Field Of Radiotherapy Research Articles

The Implementation of a MOOC-Based Flipped Classroom Teaching Method in the Context of Oncology Radiotherapy Residency Training.

The objective of this study is to examine the efficacy of the flipped classroom blended teaching method in the context of massive open online courses (MOOCs) for implementing standardized training and teaching of residents in oncology radiotherapy. A total of 48 junior residents who received standardized training at the Oncology Radiology Department of Harbin Medical University Cancer Hospital between September 2021 and August 2023 were randomly divided into two groups-i.e., the research group (24 cases) and the control group (24 cases)-using the random number table method. The control group received conventional didactic training, whereas the research group participated in a blended learning approach based on the MOOC model. The assessment results, along with the evaluations of teaching effectiveness, self-learning ability, and teaching satisfaction questionnaires, were observed and compared for the two groups of students. Compared with the control group, the research group presented significantly higher scores on theoretical foundations, skill operation, and case analysis (P < 0.05). The research group also showed greater outcomes than the control group in terms of improved theoretical knowledge, problem-solving skills, self-learning ability, teamwork, and communication (P < 0.05). The students in the research group presented significantly higher scores on measures of self-motivation beliefs, task analysis, self-monitoring and adjustment, and self-evaluation than those in the control group (P < 0.05). The research group also demonstrated significantly higher levels of satisfaction than the control group in terms of improvements in learning interest and initiative, clinical thinking ability, problem-solving ability, team cooperation ability, and the level of radiotherapy target delineation (P < 0.05). The implementation of MOOC-based flipped classroom blended teaching was shown to have positive effects on the standardized training and teaching of residents in the field of oncology radiotherapy. This approach can undoubtedly enhance students' academic performance, problem-solving abilities, and self-learning aptitudes while effectively stimulating their learning interests and initiative. Therefore, MOOC-based flipped classroom blended teaching is a valuable candidate for clinical application and promotion.

Recent developments in the field of radiotherapy for the management of lung cancer.

Lung cancer has a poor prognosis, and further improvements in outcomes are needed. Radiotherapy plays an important role in the treatment of unresectable lung cancer, and there have been recent developments in the field of radiotherapy for the management of lung cancer. However, to date, there have been few reviews on the improvement in treatment outcomes associated with high precision radiotherapy for lung cancer. Thus, this review aimed to summarize the recent developments in radiotherapy techniques and indicate the future directions in the use of radiotherapy for lung cancer. Stereotactic body radiotherapy (SBRT) for unresectable stage I lung cancer has been reported to improve local control rates without severe adverse events, such as radiation pneumonitis. For locally advanced lung cancer, a combination of chemoradiotherapy and adjuvant immune checkpoint inhibitors dramatically improves treatment outcomes, and intensity-modulated radiotherapy (IMRT) enables safer radiation therapy with less frequent pneumonitis. Particle beam therapy, such as carbon-ion radiotherapy and proton beam therapy, has been administered as advanced medical care for patients with lung cancer. Since 2024, it has been covered under insurance for early stage lung cancer with tumors ≤ 5cm in size in Japan. In addition to chemotherapy, local ablative radiotherapy improves treatment outcomes in patients with oligometastatic stage IV lung cancer. A particular problem with radiotherapy for lung cancer is that the target location changes with respiratory motion, and various physical methods have been used to control respiratory motion. Recently, coronavirus disease has had a major impact on lung cancer treatment, and cancer treatment during situations, such as the coronavirus pandemic, must be performed carefully. To improve treatment outcomes for lung cancer, it is necessary to fully utilize evolving radiotherapy modalities, and the role of radiotherapy in lung cancer treatment is expected to increase.

Interventional radiotherapy: CT-sim guided modern minimally invasive technique

The concept of interventional radiology was introduced in 1976 as a new diagnostic and therapeutic method, relying on image guidance, such as X-ray, ultrasound, computed tomography (CT), and magnetic resonance imaging. Its indications mainly include obstructive diseases, and to a lesser extent, malignant tumors. Radiotherapy (RT) plays a pivotal role in the comprehensive treatment of cancer. The advent of the CT simulator has significantly enhanced the precision of RT and improvement outcomes and accelerated the development of a series of diagnostic and RT modalities, including puncture biopsy, fiducial marker implantation, high-dose rate after-loading after-loading brachytherapy, and radioactive seed implantation. The innovative concept of image-guidance interventional RT, inspired by interventional radiology, extends the benefits to not only cancer diagnosis but also RT. The image-guidance interventional RT advances the field of RT by expanding its indications and connotation.

A review of diamond dosimeters in advanced radiotherapy techniques.

This review article synthesizes key findings from studies on the use of diamond dosimeters in advanced radiotherapy techniques, showcasing their applications, challenges, and contributions to enhancing dosimetric accuracy. The article explores various dosimeters, highlighting synthetic diamond dosimeters as potential candidates especially due to their high spatial resolution and negligible ion recombination effect. The clinically validated commercial dosimeter, PTW microDiamond (mD), faces limitations in small fields, proton and hadron therapy and ultra-high dose per pulse (UHDPP) conditions. Variability in reported values for field sizes 2 2 is noted, reflecting the competition between volume averaging and density perturbation effects. PTW's introduction of flashDiamond (fD) holds promise for dosimetric measurements in UHDPP conditions and is reliable for commissioning ultra-high dose rate (UHDR) electron beam systems, pending the clinical validation of the device. Other advancements in diamond detectors, such as in 3D configurations and real-time dose per pulse x-ray detectors, are considered valuable in overcoming challenges posed by modern radiotherapy techniques, alongside relative dosimetry and pre-treatment verifications. The studies discussed collectively provide a comprehensive overview of the evolving landscape of diamond dosimetry in the field of radiotherapy, and offer insights into future directions for research and development in thefield.

Dose prediction for cervical cancer in radiotherapy based on the beam channel generative adversarial network

BackgroundExisting deep learning methods, such as generative adversarial network (GAN) technology, face challenges when dealing with mixed datasets, which involve a combination of Intensity Modulated Radiotherapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT). This issue significantly complicates the application of dose prediction in the field of radiotherapy. In this study, we propose a novel approach called beam channel GAN (Bc-GAN) to address the task of radiation dose prediction for mixed datasets. Bc-GAN introduces a dose prediction calculation method that requires less precision. By defining an approximate range for dose prediction, Bc-GAN limits the physical range of GAN prediction, resulting in more reasonable dose distribution predictions. MethodsWe adopt a beam angle weighting method to determine the beam angle in the dose calculation. The dose of the beam with the highest weight is calculated using medical images and is then inputted into the artificial intelligence dose prediction model as the input channel. Additionally, we collect data from a total of 346 patients with Cervical Cancer (CC) for dataset. After cleaning the data, we exclude 51 cases with incomplete organ delineation, leaving us with 295 cases (IMRT: VMAT = 137:158) randomly divided into three sets: the training set, the validation set, and the test set, with proportions of 205:60:30, respectively. The assessment of model predictions was conducted via an analysis of dose distributions on the tomographic plane, dose volume histogram (DVH), and dosimetric parameters within the target zones and organs at risk (OAR). ResultsAfter DVH analysis, minimal discrepancy was found between predicted and actual dose distributions in PTV and OAR. The predicted distribution aligned with clinical standards. Dosimetric parameters for PTV were generally lower in the predicted model, except for homogeneity index (HI) (0.238 ± 0.024, P = 0.017) and Dmax (53.599 ± 0.710 Gy, P = 1.8e-05). The prediction model varied in estimating doses for six organs. Specifically, small intestine showed higher V20 (67.92 ± 51.64 %, P = 0.019) and V30 (57.171 ± 1.213 %, P = 0.024) than manual planning. A similar trend was seen in colon's V30 (37.13 ± 61.14 %, P = 0.016). However, predicted bladder V30 (87.51 ± 41.44 %, P = 2.03e-16) was lower, indicating significant dosimetric differences. ConclusionOverall, this study presents an innovative prediction method for CC in radiotherapy using the Bc-GAN model, addressing the challenges posed by different radiotherapy techniques. The proposed approach allows IMRT and VMAT in radiotherapy to be used as training sets, enabling the potential for large-scale engineering and commercialization applications of artificial intelligence (AI). The Bc-GAN-based prediction method for CC in radiotherapy not only reduces the amount of data needed for the training set but also expedites the model generation process. This approach can be applied to guide the development of clinical radiation therapy plans. Furthermore, future studies should consider extending the dose prediction method to encompass other types of tumors.

Heat shock proteins on the surface of tumor cells as a target for anti-tumor therapy

According to WHO, oncological diseases are the cause of ~5 million people deaths annually. To date, there is no universal solution to fight cancer, despite outstanding achievements in the field of radiotherapy, chemotherapy, and immunotherapy. In this regard, there is a need to develop new approaches to antitumor therapy, in particular based on the search and use of targeted molecules that allow killing tumor cells of various types with high efficiency, without significant toxic effects on healthy organs and tissues. This review presents the characteristics of the main heat shock protein (HSP) families, the features of their expression in tumor cells and the possibility of using monoclonal antibodies to these proteins as a guiding vector for antitumor immunotherapy.

A methodology for computationally generating phase space files for Monte Carlo simulations applied to treatment plans for medical linear accelerators

The application of Monte Carlo (MC) simulation in the context of Medical Linear Accelerator (LinAc) treatment planning has increased in recent decades owing to its accuracy in dose calculations. The Multileaf Collimator (MLC), a component of the LinAc heads, plays an essential role by allowing the precise customization of the treatment beam's shape, effectively targeting the tumor area while minimizing radiation exposure to surrounding healthy structures. During a jaws static treatment, this MLC configuration varies for each angular position of the accelerator gantry, while the rest of the LinAc geometry remains static. To address the operation of the accelerator during the treatment planning avoiding the need of computationally intensive and repetitive simulations, phase-space files (PSF) are widely employed in LinAc MC calculations. This study aims to introduce and validate a methodology for the computational generation of PSFs specifically defined just below the MLC. These PSFs will assume the appropriate shape corresponding to the angle at which the accelerator gantry is positioned during treatment. The core of this methodology involves the creation of a comprehensive database containing precalculated probability functions derived from MC. In order to validate the methodology, simulated dose values of a LinAc emitting a 6 MV photon beam have been employed. The results have been tested against experimental data, and compared with the results from complete simulations, yielding PSFs of varying shapes. Finally, this study reveals that the results obtained by comparing organ dose values computed from the generated PSFs to those from the reference PSF are statistically compatible. This innovative method not only offers a valuable tool for optimizing LinAc treatment planning but also underscores the importance of accurate dose calculations in the field of radiation therapy.

Prediction of Cell Survival Rate Based on Physical Characteristics of Heavy Ion Radiation.

The effect of ionizing radiation on cells is a complex process dependent on several parameters. Cancer treatment commonly involves the use of radiotherapy. In addition to the effective killing of cancer cells, another key aspect of radiotherapy is the protection of healthy cells. An interesting position is occupied by heavy ion radiation in the field of radiotherapy due to its high relative biological effectiveness, making it an effective method of treatment. The high biological efficiency of heavy ion radiation can also pose a danger to healthy cells. The extent of cell death induced by heavy ion radiation in cells was investigated using statistical learning methods in this study. The objective was to predict the healthy cell survival rate based on the physical parameters of the available ionizing radiation. This paper is based on secondary research utilizing the PIDE database. Throughout this study, a local regression and a random forest model were generated. Their predictions were compared to the results of a linear-quadratic model commonly utilized in the field of ionizing radiation using various metrics. The relationship between dose and cell survival rate was examined using the linear-quadratic (LQM) model and local regression (LocReg). An R2 value of 88.43% was achieved for LQM and 89.86% for LocReg. Upon incorporating linear energy transfer, the random forest model attained an R2 value of 96.85%. In terms of RMSE, the linear-quadratic model yielded 9.5910-2, the local regression 9.2110-2, and the random forest 1.96 × 10-2 (lower values indicate better performance). All of these methods were also applied to a log-transformed dataset to decrease the right skewedness of the distribution of the datapoints. This significantly reduced the estimates made with LQM and LocReg (28% decrease in the case of R2), while the random forest retained nearly the same level of estimation as the untransformed data. In conclusion, it can be inferred that dose alone provides a somewhat satisfactory explanatory power for cell survival rate, but the inclusion of linear energy transfer can significantly enhance prediction accuracy in terms of variance and explanatory power.

DeCoGAN: MVCT image denoising via coupled generative adversarial network

Objective. In helical tomotherapy, image-guided radiotherapy employs megavoltage computed tomography (MVCT) for precise targeting. However, the high voltage of megavoltage radiation introduces substantial noise, significantly compromising MVCT image clarity. This study aims to enhance MVCT image quality using a deep learning-based denoising method. Approach. We propose an unpaired MVCT denoising network using a coupled generative adversarial network framework (DeCoGAN). Our approach assumes that a universal latent code within a shared latent space can reconstruct any given pair of images. By employing an encoder, we enforce this shared-latent space constraint, facilitating the conversion of low-quality (noisy) MVCT images into high-quality (denoised) counterparts. The network learns the joint distribution of images from both domains by leveraging samples from their respective marginal distributions, enhanced by adversarial training for effective denoising. Main Results. Compared to an analytical algorithm (BM3D) and three deep learning-based methods (RED-CNN, WGAN-VGG and CycleGAN), the proposed method excels in preserving image details and enhancing human visual perception by removing most noise and retaining structural features. Quantitative analysis demonstrates that our method achieves the highest peak signal-to-noise ratio and Structural Similarity Index Measurement values, indicating superior denoising performance. Significance. The proposed DeCoGAN method shows remarkable MVCT denoising performance, making it a promising tool in the field of radiation therapy.

GCT-28. THE IMPACT OF RESIDUAL TUMOR ON THE OUTCOMES OF CENTRAL NERVOUS SYSTEM GERMINOMA

Abstract BACKGROUND The inclusion of chemotherapy in the treatment backbone of central nervous system germinoma has helped reduce the field and dose of radiotherapy. Typically, patients wirh residual disease after chemotherapy are treated with higher doses of radiotherapy, which may not be necessary. Hence, we aimed to assess the impact of residual disease at different points during therapy on the outcomes of germinoma patients. METHODS We performed an extensive review of all germinoma patients diagnosed at Hospital Garrahan in Argentina over the last 20 years. We evaluated the chemotherapy response after 2 cycles and 4 cycles of chemotherapy and after radiotherapy. RESULTS Thirty nine germinoma were included (localized 34; disseminated 5). All patients were treated as per the SIOP-CNS-GCT-96 strategy. At the end of 2 cycles of chemotherapy, 21 patients achieved a complete response (CR), 10 had a partial response (PR), and 8 patients didn’t have MRI evaluation. At the end of 4 chemotherapy cycles, 10 additional patients achieved a CR, 7 still had a PR, while one patient didn’t have MRI evaluation. Only 3 patients had residual tumour after radiotherapy; one had a second-look surgery that didn’t show any evidence of viable tumour. None of the patients with residual tumour at the end of chemotherapy or radiotherapy relapsed with a median follow up of 6.3 years. CONCLUSION Residual tumor at the end of both chemotherapy and radiotherapy does not have an impact on the outcomes. Clinical trials in germinomas may need to investigate administering the same dose of radiotherapy for patients regardless of the presence of residual disease at the end of chemotherapy. Furthermore, patients with no residual disease at the end of two cycles of chemotherapy may benefit from moving directly to radiotherapy without the need to receive two additional cycles of chemotherapy.

Precision dose prediction for breast cancer patients undergoing IMRT: The Swin-UMamba-Channel Model

BackgroundRadiation therapy is one of the crucial treatment modalities for cancer. An excellent radiation therapy plan relies heavily on an outstanding dose distribution map, which is traditionally generated through repeated trials and adjustments by experienced physicists. However, this process is both time-consuming and labor-intensive, and it comes with a degree of subjectivity. Now, with the powerful capabilities of deep learning, we are able to predict dose distribution maps more accurately, effectively overcoming these challenges. MethodsIn this study, we propose a novel Swin-UMamba-Channel prediction model specifically designed for predicting the dose distribution of patients with left breast cancer undergoing radiotherapy after total mastectomy. This model integrates anatomical position information of organs and ray angle information, significantly enhancing prediction accuracy. Through iterative training of the generator (Swin-UMamba) and discriminator, the model can generate images that closely match the actual dose, assisting physicists in quickly creating DVH curves and shortening the treatment planning cycle. Our model exhibits excellent performance in terms of prediction accuracy, computational efficiency, and practicality, and its effectiveness has been further verified through comparative experiments with similar networks. ResultsThe results of the study indicate that our model can accurately predict the clinical dose of breast cancer patients undergoing intensity-modulated radiation therapy (IMRT). The predicted dose range is from 0 to 50 Gy, and compared with actual data, it shows a high accuracy with an average Dice similarity coefficient of 0.86. Specifically, the average dose change rate for the planning target volume ranges from 0.28 % to 1.515 %, while the average dose change rates for the right and left lungs are 2.113 % and 0.508 %, respectively. Notably, due to their small sizes, the heart and spinal cord exhibit relatively higher average dose change rates, reaching 3.208 % and 1.490 %, respectively. In comparison with similar dose studies, our model demonstrates superior performance. Additionally, our model possesses fewer parameters, lower computational complexity, and shorter processing time, further enhancing its practicality and efficiency. These findings provide strong evidence for the accuracy and reliability of our model in predicting doses, offering significant technical support for IMRT in breast cancer patients. ConclusionThis study presents a novel Swin-UMamba-Channel dose prediction model, and its results demonstrate its precise prediction of clinical doses for the target area of left breast cancer patients undergoing total mastectomy and IMRT. These remarkable achievements provide valuable reference data for subsequent plan optimization and quality control, paving a new path for the application of deep learning in the field of radiation therapy.

A comprehensive pre-clinical treatment quality assurance program using unique spot patterns for proton pencil beam scanning FLASH radiotherapy.

Quality assurance (QA) for ultra-high dose rate (UHDR) irradiation is a crucial aspect in the emerging field of FLASH radiotherapy (FLASH-RT). This innovative treatment approach delivers radiation at UHDR, demanding careful adoption of QA protocols and procedures. A comprehensive understanding of beam properties and dosimetry consistency is vital to ensure the safe and effective delivery of FLASH-RT. To develop a comprehensive pre-treatment QA program for cyclotron-based proton pencil beam scanning (PBS) FLASH-RT. Establish appropriate tolerances for QA items based on this study's outcomes and TG-224 recommendations. A 250 MeV proton spot pattern was designed and implemented using UHDR with a 215nA nozzle beam current. The QA pattern that covers a central uniform field area, various spot spacings, spot delivery modes and scanning directions, and enabling the assessment of absolute, relative and temporal dosimetry QA parameters. A strip ionization chamber array (SICA) and an Advanced Markus chamber were utilized in conjunction with a 2cm polyethylene slab and a range (R80) verification wedge. The data have been monitored for over 3 months. The relative dosimetries were compliant with TG-224. The variations of temporal dosimetry for scanning speed, spot dwell time, and spot transition time were within±1mm/ms,±0.2 ms, and±0.2 ms, respectively. While the beam-to-beam absolute output on the same day reached up to 2.14%, the day-to-day variation was as high as 9.69%. High correlation between the absolute dose and dose rate fluctuations were identified. The dose rate of the central 5×5 cm2 field exhibited variations within 5% of the baseline value (155Gy/s) during an experimental session. A comprehensive QA program for FLASH-RT was developed and effectively assesses the performance of a UHDR delivery system. Establishing tolerances to unify standards and offering direction for future advancements in the evolving FLASH-RT field.

Review of the textbook “Oncology 2.0” edited by D. Sci. (Med.), Professor Sh.Kh. Gantsev

The textbook “Oncology 2.0”, prepared by a team of authors under the guidance of Professor Sh.Kh. Gantsev, is a comprehensive presentation of modern approaches and methods in oncology, aimed at training specialized personnel in this field. The textbook uses an integration approach that combines clinical and morphological aspects of oncology. The textbook material complies with the Federal State Educational Standard of Higher Education. The book is written at a high scientific and methodological level. The integration of the latest achievements in scientific research, the latest diagnostic and treatment methods are presented, which makes it an important tool for the education of residents and graduate students. The textbook stands out for its detailed description of the processes of carcinogenesis and cellular mechanisms of malignant transformation. The textbook is intended for residents and graduate students specializing in the field of oncology, oncological radiotherapy, molecular and cellular aspects of cancer and can be used as the main educational literature in the discipline.

The impact of dose rate on responses of human lens epithelial cells to ionizing irradiation

The knowledge on responses of human lens epithelial cells (HLECs) to ionizing radiation exposure is important to understand mechanisms of radiation cataracts that are of concern in the field of radiation protection and radiation therapy. However, biological effects in HLECs following protracted exposure have not yet fully been explored. Here, we investigated the temporal kinetics of γ-H2AX foci as a marker for DNA double-strand breaks (DSBs) and cell survival in HLECs after exposure to photon beams at various dose rates (i.e., 150 kVp X-rays at 1.82, 0.1, and 0.033 Gy/min, and 137Cs γ-rays at 0.00461 Gy/min (27.7 cGy/h) and 0.00081 Gy/min (4.9 cGy/h)), compared to those in human lung fibroblasts (WI-38). In parallel, we quantified the recovery for DSBs and cell survival using a biophysical model. The study revealed that HLECs have a lower DSB repair rate than WI-38 cells. There is no significant impact of dose rate on cell survival in both cell lines in the dose-rate range of 0.033–1.82 Gy/min. In contrast, the experimental residual γ-H2AX foci showed inverse dose rate effects (IDREs) compared to the model prediction, highlighting the importance of the IDREs in evaluating radiation effects on the ocular lens.

An Educational Chatbot Using AI in Radiotherapy

The surge in demand for information in cancer centers and hospitals, particularly during the pandemic, overwhelmed the limited manpower available. To address this challenge, there arose a need to develop an educational chatbot tailored for diverse user groups in the field of radiotherapy, including patients and their families, the general public, and radiation staff. Objective: In response to the pressing clinical demands, the primary aim of this endeavor is to delve into the intricacies of designing an educational chatbot for radiotherapy using artificial intelligence.Methods: The chatbot is meticulously crafted using a dialogue tree and layered structure, seamlessly integrated with artificial intelligence functionalities, notably natural language processing (NLP). This adaptable chatbot can be deployed across various platforms, such as IBM Watson Assistant, and embedded in websites or diverse social media channels.Results: Employing a question-and-answer methodology, the chatbot adeptly engages users seeking information on radiotherapy, presenting an approachable and reassuring interface. Recognizing that users, often anxious, may struggle to articulate precise questions, the chatbot facilitates the interaction by offering a curated list of questions. The NLP system augments the chatbot's ability to discern user intent, ensuring the provision of accurate and targeted responses. Notably, the study reveals that functional features, including mathematical operations, are preferred in educational chatbots, necessitating routine updates to furnish fresh content and features.Conclusions: The study culminates in the affirmation that leveraging artificial intelligence facilitates the creation of an educational chatbot capable of disseminating information to users with diverse backgrounds in radiotherapy. Furthermore, the importance of rigorous testing and evaluation, informed by user feedback, is emphasized to iteratively enhance and refine the chatbot's performance. Keywords: AI, machine learning, NLP, chatbot, radiotherapy, IoT, healthcare.

Thermoplastic polymers as water substitutes

Background. New applications of 3D printing have recently appeared in the fields of radiotherapy and radiology, but the knowledge of many radiological characteristics of the compounds involved is still limited. Therefore, studies are needed to improve our understanding about the transport and interaction of ionizing radiation in these materials. Purpose. The purpose of this study is to perform an analysis of the most important radiation interaction parameters in thermoplastic materials used in Fused Deposition Modeling 3D printing. Additionally, we propose improvements to bring their characteristics closer to those of water and use them as water substitutes in applications such as radiodiagnosis, external radiotherapy, and brachytherapy. Methods. We have calculated different magnitudes as mass linear attenuation, mass energy absorption coefficients, as well as stopping power and electronic density of several thermoplastic materials along with various compounds that have been used as water substitutes and in a new proposed blend. To perform these computations, we have used the XCOM and ESTAR databases from NIST and the EGSnrc code for Montecarlo simulations. Results. From the representation of the calculated interaction parameters, we have been able to establish relationships between their properties and the proportion of certain chemical elements. In addition, studying these same characteristics in different commercial solutions used as substitutes for water phantoms allows us to extrapolate improvements for these polymers. Conclusion. The radiological characteristics of the analyzed thermoplastic materials can be improved by adding some chemical elements with atomic numbers higher than oxygen and by using polyethylene in new blends.

The Integration of Deep Learning in Radiotherapy: Exploring Challenges, Opportunities, and Future Directions through an Umbrella Review.

This study investigates, through a narrative review, the transformative impact of deep learning (DL) in the field of radiotherapy, particularly in light of the accelerated developments prompted by the COVID-19 pandemic. The proposed approach was based on an umbrella review following a standard narrative checklist and a qualification process. The selection process identified 19 systematic review studies. Through an analysis of current research, the study highlights the revolutionary potential of DL algorithms in optimizing treatment planning, image analysis, and patient outcome prediction in radiotherapy. It underscores the necessity of further exploration into specific research areas to unlock the full capabilities of DL technology. Moreover, the study emphasizes the intricate interplay between digital radiology and radiotherapy, revealing how advancements in one field can significantly influence the other. This interdependence is crucial for addressing complex challenges and advancing the integration of cutting-edge technologies into clinical practice. Collaborative efforts among researchers, clinicians, and regulatory bodies are deemed essential to effectively navigate the evolving landscape of DL in radiotherapy. By fostering interdisciplinary collaborations and conducting thorough investigations, stakeholders can fully leverage the transformative power of DL to enhance patient care and refine therapeutic strategies. Ultimately, this promises to usher in a new era of personalized and optimized radiotherapy treatment for improved patient outcomes.

Computational Approach for Spatially Fractionated Radiation Therapy (SFRT) and Immunological Response in Precision Radiation Therapy.

The field of precision radiation therapy has seen remarkable advancements in both experimental and computational methods. Recent literature has introduced various approaches such as Spatially Fractionated Radiation Therapy (SFRT). This unconventional treatment, demanding high-precision radiotherapy, has shown promising clinical outcomes. A comprehensive computational scheme for SFRT, extrapolated from a case report, is proposed. This framework exhibits exceptional flexibility, accommodating diverse initial conditions (shape, inhomogeneity, etc.) and enabling specific choices for sub-volume selection with administrated higher radiation doses. The approach integrates the standard linear quadratic model and, significantly, considers the activation of the immune system due to radiotherapy. This activation enhances the immune response in comparison to the untreated case. We delve into the distinct roles of the native immune system, immune activation by radiation, and post-radiotherapy immunotherapy, discussing their implications for either complete recovery or disease regrowth.

The clinical roadmap in a dual-beam hadrontherapy centre: tumour-based and patient-tailored selection criteria, management of range uncertainties and oncological patient pathway

PurposeIn the present short report, we encompass the radiobiological and dosimetric advantages of particle beam radiotherapy and we illustrate the oncological pathway for patients eligible for hadrontherapy being used at the National Center for Oncological Hadrontherapy (CNAO).MethodsWith a narrative approach, we examined the clinical and practical aspects of hadrontherapy compared to traditional photon beam radiotherapy. We described the need to select patients considering the intrinsic characteristics both of the tumour and the patient. Moreover, we described the decisional oncological tree and the patient pathway by our facility.ResultsConsidering the dosimetric and radiobiological characteristics of particle beam radiotherapy, the selection of patients can not be done regardless of the intrinsic tumour and patient hallmarks. In particular, the tumour radioresistance, the patient radiosensitivity and the need to avoid post-actinic toxicities in long-term survivors should guide the clinical indication. The finite range of particles should be considered to avoid treatment uncertainties. Multidisciplinary national and international collaboration is crucial to better manage patients to treat with hadrontherapy and to create robust clinical evidence.ConclusionHadrontherapy, with its distinctive physical and biological advantages, heralds a promising era in the field of precision radiotherapy. Patients’ radiosensitivity, tumour radioresistance, and treatment uncertainties should be considered to enhance the efficacy and to ameliorate the selection of patients.

Advancements and future directions in positron emission tomography-guided radiotherapy: a narrative review.

Positron emission tomography (PET) imaging has been useful in delineating tumor volumes and allowing for improved radiation treatment. The field of PET-guided radiotherapy is rapidly growing and will have significant impact on radiotherapy delivery in the future. This narrative review provides an overview of the current state of PET-guided radiotherapy as well as the future directions of the field. For this narrative review, PubMed was searched for articles from 2010-2023. A total of 18 keywords or phrases were searched to provide an overview of PET-guided radiotherapy, radiotracers, the role of PET-guided radiotherapy in oligometastatic disease, and biology-guided radiotherapy (BgRT). The first 300 results for each keyword were searched and relevant articles were extracted. The references of these articles were also reviewed for relevant articles. In radiotherapy, 18F-2-fluoro-2-deoxy-D-glucose (F-FDG or FDG) is the major radiotracer for PET and when combined with computed tomography (CT) scan allows for anatomic visualization of metabolically active malignancy. Novel radiotracers are being explored to delineate certain cell types and numerous tumor metrics including metabolism, hypoxia, vascularity, and cellular proliferation. This molecular and functional imaging will provide improved tumor characterization. Through these radiotracers, radiation plans can employ dose painting by creating different dose levels based upon specific risk factors of the target volume. Additionally, biologic imaging during radiotherapy can allow for adaptation of the radiation plan based on response to treatment. Dose painting and adaptive radiotherapy should improve the therapeutic ratio through more selective dose delivery. The novel PET-linear accelerator hopes to combine these techniques and more by using radiotracers to deliver BgRT. The areas of radiotracer uptake will serve as fiducials to guide radiotherapy to themselves. This technique may prove promising in the growing area of oligometastatic radiation treatment. Significant challenges exist for the future of PET-guided radiotherapy. However, with the advancements being made, PET imaging is set to change the delivery of radiotherapy.