Utilizing collimated aperture with proton pencil beam scanning (PBS) for stereotactic radiotherapy.

PURPOSE: Proton stereotactic radiosurgery (PSRS) has emerged as an innovative proton therapy modality aimed at achieving precise dose delivery with minimal impact on healthy tissues. This study explores the dosimetric outcomes of PSRS in comparison to traditional intensity-modulated proton therapy (IMPT) by focusing on cases with small target volumes. A custom-made aperture system designed for proton therapy, specifically tailored to small target volumes, was developed and implemented for this investigation. METHODS: A prerequisite mechanical validation through an isocentricity test precedes dosimetric assessments, ensuring the seamless integration of mechanical and dosimetry analyses. Five patients were enrolled in the study, including two with choroid melanoma and three with arteriovenous malformations (AVM). Two treatment plans were meticulously executed for each patient, one utilizing a collimated aperture and the other without. Both plans were subjected to robust optimization, maintaining identical beam arrangements and consistent optimization parameters to account for setup errors of 2 mm and range uncertainties of 3.5%. Plan evaluation metrics encompassing the Heterogeneity Index (HI), Paddick Conformity Index (CIPaddick), Gradient Index (GI), and the R50% index to evaluate alterations in low-dose volume distribution. RESULTS: The comparative analysis between PSRS and traditional PBS treatment revealed no significant differences in plan outcomes, with both modalities demonstrating comparable target coverage. However, collimated apertures resulted in discernible improvements in dose conformity, dose fall-off, and reduced low-dose volume. CONCLUSIONS: This study underscores the advantageous impact of the aperture system on proton therapy, particularly in cases involving small target volumes.

Using meta-analysis and CNN-NLP to review and classify the medical literature for normal tissue complication probability in head and neck cancer.

PURPOSE: The study aims to enhance the efficiency and accuracy of literature reviews on normal tissue complication probability (NTCP) in head and neck cancer patients using radiation therapy. It employs meta-analysis (MA) and natural language processing (NLP). MATERIAL AND METHODS: The study consists of two parts. First, it employs MA to assess NTCP models for xerostomia, dysphagia, and mucositis after radiation therapy, using Python 3.10.5 for statistical analysis. Second, it integrates NLP with convolutional neural networks (CNN) to optimize literature search, reducing 3256 articles to 12. CNN settings include a batch size of 50, 50-200 epoch range and a 0.001 learning rate. RESULTS: The study's CNN-NLP model achieved a notable accuracy of 0.94 after 200 epochs with Adamax optimization. MA showed an AUC of 0.67 for early-effect xerostomia and 0.74 for late-effect, indicating moderate to high predictive accuracy but with high variability across studies. Initial CNN accuracy of 66.70% improved to 94.87% post-tuning by optimizer and hyperparameters. CONCLUSION: The study successfully merges MA and NLP, confirming high predictive accuracy for specific model-feature combinations. It introduces a time-based metric, words per minute (WPM), for efficiency and highlights the utility of MA and NLP in clinical research.

An investigation into the impact of volumetric rescanning and fractionation treatment on dose homogeneity in liver cancer proton therapy.

The Pencil Beam Scanning (PBS) technique in modern particle therapy offers a highly conformal dose distribution but poses challenges due to the interplay effect, an interaction between respiration-induced organ movement and PBS. This study evaluates the effectiveness of different volumetric rescanning strategies in mitigating this effect in liver cancer proton therapy. We used a Geant4-based Monte Carlo simulation toolkit, 'TOPAS,' and an image registration toolbox, 'Elastix,' to calculate 4D dose distributions from 5 patients' four-dimensional computed tomography (4DCT). We analyzed the homogeneity index (HI) value of the Clinical Tumor Volume (CTV) at different rescan numbers and treatment times. Our results indicate that dose homogeneity stabilizes at a low point after a week of treatment, implying that both rescanning and fractionation treatments help mitigate the interplay effect. Notably, an increase in the number of rescans doesn't significantly reduce the mean dose to normal tissue but effectively prevents high localized doses to tissue adjacent to the CTV. Rescanning techniques, based on statistical averaging, require no extra equipment or patient cooperation, making them widely accessible. However, the number of rescans, tumor location, diaphragm movement, and treatment fractionation significantly influence their effectiveness. Therefore, deciding the number of rescans should involve considering the number of beams, treatment fraction size, and total delivery time to avoid unnecessary treatment extension without significant clinical benefits. The results showed that 2-3 rescans are more clinically suitable for liver cancer patients undergoing proton therapy.

Using machine learning algorithm to analyse the hypothyroidism complications caused by radiotherapy in patients with head and neck cancer.

Machine learning algorithms were used to analyze the odds and predictors of complications of thyroid damage after radiation therapy in patients with head and neck cancer. This study used decision tree (DT), random forest (RF), and support vector machine (SVM) algorithms to evaluate predictors for the data of 137 head and neck cancer patients. Candidate factors included gender, age, thyroid volume, minimum dose, average dose, maximum dose, number of treatments, and relative volume of the organ receiving X dose (X: 10, 20, 30, 40, 50, 60 Gy). The algorithm was optimized according to these factors and tenfold cross-validation to analyze the state of thyroid damage and select the predictors of thyroid dysfunction. The importance of the predictors identified by the three machine learning algorithms was ranked: the top five predictors were age, thyroid volume, average dose, V50 and V60. Of these, age and volume were negatively correlated with thyroid damage, indicating that the greater the age and thyroid volume, the lower the risk of thyroid damage; the average dose, V50 and V60 were positively correlated with thyroid damage, indicating that the larger the average dose, V50 and V60, the higher the risk of thyroid damage. The RF algorithm was most accurate in predicting the probability of thyroid damage among the three algorithms optimized using the above factors. The Area under the receiver operating characteristic curve (AUC) was 0.827 and the accuracy (ACC) was 0.824. This study found that five predictors (age, thyroid volume, mean dose, V50 and V60) are important factors affecting the chance that patients with head and neck cancer who received radiation therapy will develop hypothyroidism. Using these factors as the prediction basis of the algorithm and using RF to predict the occurrence of hypothyroidism had the highest ACC, which was 82.4%. This algorithm is quite helpful in predicting the probability of radiotherapy complications. It also provides references for assisting medical decision-making in the future.

Application of artificial intelligence in endoscopic image analysis for the diagnosis of a gastric cancer pathogen-Helicobacter pylori infection.

Helicobacter pylori (H. pylori) infection is the principal cause of chronic gastritis, gastric ulcers, duodenal ulcers, and gastric cancer. In clinical practice, diagnosis of H. pylori infection by a gastroenterologists' impression of endoscopic images is inaccurate and cannot be used for the management of gastrointestinal diseases. The aim of this study was to develop an artificial intelligence classification system for the diagnosis of H. pylori infection by pre-processing endoscopic images and machine learning methods. Endoscopic images of the gastric body and antrum from 302 patients receiving endoscopy with confirmation of H. pylori status by a rapid urease test at An Nan Hospital were obtained for the derivation and validation of an artificial intelligence classification system. The H. pylori status was interpreted as positive or negative by Convolutional Neural Network (CNN) and Concurrent Spatial and Channel Squeeze and Excitation (scSE) network, combined with different classification models for deep learning of gastric images. The comprehensive assessment for H. pylori status by scSE-CatBoost classification models for both body and antrum images from same patients achieved an accuracy of 0.90, sensitivity of 1.00, specificity of 0.81, positive predictive value of 0.82, negative predicted value of 1.00, and area under the curve of 0.88. The data suggest that an artificial intelligence classification model using scSE-CatBoost deep learning for gastric endoscopic images can distinguish H. pylori status with good performance and is useful for the survey or diagnosis of H. pylori infection in clinical practice.

The evaluation of an on-site monitoring program for activity meter quality assurance with exemption-level sources.

This paper discusses the feasibility of a monitoring program for the quality assurance status of activity meters. We sent a questionnaire to clinical nuclear medicine departments of medical institutions, requesting information on their activity meters and quality assurance practices. On-site visits were conducted with exemption-level standard sources (Co-57, Cs-137 and Ba-133) for dose calibrators in nuclear medicine departments including physical inspection, accuracy and reproducibility. A method offering a quick check on the detection efficiency of the space dimension inside the activity meters was also introduced. For dose calibrator quality assurance, the daily checks had the highest implementation. However, annual checks and upon acceptance/after a repair check were reduced to 50% and 44%, respectively. The accuracy results of dose calibrators showed that all models exceeded the ±10% criteria with Co-57 and Cs-137 sources. The reproducibility results showed that some models exceeded the ±5% criteria with Co-57 and Cs-137 sources. The appropriate application of exemption-level standard sources considering the uncertainty that affects the measurement is discussed.

Results of the performance test for quality assessment of personal radiation dosimetry services including the influence of the dosimeter readout frequency.

This study was to determine the significance of factors considered for the measurement accuracy of personal dosimeter in dosimetry services such as dosimetry service, irradiation category, years of use and readout frequency. The investigation included management information questionnaire, on-site visit and blind test. The blind test with random selected personal badge was used in inter-comparison of eight dosimetry services, and the test results followed ANSI/HPS N13.11 criteria. This study also analyzed the measurement deviations if they felt in the criteria of ICRP 75 or not. One-way ANOVA tests were used to analyze the significant difference of the measurement deviations in different dosimetry services, irradiation categories, and years of use. Simple linear-regression test was performed for the significance of the prediction model between measurement deviations and readout frequencies. All visited dosimetry services followed the proper statue of basic management and passed the performance check of the tolerance level. The average deviations corresponding to category I, category II deep dose, and category II shallow dose were 6.08%, 9.49%, and 10.41% respectively. There had significant differences of measurement deviation in different dosimetry services (p < 0.0001) and irradiation categories (p = 0.016) but no significant difference in years of use (p = 0.498). There was no significance in the linear-regression model between measurement deviation and badge readout frequencies. Based on the regular calibration of the personal dosimeter, the deviation of the measured value is mainly affected by different dosimetry services and irradiation categories; and there shows no significant influence by years of use and readout frequency.

The Analysis with Quantitative Indexes for Public's Awareness of Radiation Knowledge in Taiwan.

(1) Background: The purpose of this study was to evaluate the radiation awareness level of the public in Taiwan. (2) Methods: This study designed an online survey form to investigate the radiation awareness level with six topics: basic knowledge of radiation, environmental radiation, medical radiation, radiation protection, and university/corporate social responsibility. The score of respondents were converted into knowledge and responsibility indexes for the quantitative evaluation. Logistic regression was used to assess the correlation between the knowledge index and individual factors. Paired t-test was used to assess the significant difference in knowledge index between pre-training and post-training. (3) Results: The knowledge index of each job category reflected the proportion of radiation awareness of the job. The logistic regression result indicated that radiation-related people could get higher knowledge index. The paired t-test indicated that the knowledge index before and after class had significant differences in all question topics. (4) Conclusions: The public's awareness of medical radiation was the topic that needed to be strengthened the most-the responses with high knowledge index significantly correlated with their experience in radiation education training or radiation-related jobs. It significantly increased the knowledge index of radiation if the public received radiation education training.

Medical Mask Wearing During Treatment for Patients Undergoing Radiotherapy in COVID-19 Pandemic - An Experience of Protocol Setup and Dosimetric Evaluation for Particle/Proton Beam Therapy.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has caused extreme challenges for the healthcare system. Medical masks have been proven to effectively block disease transmission. Radiotherapeutic departments are at unique risk for disease exposure with the repeated daily treatment schedule. A protocol of mask wearing during daily treatment was established, and the effect of wearing medical masks on dosimetry during proton beam therapy (PBT) was validated. METHODS: A department protocol of medical mask wearing was initiated after the COVID-19 pandemic. Medical masks that were made under standardized specification and regulation were obtained for analyses. The physical and dosimetric characteristics of these medical masks were measured by different proton energies using commercialized measurement tools. RESULTS: Patients and staff were able to adopt the protocol on a weekly basis, and no adverse events were reported. The average physical thickness of a single piece of medical mask was 0.5 mm with a water equivalent thickness (WET) of 0.1 mm. CONCLUSION: Our study revealed that mask wearing for patients undergoing daily radiotherapy is feasible and can provide basic protection for patients and staff. The impact of mask wearing on dosimetry was only 0.1 mm in WET, which has no impact on clinical PBT treatment. A medical mask-wearing policy can be applied safely without dosimetric concerns and should be considered as a standard practice for PBT centers during the COVID-19 pandemic.

The feasibility study of using multiple partial volumetric-modulated arcs therapy in early stage left-sided breast cancer patients.

The purpose of this study was to assess the feasibility of using a multiple partial volumetric-modulated arcs therapy (MP-VMAT) technique on the left breast irradiation and to evaluate the dosimetry and treatment efficiency. Ten patients with left-sided breast cancer who had been treated by whole breast irradiation were selected for the treatment plan evaluation by using six partial volumetric modulated arcs. Each arc consisted of a 50° gantry rotation. The planning target volumes and the normal organs, including the right breast, the bilateral lungs, left ventricle, heart, and unspecified tissue, were contoured on the CT images. Dose-volume histograms were generated and the delivery time for each arc was recorded. The PTV received greater than 95% of the V(95) for all cases, and the maximum dose was within ± 1% of 110% of the prescription dose. The mean homogeneity index (HI) was 10.61 ± 0.99, and mean conformity index (CI) was 1.21 ± 0.03. The mean dose, V(5), V(10), V(25), and V(30) of the heart were 7.61 ± 1.38 Gy, 59.73% ± 15.87%, 24.39%± 6.82%, 2.52%± 1.11%, and 1.57% ± 0.71%, respectively. The volume of the left ventricle receiving 25 Gy was 5.15% ± 2.23%. The total lung mean dose was 5.57 ± 0.36 Gy, with V(5) of 25.39% ± 3.88% and V(20) of 5.66% ± 0.89%. The right breast received a mean dose of 2.13 ± 0.22 Gy, with V(5) of 1.83% ± 1.22% and V(10) of 0.04% ± 0.12%. The mean dose of unspecified tissue was 5.34 ± 0.37 Gy and V(5) was 22.23% ± 1.57%. The volume of the unspecified tissue receiving 50 Gy was 0.50% ± 0.14%. The mean delivery time for each arc was 13.9 seconds. The average MU among ten patients was 511 MU (range 443 to 594 MUs). The MP-VMAT technique for the left-sided breast cancer patients achieved adequate target dose coverage while maintaining low doses to organs-at-risk, and therefore reduced the potential for induction of second malignancy and side effects. The highly efficient treatment delivery would be beneficial for improving patient throughput, providing patient comfort, and achieving precise treatment with the breathing control system.