed by their potential applications in radiotherapy, such as treatment planning and dose delivery. We will also discuss the current progress of these applications and their impacts on radiotherapy. We will explore and evaluate the expected benefits and challenges in implementation as well.

A surface energy spectral study on the bone heterogeneity and beam obliquity using the flattened and unflattened photon beams.

AIM: Using flattened and unflattened photon beams, this study investigated the spectral variations of surface photon energy and energy fluence in the bone heterogeneity and beam obliquity. BACKGROUND: Surface dose enhancement is a dosimetric concern when using unflattened photon beam in radiotherapy. It is because the unflattened photon beam contains more low-energy photons which are removed by the flattening filter of the flattened photon beam. MATERIALS AND METHODS: We used a water and bone heterogeneity phantom to study the distributions of energy, energy fluence and mean energy of the 6 MV flattened and unflattened photon beams (field size = 10 cm × 10 cm) produced by a Varian TrueBEAM linear accelerator. These elements were calculated at the phantom surfaces using Monte Carlo simulations. The photon energy and energy fluence calculations were repeated with the beam angle turned from 0° to 15°, 30° and 45° in the water and bone phantom. RESULTS: Spectral results at the phantom surfaces showed that the unflattened photon beams contained more photons concentrated mainly in the low-energy range (0-2 MeV) than the flattened beams associated with a flattening filter. With a bone layer of 1 cm under the phantom surface and within the build-up region of the 6 MV photon beam, it is found that both the flattened and unflattened beams had slightly less photons in the energy range <0.4 MeV compared to the water phantom. This shows that the presence of the bone decreased the low-energy photon backscatters to the phantom surface. When both the flattened and unflattened photon beams were rotated from 0° to 45°, the number of photon and mean photon energy increased. This indicates that both photon beams became more hardened or penetrate when the beam angle increased. In the presence of bone, the mean energies of both photon beams increased. This is due to the absorption of low-energy photons by the bone, resulting in more beam hardening. CONCLUSIONS: This study explores the spectral relationships of surface photon energy and energy fluence with bone heterogeneity and beam obliquity for the flattened and unflattened photon beams. The photon spectral information is important in studies on the patient's surface dose enhancement using unflattened photon beams in radiotherapy.

Thirty-day readmission rate and discharge status following total hip arthroplasty using the supercapsular percutaneously-assisted total hip surgical technique.

PURPOSE: Recent studies have reported nearly 40% of costs associated with a 30-day episode-of-care for total joint replacements are due to post-discharge activities and 81% of those are specifically due to unplanned readmissions and discharging patients to post-acute care facilities. The purpose of this study was to determine these two key variables for total hip arthroplasty (THA) patients implanted using a tissue-sparing surgical technique and to see how these values compare to those previously reported in the United States. METHODS: The healthcare databases at three institutions were searched for primary THA patients implanted using the supercapsular percutaneously-assisted total hip (SuperPath) surgical technique between January 2013 and July 2014. Data elements included 30-day all-cause readmission rate, discharge status, transfusion rate, complications, and length of stay (LOS). RESULTS: Data were available for 479 THAs. The 30-day all-cause readmission rate, transfusion rate, and average LOS was 2.3, 3.3%, and 1.6 days, respectively. Over 91% of patients were discharged routinely home, 4.1% to skilled nursing facilities, 3.8% to home health care, and 0.6% to inpatient rehabilitation facilities. Complications included dislocation (0.8%), periprosthetic fracture (0.8%), and deep vein thrombosis (0.2 %). There were no infections reported. CONCLUSIONS: Patients implanted using this tissue-sparing technique experienced reduced 30-day all-cause readmission rates (2.3% vs. 4.2%) and more were routinely discharged home (91.5% vs. 27.3%) than have been previously reported for patients in the United States. Use of this tissue-sparing technique has the potential to significantly reduce post-discharge costs.

Ethical Considerations in Human-Centered AI: Advancing Oncology Chatbots through Large Language Models.

UNSTRUCTURED: The integration of chatbots in oncology underscores the pressing need for human-centered AI that addresses patient and family concerns with empathy and precision. Human-centered AI emphasizes ethical principles, empathy, and user-centric approaches, ensuring technology aligns with human values and needs. This review critically examines the ethical implications of employing Large Language Models (LLMs) like GPT-3 and GPT-4 in oncology chatbots. It examines how these models replicate human-like language patterns, impacting the design of ethical AI systems. The paper identifies key strategies for ethically developing oncology chatbots, focusing on potential biases arising from extensive datasets and neural networks. Specific datasets, such as those sourced from predominantly Western medical literature and patient interactions, may introduce biases by over-representing certain demographic groups. Moreover, the training methodologies of LLMs, including fine-tuning processes, can exacerbate these biases, leading to outputs that may disproportionately favor affluent or Western populations while neglecting marginalized communities. By providing examples of biased outputs in oncology chatbots, the review highlights the ethical challenges LLMs present and the need for mitigation strategies. The study emphasizes integrating human-centric values into AI to mitigate these biases, ultimately advocating for the development of oncology chatbots that are aligned with ethical principles and capable of serving diverse patient populations equitably.

Impact of Scattering Foil Composition on Electron Energy Distribution in a Clinical Linear Accelerator Modified for FLASH Radiotherapy: A Monte Carlo Study.

This study investigates how scattering foil materials and sampling holder placement affect electron energy distribution in electron beams from a modified medical linear accelerator for FLASH radiotherapy. We analyze electron energy spectra at various positions-ionization chamber, mirror, and jaw-to evaluate the impact of Cu, Pb-Cu, Pb, and Ta foils. Our findings show that close proximity to the source intensifies the dependence of electron energy distribution on foil material, enabling precise beam control through material selection. Monte Carlo simulations are effective for designing foils to achieve desired energy distributions. Moving the sampling holder farther from the source reduces foil material influence, promoting more uniform energy spreads, particularly in the 0.5-10 MeV range for 12 MeV electron beams. These insights emphasize the critical role of tailored material selection and sampling holder positioning in optimizing electron energy distribution and fluence intensity for FLASH radiotherapy research, benefiting both experimental design and clinical applications.