Radiological sciences are witnessing remarkable development, especially concerning targeted radiation therapy. In this article, we review a new study that highlights the use of neural networks to improve energy estimates for alpha particle radiation events. A neural network has been developed that can accurately predict the energy spectrum of individual events, opening new horizons in the field of precise dosimetry measurements. By providing data based on Monte Carlo simulations and employing machine learning techniques, this study contributes to enhancing targeted tumor therapy methods and improving patient outcomes. Join us to explore the methods used and the exciting results discussed in this study.
Instructions Regarding Neural Networks in Micro Dosimetry Measurements
Advanced techniques in the field of precise radiation dose measurements have been developed using neural networks. These techniques focus on predicting the specific energy spectrum in individual events, especially for alpha particles used in targeted radiation therapies. This process requires the input of precise parameters related to radiation source specifications, as well as the geometry of the targeted objects. The neural network has been designed to include four main inputs: source-target configuration, particle energy, and the sizes of nuclei and cells.
The data used for training the neural network comes from a variety of simulators calculated using the Monte Carlo technique, as well as previously published data. The data was divided into a training set and a testing set, with errors calculated significantly using the mean squared error. Through this processing, the ability of the network to deliver accurate results related to the specific energy spectrum was verified.
To present the results, the various values of the root mean square error were very stable, indicating the network’s effectiveness in recognizing patterns related to the energy spectrum. The network’s ability to recognize the data positively affected the prediction of results, making it a powerful tool in the field of targeted radiation therapy.
Benefits and Strengths of Alpha Particle Targeted Radiation Therapy
Alpha particles are ideal for targeted radiation therapy due to a combination of distinctive properties. These particles are characterized by high energies, dense ionization paths, and a short range, allowing them to affect targeted cells precisely without significantly impacting healthy tissue. The dense energy transfer is a characteristic that enhances treatment effectiveness, as techniques such as alpha radiation therapy can efficiently eradicate cancer cells.
Additionally, alpha particles operate independently of dose rate and oxygen effects, making them ideal for treatments that require precise targeting. These advantages can be utilized to treat advanced types of cancer, such as hormone-resistant prostate cancer. Clinical trials have demonstrated the efficacy of alpha particle therapy, supporting its increasingly widespread use in clinical applications.
The effectiveness of targeted radiation therapy using alpha particles is closely related to the random nature that characterizes the energy deposition process and the multiplicity of effects in the micro-zones of cells, which emphasizes the need for precise measurement methods such as micro dosimetry measurements.
Challenges Associated with Dosimetry Measurements Using Alpha Particles
Dosimetry measurements using alpha particles are complex due to the random nature of energy deposition in cellular entities. Standard dose measurement requires determining the amount of energy deposited per unit mass, which is highlighted by the non-homogeneous energy deposition. Unexpected dose input due to the nature of alpha particles can lead to local variations of more than 20% in deposited energy, necessitating precise and accurate credits for dose measurements.
The challenges exist not only in dose measurements but also in understanding how the random interactions of alpha particles affect cellular structure and overall interactions. The random nature can lead to some cell nuclei receiving multiple hits from alpha particles while others may receive very few, thus requiring the use of methods such as Monte Carlo to achieve a comprehensive understanding of micro dosimetric dimensions.
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the need for strategies to account for these disparities is imperative, reflecting the importance of continuous research in this field to provide accurate and timely solutions, thereby ensuring the highest level of success in cancer treatments.
Modern Techniques in Micro Dosimetry Using Machine Learning
The use of modern technologies such as machine learning in dose measurements has led to significant advancements in efficiency and effectiveness as a tool for analyzing big data. Neural networks represent a critical tool in this field, as they can handle complex information rapidly and with great accuracy. Despite the complex mechanism of targeted radiation therapy, neural networks have managed to develop a robust model that can accurately predict the specific energy spectrum.
The use of machine learning is not only limited to enhancing the credibility of results but also allows for the exploration of new patterns in the specified radiation study and contributes to the quantitative processing of radiations. Through this approach, a variety of parameters can be introduced and predictions can be tuned more accurately, paving the way for more effective clinical applications.
This innovation enhances the potential for advanced research on particles and their interactions with human tissues, enabling treatment processes characterized by high precision and flexibility, thus facilitating significant progress in the field of targeted radiation therapies.
Types of Radiation Sources in Cells
The discussions on this topic relate to the distribution of radiation sources and their arrangements within cell structures. The study utilized different quantitative models to determine where radioactive activity is distributed, such as within the cell nucleus, on the cell surface, in the cytoplasm, or even outside it. Different types of distributions were considered to achieve a comprehensive understanding of the effects arising from the distribution of radiation sources in cells. This includes uniform distribution everywhere or concentration on specific areas such as the cell nucleus.
For example, in the case of the model of activity distribution outside the cell nucleus, the response of the cells is studied when exposed to certain rays. This allows researchers to understand how these rays affect cells in general, especially with different forms of radiation and radiation energy. Additionally, various sizes for the nucleus and the cell were defined, contributing to improving the accuracy of the models required for radiations.
By using the Monte Carlo (MC) algorithm described in the appendix, the data were better represented by assembling 2264 unique sets of source and target configurations. What distinguishes this study is the diversification of energies and sizes of cells and nuclei, making the results more accurate when simulating the radiative effects on cells.
Neural Network Tool and Its Architecture
In this study, the choice of the neural network model was not arbitrary; rather, it was based on strong mathematical foundations. The MATLAB® Deep Learning Toolbox™ was used to develop the neural network, allowing for precise testing and necessary adjustments to achieve the best results. The Levenberg-Marquardt algorithm was employed, which is considered one of the most efficient for large neural network models.
The study used a network architecture consisting of two hidden layers with 10 to 40 nodes to achieve a balance between complexity and efficiency. The goal of this design was to ensure that the network could learn effectively from the available data and be capable of accurate predictions while maintaining the complexity added to the model. The results showed that using multiple layers with a suitable number of nodes makes the network more effective in processing information.
One of the challenges faced by researchers was data formatting. The spectral data was not uniformly distributed, so there was an urgent need to transform the data using the natural logarithm. This transformation significantly improved performance and ensured a distribution close to normal, positively impacting the network’s accuracy with a lower error rate.
Analysis
Results and Accuracy of the Models
The results of the neural network can be compared to the actual results from Monte Carlo simulations, where this comparison represents an important step in understanding the accuracy of the models. Studies have shown that the expected values align well with the real values, which is evident from the curves that show the agreement of results between the model and the experimental data. The performance of the neural network was excellent, recording R² values above 0.98 for all diagrams, indicating the accuracy of the predictions.
Additionally, the root mean square errors (RMSE) for the expected values were measured, where the figures showed a slight deviation from the origin. This investment in numbers and analytical strategies is a key component in understanding the strength and sophistication of the constructed network. This includes all important values such as zmax, which are vital for determining the scale of the x-axis of the spectrum.
Furthermore, even with a number of variations in some small spectral values, they did not have a significant impact on the final results or the overall shape of the spectrum. This suggests that the neural network may be capable of providing accurate results even when dealing with different types of radiation sources.
Data Analysis and Comparison of Results
This analysis highlights the data produced by the neural network (NN) and compares it to the data resulting from Monte Carlo (MC) calculations. Overall, good results were found between the values calculated by the neural network and the available data from Monte Carlo. The use of the neural network to predict the energy spectrum related to specific events showed a significant agreement with the values resulting from the Monte Carlo calculations. This agreement reflects the strength of the network in learning from the training data and thus its ability to produce accurate results in various environments. For instance, during the experiment, the spectrum of single events in suspension cells was analyzed, where the network proved its ability to predict the spectrum of events whether the source of radioactivity was inside the nucleus, in the cytoplasm, or even on the cell surface.
The error rate for the numbers
Development of the Neural Network and Its Applications in Different Contexts
This neural network was developed with the aim of providing the energy spectrum for individual events in an efficient and accurate manner. This network represents a positive step towards reducing the traditional reliance on intensive Monte Carlo models that require substantial computational resources. The ability to use the data produced by a neural network based on an extensive dataset of various geometric shapes enhances research capabilities in the fields of radiotherapy, contributing to providing accurate estimates of the energy effects at the cellular level in various clinical contexts.
The significance of this work lies in the potential to expand the training database to include more diverse geometric shapes, enabling the network to adapt to new conditions and enhance its accuracy. Future contributions should focus on how to utilize the neural network for different inputs such as experimental data to achieve more precise results. This can be achieved by using the experimental energy spectrum and comparing it to the calculated data to maintain estimation accuracy.
Challenges and Limitations of the Current Neural Network
Some limitations remain concerning the ability of the neural network to generate the spectrum of single events only for the geometric shapes it was trained on. There is a strain on the accuracy of the results when attempting to predict a new spectrum that has not been trained on, indicating the need for further support from research exploring a wide variety of geometric designs. For example, if the network is trained to predict data related to a specific network, it may fail to provide accurate predictions for another completely different network. For this reason, developing more complex models capable of handling multiple geometric shapes is essential.
other challenges its ability to handle complex models, such as those requiring multiple energy sources or unconventional geometries. This necessitates providing multiple outputs that are integrated based on the relative magnitude of each component. This presents a significant challenge but also an opportunity for creativity in coding and developing future neural networks. By enhancing artificial intelligence, greater benefits can be derived from data storage and analysis, thus improving the accuracy of results in complex relationships between performance and energy.
The Role of Radiometric Measurements in Nuclear Therapy
Radiometric measurements are a critical aspect of nuclear therapy, as the use of radioactive isotopes in precise treatment requires accurate measurements to determine targeted radiation doses. Recent research emphasizes the importance of measuring radiation doses not only to improve the efficacy of treatment but also to reduce side effects. For example, when using alpha isotopes to treat tumors, it is necessary to provide accurate information about dose distribution within cancerous tissues in order to calculate the likelihood of tumor response to treatment and achieve tumor control.
The study “Lawhn-Heath,” published in “The Lancet Oncology,” serves as a good example of how radiometric measurements can improve treatment outcomes. The study addresses the importance of dose measurements in providing accurate data to enhance the design of therapeutic protocols. It also indicates that the use of measurements effectively contributes to reducing damage to healthy cells surrounding tumors. This illustrates how measurements can positively affect treatment outcomes and patient safety.
Precise Analysis of Energy Distribution in Targeted Radiation Therapy
The analysis of energy distribution in radiation therapy involves a deep understanding of microdosimetry. Research such as that conducted by “Kellerer” and “Roeske” serves as a starting point for understanding how to mitigate the negative effects of radiation on healthy cells. These studies provide theoretical models that enable researchers to accurately identify patterns of energy deposition. The significance of energy distribution analysis lies in the ability to assess cellular survival based on radiation energy and its placement within the target tissues.
One practical application of these concepts is the use of pharmaceutical fields supported by microdosimetry techniques to achieve varying degrees of dose control. “Roeske’s” study on the relationships between cell survival and the energy allocated to alpha isotopes represents a key point in this context. These results illustrate how radiation therapy can become more precise by relying on accurate data to calculate doses based on energy distribution patterns.
Future Challenges in the Use of Nuclides for Cancer Treatment
Despite the significant advances in the use of nuclides in cancer treatment, there are substantial challenges that must be addressed in this field. One of the most pressing challenges is determining the appropriate doses to be applied without compromising healthy body cells. Developing precise measurement tools is crucial to meet this challenge. Advances in imaging technology and dose measurement represent a qualitative leap in overcoming this obstacle.
A review of new types of nuclides, such as those discussed in the study “Eychenne” regarding the active gift of targeted therapy, reveals unprecedented possibilities for treating various tumors. However, these treatments require further research to ensure their safety and efficacy. Innovations in microdosimetry and studies on how radioactive materials affect cells form the foundation for future research to provide new treatments that are more effective and less harmful.
Precision Measurement Methods and Results Analysis
Precision measurement methods in radiation sciences are essential for understanding the dynamics associated with energy distribution within cells. A deep understanding of these processes can open new horizons in improving treatment strategies. We need better tools to measure doses more accurately to determine whether treatment has a direct effect on cancer cells as well as healthy cells.
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These methods utilize advanced devices such as thin lenses used in microdosimetry, which provide accurate measurements of the doses distributed within tissues. The use of data extracted from these measurements to improve radiation therapy models offers a platform for developing new strategies in treatment. The results from these studies are pivotal in transforming the approach to tumor treatment, indicating a bright future for targeted cancer therapy.
Targeted Radiation Therapy and its Role in Cancer Treatment
Targeted radiation therapy (TRT) applications have been present for over 80 years, evolving into a significant area of interest due to advancements in oncology, radiology, and biochemistry. This form of treatment relies on using high-affinity particles as carriers for radioactive isotopes that specifically target tumor cells, allowing for personalized treatment for each patient. TRT is characterized by minimizing adverse risks to healthy tissues by delivering a concentrated and specified dose of radiation directly to the tumor. There are three categories of radioactive isotopes that have been considered for targeted radiation therapy: alpha emitters, beta emitters, and electron emitters.
Alpha emitters are highly favorable compared to the other categories. They have unique characteristics including high energy (between 3 to 9 mega-electron volts) and a short radiation path (from 40 to 90 microns), which allows for precise targeting of tumor cells without affecting the surrounding healthy tissues. These unique characteristics enable effective treatment, especially in cases of cancers resistant to conventional treatment such as castration-resistant prostate cancer, enhancing the chances of improved clinical outcomes.
However, the use of alpha emitters faces challenges due to their stochastic nature in energy deposition at the cellular level. Dosimetry measurements have become contentious due to the complexities inherent in how that energy is distributed. For example, some cell nuclei may receive several hits from alpha particles while others receive none, leading to significant variability, sometimes estimated at over 20%. These fluctuations require the use of microdosimetry to better understand the effects of radiation exposure.
Microdosimetry and its Importance in Measuring Deposited Energy
The key challenge in radiation therapy is accurately measuring the dose at the subcellular level. Microdosimetry focuses on measuring the deposited energy per unit mass, allowing for an understanding of how radioactive particles affect tumor cells. The foundational equation used in this field states that the deposited energy within the cellular target (ϵ) and the mass of the target (m) can be used to determine the specific energy (z) as a measure of dose.
Measuring deposited energy involves studying the energy spectrum of a single event, which can be inferred from a multi-energy integration spectrum. The importance of these measurements lies in our ability to estimate the viable remnants of cells after receiving certain radiation doses. For instance, the specific energy spectrum can be used to predict how many cells will remain viable after exposure to a specific type of radiation, aiding in the optimization of treatment strategies.
The study of the specific energy deposited per cell is fundamental to understanding how various types of radiation affect cancer cells. For example, there is a method called “spectral moments,” which involves first and second calculations of the dedicated energy spectrum, deemed important for providing insights into the levels and proportions of damage that can occur to cells. Using this method, the effective dose can be estimated based on a specific type of radiation dose and the characteristics of the targeted cells.
The Practical Application of Neural Networks in Microdosimetric Calculations
Recently, neural networks have been employed to facilitate microdosimetric dose calculations, reflecting a revolutionary shift in measurement methods. Through a machine-learning-based model, a network has been developed to accommodate excess data to accurately calculate the specific energy spectrum. This process represents an evolutionary step in the utilization of computing sciences in radiation medicine.
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The process involves collecting a wide dataset containing variables such as cell and nucleus sizes, alpha particle energy, and the geometries used. By training the network on this data, the model can learn from patterns and apply the extracted knowledge to new types of therapies or new combinations in a short amount of time, contributing to the acceleration of trials for new treatments and providing innovative solutions in the field of cancer therapy.
For example, in previous studies, the network was able to accurately calculate spectral moments, allowing physicians and scientists to improve targeted radiation therapy strategies and achieve better clinical outcomes. These achievements represent a new threshold in understanding the complex effects of radiation on cancer cells, giving researchers new tools to develop more precise and specialized treatments.
The Geometric Structure of the Models Used in the Study
The geometric models used in this study were designed to mimic the complex formations of tissues, such as cell clusters. These models are represented by layers of tissues woven as microspheres. Surrounding each central spherical nucleus is a cluster of cells packed in a plane, where these planes are stacked to form layers that mimic biological tissues. This design is essential as the multidimensional and intricate nature of human tissues requires accurate models that reflect the real interactions between radiation and cells.
The models utilized multiple sources of radiation, including a uniform distribution of activity that focuses on cells and specific sites such as cell nuclei. For example, a uniformly distributed activity was mimicked except for the cell nuclei or was active only in a spherical shell between the cell membrane and the nucleus radius multiplied by 1.25, in addition to activity confined to the cytoplasm. The energy levels used in these models ranged from 3.97 to 8.78 mega-electron volts, which helps provide a higher accuracy in approximating radiation behavior in tissues.
Furthermore, multiple levels of cell and nucleus sizes were used, with nucleus diameters ranging from 2-10 micrometers and cell diameters from 2.5-20 micrometers. These variations contribute to expanding the database and provide more realistic techniques for developing a model that can be applied in multiple medical and scientific contexts. The use of a wide array of evidence including 2264 unique combinations of technical variables makes subsequent studies more accurate and robust.
Neural Network Structure and Training
The neural network used in this study was developed using the MATLAB® deep learning tool. During this process, the neural network was created, evaluated, and modified until optimal results were achieved. The Levenberg-Marquardt algorithm was used for training, which combines the gradient descent method and the Gauss-Newton method, making it effective for solving nonlinear approximation problems.
The data transformation process involved several adjustments before training began. For instance, a logarithmic transformation was applied to all data due to the highly skewed nature of the energy plots. Additionally, a unit value was added to all values before the transformation process to ensure that multiple targets that may contain a zero value were handled. These operations improved the overall performance of the neural network.
Several combinations of hidden layers were tested, and the optimal structure was determined to be a neural network consisting of two hidden layers, with the first layer containing 10 units and the second containing 26 units. During the training process, the data were divided into three groups: 70% for training, 15% for validation, and 15% for testing. This approach proved effective in reducing the root mean square error, making it a reliable indicator for performance assessment.
Experimental Results and Data Analysis
The results extracted from the training process showed a good match between the predicted results and the known data, as shown in the prepared graphs. The X-axes represent the actual values calculated using Monte Carlo simulations, while the Y-axes represent the values predicted by the neural network. The slope of the best fit line close to one indicates prediction accuracy.
The results show
The data also indicate that the values extracted from the neural network align well across the entire range, reflecting the importance of using these values as criteria for adjusting axes in the resultant graphs. The root mean square error values for specific power outputs and normative results were small amounts indicating excellent model performance.
These results allow for an in-depth analysis of energy distribution and the calculation of values specific to the generated chart visuals, contributing to a better scientific understanding of the interactions between radiation and tissues. This approach also provides the potential for application in other fields requiring precision in energy measurements, such as medical physics and molecular biology.
Developing Neural Networks for Generating Energy Spectra for Individual Events
In recent decades, neural network technology has seen significant advancements across various fields, including medical physics. In this study, a neural network was developed capable of producing the specific energy spectrum for individual events (f1(z1)) from eight different source-target geometries. The study results showed a good match between the spectra produced by the neural network and the values generated from Monte Carlo (MC) simulations, demonstrating the strength and accuracy of this approach. The resulting spectrum indicates the network’s ability to learn from the training dataset, successfully predicting the output spectrum for several simple and complex scenarios, such as producing spectra from sources located in the nucleus, cytoplasm, and cell surface.
The results demonstrate that the neural network is not only capable of generating simple energy spectra but can also handle more complex spectra. These results serve as evidence for the principle of the network’s success in deep learning tasks, showing consistent accuracy as it transitioned from simple to complex spectra.
Error Analysis in Extracted Values
The study involves analyzing the errors between the computed values for
The differences are computed using statistical information, such as standard deviation and range of values, providing a better understanding of the quality and performance analysis of the neural network. These statistics are composed of approximately normal distributions, facilitating the calculation of high-confidence intervals. The results were completely consistent with previously published values, enhancing the credibility of the neural network as an effective tool in this field.
Practical Considerations in Using Neural Networks
Despite the success achieved, there are important practical considerations related to the efficiency of the neural network. One of the main limitations is the network’s ability to generate energy spectra for only one geometry at a time. If there is interest in all spectra from multiple sources or multiple energies collectively, the individual spectra must first be generated independently.
These limitations present a challenge, but they also open up avenues for improving the neural network by expanding the training dataset to include a variety of geometries. By doing so, the network may be able to learn from the expanded dataset and become more adaptable, allowing it to better handle various combinations and complex spectra.
Using Experimental Data to Enhance Model Accuracy
One important aspect is that the data used to train the network was primarily derived from Monte Carlo data. However, this is not the only data that can be utilized; the model can also benefit from real experimental data. The use of experimental data enables direct comparison between the neural network results and direct measurements, potentially reducing any experimental errors.
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The experimental data is not always available in the necessary quantities; however, integrating it can significantly improve network performance. Such an approach will also enhance the reliability of predictions related to the success of machine learning algorithms, representing a significant step toward further advancements in medical physics and cancer treatment.
Future Challenges and Opportunities in Neural Network Development
There are known challenges in the use of neural networks, such as the limited effectiveness of the model with new geometries it has not previously been trained on. This means that the resulting predictions may not be accurate for new modalities. Therefore, it is essential to continuously expand the dataset and train the network on various geometries.
Although these limitations represent constraints, there are opportunities for further developing the network, allowing it to be more flexible and capable of processing new data. Larger and more diverse datasets need to be compiled, in addition to developing new methods to enhance deep learning.
These steps are crucial for expanding the capabilities of neural networks in practical applications, contributing to improved cancer treatment and the accuracy of other medical procedures. By effectively combining the use of neural networks with experimental data, strong outcomes can be achieved in enhancing treatment and health outcomes.
Radiation Therapy Using Alpha Particles
Radiation therapy using alpha particles is a recent development in the field of chemotherapy and nuclear medicine. This type of therapy is a special form of targeted radiation therapy that relies on directly targeting cancer cells using alpha particles, which are characterized by their ability to effectively destroy cells. Alpha particles are heavy and fast, and due to their high energy, they can cause severe damage to the DNA of the targeted cells, leading to cell death. Unlike traditional radiation therapies that may affect healthy cells, alpha particle therapy focuses on increasing treatment efficacy while reducing damage to surrounding healthy tissues.
Many studies have addressed the impact of alpha particle therapy on cancer cells, with research showing that the number of collisions of alpha particles with the cell nucleus necessary to eliminate cancer cells can enhance the treatment’s effectiveness. Researchers such as Roeske and Stinchcomb have indicated that the models used to assess the efficacy of the therapy can be improved by studying dose distribution algorithms and their effects on cells. A tumor control model has been established to evaluate the effectiveness of the radiation enhancers released by alpha particles. Such models can assist in determining optimal doses and improving treatment outcomes.
Precise Knowledge and Radiation Doses
Precise knowledge about the distribution of particles within tissues is a key element in understanding how alpha particle therapy affects cancer cells. Micrometric techniques are used to determine how radiation doses impact cellular structure. For example, precise models have been used to ascertain how alpha particles distribute within cells and their effects on nuclei. Through experiments, the ratio of energy deposited in the nucleus compared to the whole cell was measured, which can provide comprehensive guidelines for designing customized therapeutic protocols based on the physical characteristics of each cell type.
Many studies rely on experimental data concerning alpha particle doses, wherein numerous research efforts parallel the airborne dose measurements from these particles. The use of accurate mathematical equations opens new horizons for understanding the biological impacts of therapy. For instance, mathematical models have been developed to illustrate the relationship between the range of alpha particles and the amount of energy deposited in the nucleus, thus improving treatment effectiveness and reducing the required dose.
Challenges
Future Prospects
Despite the significant advancements in the use of alpha particles for cancer treatment, there are a number of challenges that need to be addressed to ensure the success of this type of therapy. These challenges include how to safely manage doses so that they are effective against tumors without harming healthy tissues. The study of the dose-response relationship remains a prerequisite, as a precise understanding of this relationship can greatly impact dosing algorithms.
Another challenge related to alpha particle therapy is achieving the ideal distribution of these particles within the body. Unbalanced distribution can reduce treatment efficacy, making it essential to explore new methods to enhance tumor targeting. Clinical trials involving alpha particle-based drugs continue to progress, providing insights into how to improve existing therapeutic approaches while increasing effectiveness.
In the future, it is expected that artificial intelligence and machine learning technologies will become an integral part of the research and treatment process for complex cancer cases. The use of these technologies may facilitate the identification of optimal dosing models and lead to better outcomes at the individual level. The integration of basic research with clinical applications holds great promise for improving alpha particle therapy outcomes and providing safer, new treatments for patients suffering from malignant tumors.
Source link: https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1394671/full
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