Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO).

INTRODUCTION: The advent of image-guided radiation therapy (IGRT) has recently changed the workflow of radiation treatments by ensuring highly collimated treatments. Artificial intelligence (AI) and radiomics are tools that have shown promising results for diagnosis, treatment optimization and outcome prediction. This review aims to assess the impact of AI and radiomics on modern IGRT modalities in RT. METHODS: A PubMed/MEDLINE and Embase systematic review was conducted to investigate the impact of radiomics and AI to modern IGRT modalities. The search strategy was "Radiomics" AND "Cone Beam Computed Tomography"; "Radiomics" AND "Magnetic Resonance guided Radiotherapy"; "Radiomics" AND "on board Magnetic Resonance Radiotherapy"; "Artificial Intelligence" AND "Cone Beam Computed Tomography"; "Artificial Intelligence" AND "Magnetic Resonance guided Radiotherapy"; "Artificial Intelligence" AND "on board Magnetic Resonance Radiotherapy" and only original articles up to 01.11.2022 were considered. RESULTS: A total of 402 studies were obtained using the previously mentioned search strategy on PubMed and Embase. The analysis was performed on a total of 84 papers obtained following the complete selection process. Radiomics application to IGRT was analyzed in 23 papers, while a total 61 papers were focused on the impact of AI on IGRT techniques. DISCUSSION: AI and radiomics seem to significantly impact IGRT in all the phases of RT workflow, even if the evidence in the literature is based on retrospective data. Further studies are needed to confirm these tools' potential and provide a stronger correlation with clinical outcomes and gold-standard treatment strategies.

Revolutionizing radiation therapy: the role of AI in clinical practice.

This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist's perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.

Nursing Care for Metastatic Bone Cancer: Trends for the Future.

To effectively treat patients and minimize viral exposure, oncology nurses and radiology departments during COVID-19 had to re-examine the ability to offer palliative radiation treatments to people with metastatic bone cancer. Decreasing potential exposure to the virus resulted in extra measures to keep patients and personnel safe. Limiting radiotherapy treatments, social distancing, and limiting caregivers were a few of the ways that oncology patients were impacted by the pandemic. Hypofractionated radiation therapy (HFRT), or the delivery of fewer higher-dose treatments, was a method of providing care but also limiting exposure to infection for immunocompromised patients as well as healthcare staff. As oncology radiation centers measure the impact of patient care during the pandemic, a trend toward HFRT may occur in treating the painful symptoms of bone cancer. In anticipation that HFRT may be increasingly used in patient treatment plans, oncology nurses should consider patient perspectives and outcomes from the pandemic to further determine how to manage future trends in giving personalized care, and supportive care.

Twenty Years of Advancements in a Radiotherapy Facility: Clinical Protocols, Technology, and Management.

BACKGROUND: Hypo-fractionation can be an effective strategy to lower costs and save time, increasing patient access to advanced radiation therapy. To demonstrate this potential in practice within the context of temporal evolution, a twenty-year analysis of a representative radiation therapy facility from 2003 to 2022 was conducted. This analysis utilized comprehensive data to quantitatively evaluate the connections between advanced clinical protocols and technological improvements. The findings provide valuable insights to the management team, helping them ensure the delivery of high-quality treatments in a sustainable manner. METHODS: Several parameters related to treatment technique, patient positioning, dose prescription, fractionation, equipment technology content, machine workload and throughput, therapy times and patients access counts were extracted from departmental database and analyzed on a yearly basis by means of linear regression. RESULTS: Patients increased by 121 ± 6 new per year (NPY). Since 2010, the incidence of hypo-fractionation protocols grew thanks to increasing Linac technology. In seven years, both the average number of fractions and daily machine workload decreased by -0.84 ± 0.12 fractions/year and -1.61 ± 0.35 patients/year, respectively. The implementation of advanced dose delivery techniques, image guidance and high dose rate beams for high fraction doses, currently systematically used, has increased the complexity and reduced daily treatment throughput since 2010 from 40 to 32 patients per 8 h work shift (WS8). Thanks to hypo-fractionation, such an efficiency drop did not affect NPY, estimating 693 ± 28 NPY/WS8, regardless of the evaluation time. Each newly installed machine was shown to add 540 NPY, while absorbing 0.78 ± 0.04 WS8. The COVID-19 pandemic brought an overall reduction of 3.7% of patients and a reduction of 0.8 fractions/patient, to mitigate patient crowding in the department. CONCLUSIONS: The evolution of therapy protocols towards hypo-fractionation was supported by the use of proper technology. The characteristics of this process were quantified considering time progression and organizational aspects. This strategy optimized resources while enabling broader access to advanced radiation therapy. To truly value the benefit of hypo-fractionation, a reimbursement policy should focus on the patient rather than individual treatment fractionation.

Histology Specific Molecular Biomarkers: Ushering in a New Era of Precision Radiation Oncology.

Histopathology and clinical staging have historically formed the backbone for allocation of treatment decisions in oncology. Although this has provided an extremely practical and fruitful approach for decades, it has long been evident that these data alone do not adequately capture the heterogeneity and breadth of disease trajectories experienced by patients. As efficient and affordable DNA and RNA sequencing have become available, the ability to provide precision therapy has become within grasp. This has been realized with systemic oncologic therapy, as targeted therapies have demonstrated immense promise for subsets of patients with oncogene-driver mutations. Further, several studies have evaluated predictive biomarkers for response to systemic therapy within a variety of malignancies. Within radiation oncology, the use of genomics/transcriptomics to guide the use, dose, and fractionation of radiation therapy is rapidly evolving but still in its infancy. The genomic adjusted radiation dose/radiation sensitivity index is one such early and exciting effort to provide genomically guided radiation dosing with a pan-cancer approach. In addition to this broad method, a histology specific approach to precision radiation therapy is also underway. Herein we review select literature surrounding the use of histology specific, molecular biomarkers to allow for precision radiotherapy with the greatest emphasis on commercially available and prospectively validated biomarkers.

The Promise and Future of Radiomics for Personalized Radiotherapy Dosing and Adaptation.

Quantitative image analysis, also known as radiomics, aims to analyze large-scale quantitative features extracted from acquired medical images using hand-crafted or machine-engineered feature extraction approaches. Radiomics has great potential for a variety of clinical applications in radiation oncology, an image-rich treatment modality that utilizes computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) for treatment planning, dose calculation, and image guidance. A promising application of radiomics is in predicting treatment outcomes after radiotherapy such as local control and treatment-related toxicity using features extracted from pretreatment and on-treatment images. Based on these individualized predictions of treatment outcomes, radiotherapy dose can be sculpted to meet the specific needs and preferences of each patient. Radiomics can aid in tumor characterization for personalized targeting, especially for identifying high-risk regions within a tumor that cannot be easily discerned based on size or intensity alone. Radiomics-based treatment response prediction can aid in developing personalized fractionation and dose adjustments. In order to make radiomics models more applicable across different institutions with varying scanners and patient populations, further efforts are needed to harmonize and standardize the acquisition protocols by minimizing uncertainties within the imaging data.

Treatment of Oligometastatic Non-Small Cell Lung Cancer: An ASTRO/ESTRO Clinical Practice Guideline.

PURPOSE: This joint guideline by American Society for Radiation Oncology (ASTRO) and the European Society for Radiotherapy and Oncology (ESTRO) was initiated to review evidence and provide recommendations regarding the use of local therapy in the management of extracranial oligometastatic non-small cell lung cancer (NSCLC). Local therapy is defined as the comprehensive treatment of all known cancer-primary tumor, regional nodal metastases, and metastases-with definitive intent. METHODS: ASTRO and ESTRO convened a task force to address 5 key questions focused on the use of local (radiation, surgery, other ablative methods) and systemic therapy in the management of oligometastatic NSCLC. The questions address clinical scenarios for using local therapy, sequencing and timing when integrating local with systemic therapies, radiation techniques critical for oligometastatic disease targeting and treatment delivery, and the role of local therapy for oligoprogression or recurrent disease. Recommendations were based on a systematic literature review and created using ASTRO guidelines methodology. RESULTS: Based on the lack of significant randomized phase 3 trials, a patient-centered, multidisciplinary approach was strongly recommended for all decision-making regarding potential treatment. Integration of definitive local therapy was only relevant if technically feasible and clinically safe to all disease sites, defined as 5 or fewer distinct sites. Conditional recommendations were given for definitive local therapies in synchronous, metachronous, oligopersistent, and oligoprogressive conditions for extracranial disease. Radiation and surgery were the only primary definitive local therapy modalities recommended for use in the management of patients with oligometastatic disease, with indications provided for choosing one over the other. Sequencing recommendations were provided for systemic and local therapy integration. Finally, multiple recommendations were provided for the optimal technical use of hypofractionated radiation or stereotactic body radiation therapy as definitive local therapy, including dose and fractionation. CONCLUSIONS: Presently, data regarding clinical benefits of local therapy on overall and other survival outcomes is still sparse for oligometastatic NSCLC. However, with rapidly evolving data being generated supporting local therapy in oligometastatic NSCLC, this guideline attempted to frame recommendations as a function of the quality of data available to make decisions in a multidisciplinary approach incorporating patient goals and tolerances.

Consensus Quality Measures and Dose Constraints for Lung Cancer From the Veterans Affairs Radiation Oncology Quality Surveillance Program and ASTRO Expert Panel.

PURPOSE: For patients with lung cancer, it is critical to provide evidence-based radiation therapy to ensure high-quality care. The US Department of Veterans Affairs (VA) National Radiation Oncology Program partnered with the American Society for Radiation Oncology (ASTRO) as part of the VA Radiation Oncology Quality Surveillance to develop lung cancer quality metrics and assess quality of care as a pilot program in 2016. This article presents recently updated consensus quality measures and dose-volume histogram (DVH) constraints. METHODS AND MATERIALS: A series of measures and performance standards were reviewed and developed by a Blue-Ribbon Panel of lung cancer experts in conjunction with ASTRO in 2022. As part of this initiative, quality, surveillance, and aspirational metrics were developed for (1) initial consultation and workup; (2) simulation, treatment planning, and treatment delivery; and (3) follow-up. The DVH metrics for target and organ-at-risk treatment planning dose constraints were also reviewed and defined. RESULTS: Altogether, a total of 19 lung cancer quality metrics were developed. There were 121 DVH constraints developed for various fractionation regimens, including ultrahypofractionated (1, 3, 4, or 5 fractions), hypofractionated (10 and 15 fractionations), and conventional fractionation (30-35 fractions). CONCLUSIONS: The devised measures will be implemented for quality surveillance for veterans both inside and outside of the VA system and will provide a resource for lung cancer-specific quality metrics. The recommended DVH constraints serve as a unique, comprehensive resource for evidence- and expert consensus-based constraints across multiple fractionation schemas.

Artificial Intelligence for Radiotherapy Auto-Contouring: Current Use, Perceptions of and Barriers to Implementation.

AIMS: Artificial intelligence has the potential to transform the radiotherapy workflow, resulting in improved quality, safety, accuracy and timeliness of radiotherapy delivery. Several commercially available artificial intelligence-based auto-contouring tools have emerged in recent years. Their clinical deployment raises important considerations for clinical oncologists, including quality assurance and validation, education, training and job planning. Despite this, there is little in the literature capturing the views of clinical oncologists with respect to these factors. MATERIALS AND METHODS: The Royal College of Radiologists realises the transformational impact artificial intelligence is set to have on our specialty and has appointed the Artificial Intelligence for Clinical Oncology working group. The aim of this work was to survey clinical oncologists with regards to perceptions, current use of and barriers to using artificial intelligence-based auto-contouring for radiotherapy. Here we share our findings with the wider clinical and radiation oncology communities. We hope to use these insights in developing support, guidance and educational resources for the deployment of auto-contouring for clinical use, to help develop the case for wider access to artificial intelligence-based auto-contouring across the UK and to share practice from early-adopters. RESULTS: In total, 78% of clinical oncologists surveyed felt that artificial intelligence would have a positive impact on radiotherapy. Attitudes to risk were more varied, but 49% felt that artificial intelligence will decrease risk for patients. There is a marked appetite for urgent guidance, education and training on the safe use of such tools in clinical practice. Furthermore, there is a concern that the adoption and implementation of such tools is not equitable, which risks exacerbating existing inequalities across the country. CONCLUSION: Careful coordination is required to ensure that all radiotherapy departments, and the patients they serve, may enjoy the benefits of artificial intelligence in radiotherapy. Professional organisations, such as the Royal College of Radiologists, have a key role to play in delivering this.

Quality and Safety Considerations in Intensity Modulated Radiation Therapy: An ASTRO Safety White Paper Update.

PURPOSE: This updated report on intensity modulated radiation therapy (IMRT) is part of a series of consensus-based white papers previously published by the American Society for Radiation Oncology (ASTRO) addressing patient safety. Since the first white papers were published, IMRT went from widespread use to now being the main delivery technique for many treatment sites. IMRT enables higher radiation doses to be delivered to more precise targets while minimizing the dose to uninvolved normal tissue. Due to the associated complexity, IMRT requires additional planning and safety checks before treatment begins and, therefore, quality and safety considerations for this technique remain important areas of focus. METHODS AND MATERIALS: ASTRO convened an interdisciplinary task force to assess the original IMRT white paper and update content where appropriate. Recommendations were created using a consensus-building methodology, and task force members indicated their level of agreement based on a 5-point Likert scale, from "strongly agree" to "strongly disagree." A prespecified threshold of ≥75% of raters who select "strongly agree" or "agree" indicated consensus. CONCLUSIONS: This IMRT white paper primarily focuses on quality and safety processes in planning and delivery. Building on the prior version, this consensus paper incorporates revised and new guidance documents and technology updates. IMRT requires an interdisciplinary team-based approach, staffed by appropriately trained individuals as well as significant personnel resources, specialized technology, and implementation time. A comprehensive quality assurance program must be developed, using established guidance, to ensure IMRT is performed in a safe and effective manner. Patient safety in the delivery of IMRT is everyone's responsibility, and professional organizations, regulators, vendors, and end-users must work together to ensure the highest levels of safety.

Consensus Quality Measures and Dose Constraints for Breast Cancer From the Veterans Affairs Radiation Oncology Quality Surveillance Program and American Society for Radiation Oncology Expert Panel.

PURPOSE: Using evidence-based radiation therapy to direct care for patients with breast cancer is critical to standardize practice, improve safety, and optimize outcomes. To address this need, the Veterans Affairs (VA) National Radiation Oncology Program (NROP) established the VA Radiation Oncology Quality Surveillance Program to develop clinical quality measures (QMs). The VA NROP contracted with the American Society for Radiation Oncology to commission 5 Blue Ribbon Panels for breast, lung, prostate, rectal, and head and neck cancers. METHODS AND MATERIALS: The Breast Cancer Blue Ribbon Panel experts worked collaboratively with the NROP to develop consensus QMs for use throughout the VA system, establishing a set of QMs for patients in several areas, including consultation and work-up; simulation, treatment planning, and treatment; and follow-up care. As part of this initiative, consensus dose-volume histogram (DVH) constraints were outlined. RESULTS: In total, 36 QMs were established. Herein, we review the process used to develop QMs and final consensus QMs pertaining to all aspects of radiation patient care, as well as DVH constraints. CONCLUSIONS: The QMs and expert consensus DVH constraints are intended for ongoing quality surveillance within the VA system and centers providing community care for Veterans. They are also available for use by greater non-VA community measures of quality care for patients with breast cancer receiving radiation.

Risk management patterns in radiation oncology-results of a national survey within the framework of the Patient Safety in German Radiation Oncology (PaSaGeRO) project.

PURPOSE: Risk management (RM) is a key component of patient safety in radiation oncology (RO). We investigated current approaches on RM in German RO within the framework of the Patient Safety in German Radiation Oncology (PaSaGeRO) project. Aim was not only to evaluate a status quo of RM purposes but furthermore to discover challenges for sustainable RM that should be addressed in future research and recommendations. METHODS: An online survey was conducted from June to August 2021, consisting of 18 items on prospective and reactive RM, protagonists of RM, and self-assessment concerning RM. The survey was designed using LimeSurvey and invitations were sent by e­mail. Answers were requested once per institution. RESULTS: In all, 48 completed questionnaires from university hospitals, general and non-academic hospitals, and private practices were received and considered for evaluation. Prospective and reactive RM was commonly conducted within interprofessional teams; 88% of all institutions performed prospective risk analyses. Most institutions (71%) reported incidents or near-events using multiple reporting systems. Results were presented to the team in 71% for prospective analyses and 85% for analyses of incidents. Risk conferences take place in 46% of institutions. 42% nominated a manager/committee for RM. Knowledge concerning RM was mostly rated "satisfying" (44%). However, 65% of all institutions require more information about RM by professional societies. CONCLUSION: Our results revealed heterogeneous patterns of RM in RO departments, although most departments adhered to common recommendations. Identified mismatches between recommendations and implementation of RM provide baseline data for future research and support definition of teaching content.

Bladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence.

Machine learning (ML) and artificial intelligence (AI) have demonstrated potential to improve the care of radiation oncology patients. Here we review recent advances applicable to the care of bladder cancer, with an eye towards studies that may suggest next steps in clinical implementation. Algorithms have been applied to clinical records, pathology, and radiology data to generate accurate predictive models for prognosis and clinical outcomes. AI has also shown increasing utility for auto-contouring and efficient creation of workflows involving multiple treatment plans. As technologies progress towards routine clinical use for bladder cancer patients, we also discuss emerging methods to improve interpretability and reliability of algorithms.

Artificial Intelligence for Outcome Modeling in Radiotherapy.

Outcome modeling plays an important role in personalizing radiotherapy and finds applications in specialized areas such as adaptive radiotherapy. Conventional outcome models that are based on a simplified understanding of radiobiological effects or empirical fitting often only consider dosimetric information. However, it is recognized that response to radiotherapy is multi-factorial and involves a complex interaction of radiation therapy, patient and treatment factors, and the tumor microenvironment. Recently, large pools of patient-specific biological and imaging data have become available with the development of advanced biotechnology and multi-modality imaging techniques. Given this complexity, artificial intelligence (AI) and machine learning (ML) are valuable to make sense of such a plethora of heterogeneous data and to aid clinicians in their decision-making process. The role of AI/ML has been demonstrated in many retrospective studies and more recently prospective evidence has been emerging as well to support AI/ML for personalized and precision radiotherapy.

Artificial Intelligence for Radiation Oncology Applications Using Public Datasets.

Artificial intelligence (AI) has exceptional potential to positively impact the field of radiation oncology. However, large curated datasets - often involving imaging data and corresponding annotations - are required to develop radiation oncology AI models. Importantly, the recent establishment of Findable, Accessible, Interoperable, Reusable (FAIR) principles for scientific data management have enabled an increasing number of radiation oncology related datasets to be disseminated through data repositories, thereby acting as a rich source of data for AI model building. This manuscript reviews the current and future state of radiation oncology data dissemination, with a particular emphasis on published imaging datasets, AI data challenges, and associated infrastructure. Moreover, we provide historical context of FAIR data dissemination protocols, difficulties in the current distribution of radiation oncology data, and recommendations regarding data dissemination for eventual utilization in AI models. Through FAIR principles and standardized approaches to data dissemination, radiation oncology AI research has nothing to lose and everything to gain.

Artificial intelligence in radiotherapy.

Radiotherapy is a discipline closely integrated with computer science. Artificial intelligence (AI) has developed rapidly over the past few years. With the explosive growth of medical big data, AI promises to revolutionize the field of radiotherapy through highly automated workflow, enhanced quality assurance, improved regional balances of expert experiences, and individualized treatment guided by multi-omics. In addition to independent researchers, the increasing number of large databases, biobanks, and open challenges significantly facilitated AI studies on radiation oncology. This article reviews the latest research, clinical applications, and challenges of AI in each part of radiotherapy including image processing, contouring, planning, quality assurance, motion management, and outcome prediction. By summarizing cutting-edge findings and challenges, we aim to inspire researchers to explore more future possibilities and accelerate the arrival of AI radiotherapy.

News in magnetic resonance imaging use for radiation oncology.

The purpose of this article is to give a summary of the progress of magnetic resonance imaging (MRI) in radiotherapy. MRI is an important imaging modality for treatment planning in radiotherapy. However, the registration step with the simulation scanner can be a source of errors, motivating the implementation of all-MRI simulation methods and new accelerators coupled with on-board MRI. First, practical MRI imaging for radiotherapy is detailed, but also the importance of a coherent imaging workflow incorporating all imaging modalities. Second, future evolutions and research domains such as quantitative imaging biomarkers, MRI-only pseudo computed tomography and radiomics are discussed. Finally, the application of MRI during radiotherapy treatment is reviewed: the use of MR-linear accelerators. MRI is increasingly integrated into radiotherapy. Advances in diagnostic imaging can thus benefit radiotherapy, but specific radiotherapy constraints lead to additional challenges and require close collaboration between radiologists, radiation oncologists, technologists and physicists. The integration of quantitative imaging biomarkers in the radiotherapy process will result in mutual benefit for diagnostic imaging and radiotherapy. MRI-guided radiotherapy has already been used for several years in clinical routine. Abdominopelvic neoplasias (pancreas, liver, prostate) are the preferred locations for treatment because of their favourable contrast in MRI, their movement during irradiation and their proximity to organs at risk of radiation exposure, making the tracking and daily adaptation of the plan essential. MRI has emerged as an increasingly necessary imaging modality for radiotherapy planning. Inclusion of patients in clinical trials evaluating new MRI-guided radiotherapy techniques and associated quantitative imaging biomarkers will be necessary to assess the benefits.

Current status and future developments in predicting outcomes in radiation oncology.

Advancements in data-driven technologies and the inclusion of information-rich multiomics features have significantly improved the performance of outcomes modeling in radiation oncology. For this current trend to be sustainable, challenges related to robust data modeling such as small sample size, low size to feature ratio, noisy data, as well as issues related to algorithmic modeling such as complexity, uncertainty, and interpretability, need to be mitigated if not resolved. Emerging computational technologies and new paradigms such as federated learning, human-in-the-loop, quantum computing, and novel interpretability methods show great potential in overcoming these challenges and bridging the gap towards precision outcome modeling in radiotherapy. Examples of these promising technologies will be presented and their potential role in improving outcome modeling will be discussed.

Rates of MRI simulator utilisation in a tertiary cancer therapy centre.

Magnetic resonance imaging (MRI) is increasingly being integrated into the radiation oncology workflow, due to its improved soft tissue contrast without additional exposure to ionising radiation. A review of MRI utilisation according to evidence based departmental guidelines was performed. Guideline utilisation rates were calculated to be 50% (true utilisation rate was 46%) of all new cancer patients treated with adjuvant or curative intent, excluding simple skin and breast cancer patients. Guideline utilisation rates were highest in the lower gastrointestinal and gynaecological subsites, with the lowest being in the upper gastrointestinal and thorax subsites. Head and neck (38% vs 45%) and CNS (46% vs 67%) cancers had the largest discrepancy between true and guideline utilisation rates due to unnamed reasons and non-contemporaneous diagnostic imaging respectively. This report outlines approximate MRI utilisation rates in a tertiary radiation oncology service and may help guide planning for future departments contemplating installation of an MRI simulator.

Planning evaluation of a novel volume-based algorithm for personalized optimization of lung dose in VMAT for esophageal cancer.

Radiotherapy treatment planning (RTP) is time-consuming and labor-intensive since medical physicists must devise treatment plans carefully to reduce damage to tissues and organs for patients. Previously, we proposed the volume-based algorithm (VBA) method, providing optimal partial arcs (OPA) angle to achieve the low-dose volume of lungs in dynamic arc radiotherapy. This study aimed to implement the VBA for esophageal cancer (EC) patients and compare the lung dose and delivery time between full arcs (FA) without using VBA and OPA angle using VBA in volumetric modulated arc therapy (VMAT) plans. We retrospectively included 30 patients diagnosed with EC. RTP of each patient was replanned to 4 VMAT plans, including FA plans without (FA-C) and with (FA + C) dose constraints of OARs and OPA plans without (OPA-C) and with (OPA + C) dose constraints of OARs. The prescribed dose was 45 Gy. The OARs included the lungs, heart, and spinal cord. The dose distribution, dose-volume histogram, monitor units (MUs), delivery time, and gamma passing rates were analyzed. The results showed that the lung V5 and V10 in OPA + C plans were significantly lower than in FA + C plans (p < 0.05). No significant differences were noted in planning target volume (PTV) coverage, lung V15, lung V20, mean lung dose, heart V30, heart V40, mean heart dose, and maximal spinal cord dose between FA + C and OPA + C plans. The delivery time was significantly longer in FA + C plans than in OPA + C plans (237 vs. 192 s, p < 0.05). There were no significant differences between FA + C and OPA + C plans in gamma passing rates. We successfully applied the OPA angle based on the VBA to clinical EC patients and simplified the arc angle selection in RTP. The VBA could provide a personalized OPA angle for each patient and effectively reduce lung V5, V10, and delivery time in VMAT.