Navigating the Critical Translational Questions for Implementing FLASH in the Clinic.

The "FLASH effect" is an increased therapeutic index, that is, reduced normal tissue toxicity for a given degree of anti-cancer efficacy, produced by ultra-rapid irradiation delivered on time scales orders of magnitude shorter than currently conventional in the clinic for the same doses. This phenomenon has been observed in numerous preclinical in vivo tumor and normal tissue models. While the underlying biological mechanism(s) remain to be elucidated, a path to clinical implementation of FLASH can be paved by addressing several critical translational questions. Technological questions pertinent to each beam type (eg, electron, proton, photon) also dictate the logical progression of experimentation required to move forward in safe and decisive clinical trials. Here we review the available preclinical data pertaining to these questions and how they may inform strategies for FLASH cancer therapy clinical trials.

Artificial intelligence solution to accelerate the acquisition of MRI images: Impact on the therapeutic care in oncology in radiology and radiotherapy departments.

PURPOSE: MRI is essential in the management of brain tumours. However, long waiting times reduce patient accessibility. Reducing acquisition time could improve access but at the cost of spatial resolution and diagnostic quality. A commercially available artificial intelligence (AI) solution, SubtleMR™, can increase the resolution of acquired images. The objective of this prospective study was to evaluate the impact of this algorithm that halves the acquisition time on the detectability of brain lesions in radiology and radiotherapy. MATERIAL AND METHODS: The T1/T2 MRI of 33 patients with brain metastases or meningiomas were analysed. Images acquired quickly have a matrix divided by two which halves the acquisition time. The visual quality and lesion detectability of the AI images were evaluated by radiologists and radiation oncologist as well as pixel intensity and lesions size. RESULTS: The subjective quality of the image is lower for the AI images compared to the reference images. However, the analysis of lesion detectability shows a specificity of 1 and a sensitivity of 0.92 and 0.77 for radiology and radiotherapy respectively. Undetected lesions on the IA image are lesions with a diameter less than 4mm and statistically low average gadolinium-enhancement contrast. CONCLUSION: It is possible to reduce MRI acquisition times by half using the commercial algorithm to restore the characteristics of the image and obtain good specificity and sensitivity for lesions with a diameter greater than 4mm.

Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation.

PURPOSE: The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived segmentations are poorly understood; our study aims to quantify these factors. METHODS: Organ at risk (OAR) and tumor-related segmentations provided by radiation oncologists from the Contouring Collaborative for Consensus in Radiation Oncology data set were used. Segmentations were derived from five disease sites: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and GI. Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus, which served as a reference standard benchmark. The Dice similarity coefficient (DSC) was primarily used as a metric for the comparisons. DSC was stratified into binary groups on the basis of structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Bayesian estimation were used to investigate the association between demographic variables and the binarized DSC for each disease site. Variables with a highest density interval excluding zero were considered to substantially affect the outcome measure. RESULTS: Five hundred seventy-four, 110, 452, 112, and 48 segmentations were used for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of segmentations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumors, respectively. Regression analysis revealed that the structure being tumor-related had a substantial negative impact on binarized DSC for the breast, sarcoma, H&N, and GI cases. There were no recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations. CONCLUSION: Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality relative to benchmarks.

Which Modality of SFRT Should be Considered First for Bulky Tumor Radiation Therapy, GRID or LATTICE?

Spatially fractionated radiation therapy (SFRT), also known as the GRID and LATTICE radiotherapy (GRT, LRT), the concept of treating tumors by delivering a spatially modulated dose with highly non-uniform dose distributions, is a treatment modality of growing interest in radiation oncology, physics, and radiation biology. Clinical experience in SFRT has suggested that GRID and LATTICE therapy can achieve a high response and low toxicity in the treatment of refractory and bulky tumors. Limited initially to GRID therapy using block collimators, advanced, and versatile multi-leaf collimators, volumetric modulated arc technologies and particle therapy have since increased the capabilities and individualization of SFRT and expanded the clinical investigation of SFRT to various dosing regimens, multiple malignancies, tumor types and sites. As a 3D modulation approach outgrown from traditional 2D GRID, LATTICE therapy aims to reconfigure the traditional SFRT as spatial modulation of the radiation is confined solely to the tumor volume. The distinctively different beam geometries used in LATTICE therapy have led to appreciable variations in dose-volume distributions, compared to GRID therapy. The clinical relevance of the variations in dose-volume distribution between LATTICE and traditional GRID therapies is a crucial factor in determining their adoption in clinical practice. In this Point-Counterpoint contribution, the authors debate the pros and cons of GRID and LATTICE therapy. Both modalities have been used in clinics and their applicability and optimal use have been discussed in this article.

Autologous Peripheral Blood Stem Cell Transplantation and Timing of Local Radiation Therapy in Patients With Malignant Lymphoma: Results of the Japanese Radiation Oncology Study Group Questionnaire Survey.

BACKGROUND/AIM: Malignant lymphoma (ML) including Hodgkin's lymphoma and non-Hodgkin's lymphoma is often treated with local radiation therapy (RT) in combination with autologous hematopoietic stem cell transplantation (ASCT) to prevent relapse; however, the efficacy and optimal timing of this approach is unclear. In this study, a national survey conducted by the Japanese Radiation Oncology Study Group reviewed ML cases from 2011 to 2019 to determine whether RT should be added to ASCT, focusing on the use of autologous peripheral blood stem cell transplantation (auto-PBSCT), a predominant form of ASCT. PATIENTS AND METHODS: The survey encompassed 92 patients from 11 institutes, and assessed histological ML types, treatment regimens, timing of RT relative to auto-PBSCT, and associated adverse events. RESULTS: The results indicated no significant differences in adverse events, including myelosuppression, based on the timing of RT in relation to auto-PBSCT. However, anemia was more prevalent when RT was administered before auto-PBSCT, and there was a higher incidence of neutropenia recovery delay in patients receiving RT after auto-PBSCT. CONCLUSION: This study provides valuable insights into the variable practices of auto-PBSCT and local RT in ML treatment, emphasizing the need for optimized timing of these therapies to improve patient outcomes and reduce complications.

Deformable registration of magnetic resonance images using unsupervised deep learning in neuro-/radiation oncology.

PURPOSE: Accurate deformable registration of magnetic resonance imaging (MRI) scans containing pathologies is challenging due to changes in tissue appearance. In this paper, we developed a novel automated three-dimensional (3D) convolutional U-Net based deformable image registration (ConvUNet-DIR) method using unsupervised learning to establish correspondence between baseline pre-operative and follow-up MRI scans of patients with brain glioma. METHODS: This study involved multi-parametric brain MRI scans (T1, T1-contrast enhanced, T2, FLAIR) acquired at pre-operative and follow-up time for 160 patients diagnosed with glioma, representing the BraTS-Reg 2022 challenge dataset. ConvUNet-DIR, a deep learning-based deformable registration workflow using 3D U-Net style architecture as a core, was developed to establish correspondence between the MRI scans. The workflow consists of three components: (1) the U-Net learns features from pairs of MRI scans and estimates a mapping between them, (2) the grid generator computes the sampling grid based on the derived transformation parameters, and (3) the spatial transformation layer generates a warped image by applying the sampling operation using interpolation. A similarity measure was used as a loss function for the network with a regularization parameter limiting the deformation. The model was trained via unsupervised learning using pairs of MRI scans on a training data set (n = 102) and validated on a validation data set (n = 26) to assess its generalizability. Its performance was evaluated on a test set (n = 32) by computing the Dice score and structural similarity index (SSIM) quantitative metrics. The model's performance also was compared with the baseline state-of-the-art VoxelMorph (VM1 and VM2) learning-based algorithms. RESULTS: The ConvUNet-DIR model showed promising competency in performing accurate 3D deformable registration. It achieved a mean Dice score of 0.975 ± 0.003 and SSIM of 0.908 ± 0.011 on the test set (n = 32). Experimental results also demonstrated that ConvUNet-DIR outperformed the VoxelMorph algorithms concerning Dice (VM1: 0.969 ± 0.006 and VM2: 0.957 ± 0.008) and SSIM (VM1: 0.893 ± 0.012 and VM2: 0.857 ± 0.017) metrics. The time required to perform a registration for a pair of MRI scans is about 1 s on the CPU. CONCLUSIONS: The developed deep learning-based model can perform an end-to-end deformable registration of a pair of 3D MRI scans for glioma patients without human intervention. The model could provide accurate, efficient, and robust deformable registration without needing pre-alignment and labeling. It outperformed the state-of-the-art VoxelMorph learning-based deformable registration algorithms and other supervised/unsupervised deep learning-based methods reported in the literature.

Radiomics for clinical decision support in radiation oncology.

Radiomics is a promising tool for the development of quantitative biomarkers to support clinical decision-making. It has been shown to improve the prediction of response to treatment and outcome in different settings, particularly in the field of radiation oncology by optimising the dose delivery solutions and reducing the rate of radiation-induced side effects, leading to a fully personalised approach. Despite the promising results offered by radiomics at each of these stages, standardised methodologies, reproducibility and interpretability of results are still lacking, limiting the potential clinical impact of these tools. In this review, we briefly describe the principles of radiomics and the most relevant applications of radiomics at each stage of cancer management in the framework of radiation oncology. Furthermore, the integration of radiomics into clinical decision support systems is analysed, defining the challenges and offering possible solutions for translating radiomics into a clinically applicable tool.

Optimizing Outpatient Radiation Oncology Consult Workflow by Using Time-Driven Activity-Based Costing: Efficiency and Financial Impacts.

PURPOSE: Clinical efficiency is a key component of value-based health care. Our objective here was to identify workflow inefficiencies by using time-driven activity-based costing (TDABC) and evaluate the implementation of a new clinical workflow in high-volume outpatient radiation oncology clinics. METHODS: Our quality improvement study was conducted with the Departments of GI, Genitourinary (GU), and Thoracic Radiation Oncology at a large academic cancer center and four community network sites. TDABC was used to create process maps and optimize workflow for outpatient consults. Patient encounter metrics were captured with a real-time status function in the electronic medical record. Time metrics were compared using Mann-Whitney U tests. RESULTS: Individual patient encounter data for 1,328 consults before the intervention and 1,234 afterward across all sections were included. The median overall cycle time was reduced by 21% in GI (19 minutes), 18% in GU (16 minutes), and 12% at the community sites (9 minutes). The median financial savings per consult were $52 in US dollars (USD) for the GI, $33 USD for GU, $30 USD for thoracic, and $42 USD for the community sites. Patient satisfaction surveys (from 127 of 228 patients) showed that 99% of patients reported that their providers spent adequate time with them and 91% reported being seen by a care provider in a timely manner. CONCLUSION: TDABC can effectively identify opportunities to improve clinical efficiency. Implementing workflow changes on the basis of our findings led to substantial reductions in overall encounter cycle times across several departments, as well as high patient satisfaction and significant financial savings.

Systematic Implementation of Effective Quality Assurance Processes for the Assessment of Radiation Target Volumes in Head and Neck Cancer.

PURPOSE: Significant heterogeneity exists in clinical quality assurance (QA) practices within radiation oncology departments, with most chart rounds lacking prospective peer-reviewed contour evaluation. This has the potential to significantly affect patient outcomes, particularly for head and neck cancers (HNC) given the large variance in target volume delineation. With this understanding, we incorporated a prospective systematic peer contour-review process into our workflow for all patients with HNC. This study aims to assess the effectiveness of implementing prospective peer review into practice for our National Cancer Institute Designated Cancer Center and to report factors associated with contour modifications. METHODS AND MATERIALS: Starting in November 2020, our department adopted a systematic QA process with real-time metrics, in which contours for all patients with HNC treated with radiation therapy were prospectively peer reviewed and graded. Contours were graded with green (unnecessary), yellow (minor), or red (major) colors based on the degree of peer-recommended modifications. Contours from November 2020 through September 2021 were included for analysis. RESULTS: Three hundred sixty contours were included. Contour grades were made up of 89.7% green, 8.9% yellow, and 1.4% red grades. Physicians with >12 months of clinical experience were less likely to have contour changes requested than those with <12 months (8.3% vs 40.9%; P < .001). Contour grades were significantly associated with physician case load, with physicians presenting more than the median number of 50 cases having significantly less modifications requested than those presenting <50 (6.7% vs 13.3%; P = .013). Physicians working with a resident or fellow were less likely to have contour changes requested than those without a trainee (5.2% vs 12.6%; P = .039). Frequency of major modification requests significantly decreased over time after adoption of prospective peer contour review, with no red grades occurring >6 months after adoption. CONCLUSIONS: This study highlights the importance of prospective peer contour-review implementation into systematic clinical QA processes for HNC. Physician experience proved to be the highest predictor of approved contours. A growth curve was demonstrated, with major modifications declining after prospective contour review implementation. Even within a high-volume academic practice with subspecialist attendings, >10% of patients had contour changes made as a direct result of prospective peer review.

Practical Implications of the American Society for Radiation Oncology Radiation Oncology Case Rate Program Proposal for Radiation Oncologists.

The American Society for Radiation Oncology has proposed the Radiation Oncology Case Rate Program (ROCR) to advocate for fair reimbursement for radiation oncologists. ROCR would replace Medicare fee-for-service with a case rate payment for each of the 15 most common cancer types treated with external beam or stereotactic radiation therapy. This topic discussion attempts to provide a concise overview of the practical implications for radiation oncologists should the ROCR payment program be legislated by Congress and subsequently implemented by the Center for Medicare and Medicaid Services. This topic discussion covers the practical changes to billing and reimbursement, the Health Equity Achievement in Radiation Therapy payment, the Quality of Care requirement, and the available tool to calculate the effect of the ROCR based on an individual practice's case mix.

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.

An Automated, Dynamic Radiation Oncology Prescription Checking System.

PURPOSE: Despite serving as a critical communication tool, radiation oncology prescriptions are entered manually and prone to error. An automated prescription checking system was developed and implemented to help address this problem. METHODS AND MATERIALS: Rules defining clinically appropriate prescriptions were generated, examining specific types of errors: (1) unapproved dose per fraction for a given disease site; (2) dose per fraction too large for nonstereotactic treatment technique; and (3) dose per fraction too low. With a goal of catching errors as upstream as possible to minimize their propagation, a report was created and ran every 30 minutes to check all newly written or approved prescriptions against the 3 rules. When a prescription violated these rules, an automated email was immediately sent to the prescriber alerting them of the potential error. System performance was continuously monitored and the criteria triggering an alert adjusted to balance error detection against false positives. Alerts leading to prescription amendment were considered true errors. RESULTS: From June 2021 to November 2022, the system checked 24,047 prescriptions. A total of 241 email alerts were triggered, for an average alert rate of 1%. Of the 241 alerts, 198 (82.2%) were unapproved doses per fraction for the disease site, 14 (5.8%) were doses per fraction that were too low, and 29 (12%) were doses too large for nonstereotactic treatment technique. Thirty-one percent of alerts led to a change of prescription, suggesting they were true errors. The baseline rate of erroneous prescription entry was 0.3%. A regression model showed that trainee prescription entry and dose per fraction <150 cGy were significantly associated with true errors. CONCLUSIONS: Given the significant consequences of erroneous prescription entry, which ranged from wasted resources and treatment delays to potentially serious misadministration, there is significant value in implementing automated prescription checking systems in radiation oncology clinics.

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.