Embracing the Future of Clinical Trials in Radiation Therapy: An NRG Oncology CIRO Technology Retreat Whitepaper on Pioneering Technologies and AI-Driven Solutions.

Embracing the Future of Clinical Trials in Radiation Therapy: An NRG Oncology CIRO Technology Retreat Whitepaper on Pioneering Technologies and AI-Driven Solutions - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Int J Radiat Oncol Biol Phys . Author manuscript; available in PMC: 2026 Jun 1. Published in final edited form as: Int J Radiat Oncol Biol Phys. 2025 Jan 22;122(2):443–457. doi: 10.1016/j.ijrobp.2025.01.006 Embracing the Future of Clinical Trials in Radiation Therapy: An NRG Oncology CIRO Technology Retreat Whitepaper on Pioneering Technologies and AI-Driven Solutions Ying Xiao a , Stanley Benedict b , Yunfeng Cui c , Carri Glide-Hurst d , Stephen Graves e , Xun Jia f , Stephen F Kry g , Heng Li f , Liyong Lin h , Martha Matuszak i , Mark Newpower j , Harald Paganetti k , X Sharon Qi l , Emilie Roncali m , Yi Rong n , George Sgouros f , Charles B Simone o , John J Sunderland e , Paige A Taylor g , Leila Tchelebi p , Michael Weldon q , Jennifer W Zou a , Evan J Wuthrick r , Mitchell Machtay s , Quynh-Thu Le t , Jeffrey C Buchsbaum u Author information Article notes Copyright and License information a Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania b Department of Radiation Oncology, University of California at Davis, Comprehensive Cancer Center, Davis, California c Department of Radiation Oncology, Duke University, Durham, North Carolina d Department of Human Oncology, University of Wisconsin, Madison, Wisconsin e Department of Radiology, Division of Nuclear Medicine, University of Iowa, Iowa City, Iowa f Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland g Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas h Department of Radiation Oncology, Emory University, Atlanta, Georgia i Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan j Department of Radiation Oncology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma k Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts l Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California m Department of Radiology, University of California at Davis, Davis, California n Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota o New York Proton Center, New York, New York p Department of Radiation Oncology, Northwell Health, Mt. Kisco, New York q Department of Radiation Oncology, The Ohio State University Medical Center, Columbus, Ohio r Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida s Department of Radiation Oncology, Penn State Health Milton S. Hershey Medical Center, Hershey, Pennsylvania t Department of Radiation Oncology, Stanford University, Stanford, California u Division of Cancer Treatment and Diagnosis, Radiation Research Program, National Cancer Institute, Bethesda, Maryland ✉ Corresponding author: Jeffrey C. Buchsbaum, MD, PhD; jeff.buchsbaum@nih.gov Issue date 2025 Jun 1. PMC Copyright notice PMCID: PMC12086052  NIHMSID: NIHMS2058261  PMID: 39848295 The publisher's version of this article is available at Int J Radiat Oncol Biol Phys This article has been corrected. See Int J Radiat Oncol Biol Phys. 2026 Feb 1;124(2):560 . Abstract This white paper examines the potential of pioneering technologies and artificial intelligence-driven solutions in advancing clinical trials involving radiation therapy. As the field of radiation therapy evolves, the integration of cutting-edge approaches such as radiopharmaceutical dosimetry, FLASH radiation therapy, image guided radiation therapy, and artificial intelligence promises to improve treatment planning, patient care, and outcomes. Additionally, recent advancements in quantum science, linear energy transfer/relative biological effect, and the combination of radiation therapy and immunotherapy create new avenues for innovation in clinical trials. The paper aims to provide an overview of these emerging technologies and discuss their challenges and opportunities in shaping the future of radiation oncology clinical trials. By synthesizing knowledge from experts across various disciplines, this white paper aims to present a foundation for the successful integration of these innovations into radiation therapy research and practice, ultimately enhancing patient outcomes and revolutionizing cancer care. Introduction As the global fight against cancer continues, the urgent quest for innovative and effective treatments intensifies. Radiation therapy, a cornerstone of cancer care, has seen immense advancements, yet the path to innovation faces challenges and opportunities. Pioneering technologies and artificial intelligence (AI)-driven solutions offer hope in this complex landscape. In January 2023, the NRG Oncology Center for Innovation in Radiation Oncology (CIRO) held a workshop on strategies for emerging clinical trials, focusing on current and novel radiation therapy technologies. The intent was to enable optimized technical reporting to support diverse trials, ushering in technological integration. The wide applications of radiation therapy have proved pivotal in treating cancers, but continuous innovation is needed to further advance treatment planning, patient care, and, ultimately, outcomes. CIRO’s initiative epitomizes this urgent progress quest. The workshop focused on approaches like radiopharmaceutical dosimetry, FLASH, and image guided radiation therapy (IGRT). Furthermore, integrating AI promises transformative impacts. Beyond AI and techniques, developments in quantum science, digital twins (DTs), linear energy transfer (LET)/relative biological effectiveness (RBE), and radiation therapy/immunotherapy combinations are opening new avenues for innovation. Although the potential is enormous, inherent challenges exist around developing robust physics and quality assurance (QA) protocols to ensure safe and effective delivery. In particular, parallel to the development of technology, there will be a need for continued global dedication to the importance of maintaining applicability to all of society in a just fashion so that all patients have equal, fair access to these critical new technologies. Finally, these technologies need to optimize costs, given our limited resources. NRG Oncology, with CIRO, leads the charge to improve care through research, fostering collaboration, promoting innovative radiation therapy trials, and ensuring harmonization—thereby addressing challenges. 1 This paper provides an overview of key emerging radiation therapy technologies anticipated for near-term trials, presenting an overview of these technologies, challenges, and implications of their successful integration into research and practice. With NRG Oncology’s commitment to enhancing lives through research and CIRO’s collaboration, it serves as a communication tool to articulate technology focuses for revolutionizing care and marking a critical innovation step for radiation therapy trials. Ultra-High-Dose Rate Radiation Therapy FLASH effect background FLASH effects: FLASH radiation therapy, delivering radiation at ultra-high dose rates (UHDR) much faster than current standards, has shown promise in preclinical studies for reducing normal tissue toxicity while effectively controlling tumors. 2 – 14 This approach, involving electron, proton, photon, or heavy ion beams, is being studied by multidisciplinary teams to understand its radiobiology and potential clinical application. Dose and dose rate thresholds: Favaudon et al indicated a threshold of mean dose rate of 40 Gy/s where the FLASH effect was observed in mice lung irradiation with pulsed electron. Further studies pointed out that the instantaneous dose rate and pulse frequency were also critical in achieving the FLASH effects. 6 , 7 Multiple definitions of the dose rates were proposed for proton beams, especially when scanning pencil beams and/or multiple fields are used. 15 – 19 A consensus on a relevant dose rate and its threshold for FLASH therapy is pending future clinical trials. Another key aspect is the dose threshold for tissue sparing, where FLASH preserves the sigmoidal dose-toxicity trend but with a shifted curve, 20 suggesting a correlation between increased sparing and doses above 10 Gy. 21 This threshold is expected to apply to human patients, although its exact value needs determination. Various radiation accelerator and delivery system designs present drastically different UHDR beam structures and delivery techniques. 22 It is recommended that a set of delivery parameters and voxelated dose and its temporal sequence be collected for FLASH clinical trials. 23 Fractionation schemes: potential FLASH dose threshold prompted the proposal for a hypo-fractionated regimen in FLASH radiation therapy. FAST-01, 24 FAST-02, and IMPulse clinical trial ( NCT04986696 ) employs single fraction. Multiple preclinical studies investigated various fractionation regimes. 6 – 8 The concept of biological effective dose 25 was employed for fractionation scheme design. Disease site and tissue type dependencies: the on-set and extent of the FLASH effect in patients likely are tissue and organ-dependent. 20 , 21 In patient clinical trials, disease site-specific and organ-specific dose and dose rate distribution need to be collected and reported. Radiobiology mechanisms: the FLASH radiobiology mechanisms remain to be elucidated. One of the hypotheses is that the FLASH sparing effect is due in part to the changes in tissue oxygen level during irradiation. 26 , 27 The radiochemical process where the radiolytic products and reactive oxygen species accumulate after irradiation is another explanation for FLASH sparing effect. 13 , 28 Enhanced immune responses with biomarkers including TGF- β and T-lymphocyte were also observed in UHDR irradiations. 2 , 6 , 10 , 29 , 30 Long-term effects: long-term FLASH effects in protocol-specific fractionated dose regimes should be carefully thought out in FLASH clinical trials. FLASH clinical trial considerations and path forward The National Cancer Institute (NCI), through its National Clinical Trial Network, facilitates large Phase II/III FLASH clinical trials, with the Imaging and Radiation Oncology Core (IROC) verifying that participating institutions meet essential performance and consistency standards. IROC and the field are focused on developing new, robust, valid methods that will be critical to FLASH machines’ Food and Drug Administration approval, consistent UHDR achievement, precise dose delivery, and necessary safety equipment. The credentialing involves evaluating machine capabilities, disease-specific treatment, and motion management. This may require new phantoms or algorithms for dose calculation. Treatment planning review includes anatomical contours, dose volume for targets and organs at risk, and the planned time structure of beam delivery. A pretreatment review is recommended to incorporate feedback and minimize protocol deviations. Challenges include defining a specific dose rate because of variability in treatment modalities, machine design, and patient anatomy. A comprehensive spatial-temporal dose distribution is crucial for correlating delivery parameters with outcomes. UHDR dosimetry requires enhanced temporal recording and fidelity. Safety measures are vital for such rapid dose delivery, with protocols like FAST-01 ensuring treatment continuity even after interlocks or dose rate reductions. FLASH plans need voxelated dose rate calculations and treatment planning systems adapted for modality-specific beam configurations and patient-specific devices. Site QA procedures must evolve to encompass UHDR delivery, and patient setup and image guidance need refinement for accurate UHDR targeting. 23 Proton FLASH therapy requires optimizing and validating LET and dose rate, unlike low-LET transmission proton and electron FLASH. Integrated Physical Optimization (IPO) Intensity Modulated Proton Therapy (IMPT) 31 consider dose rate and LET’s effects, employing computational techniques, Simultaneous Intensity Energy Modulation and Compensation solution, 32 for dose, dose rate, and LET optimization, shown in Figure 1 . Current planning systems do not account for dose rate and LET. Quantum physics parameters, such as particle interaction cross-sections, affect microscale radiation transport, whose inaccuracy can lead to disagreement between measurement and calculated LET in IPO-IMPT. Therefore IPO-IMPT needs more rigorous validation and QA than conventional IMPT, involving novel detectors and Monte Carlo simulations, to ensure accurate characterization and optimization of dose rate and LET for effective treatment delivery. 32 Fig. 1. Open in a new tab Linking LET and FLASH to plan optimization. Preliminary SIEMAC solution of IPO-IMPT. (A) and (B) Maps of spot weight, energy compensation bar length, and energy modulation pin length. (C)-(E) Dose, dose rate, and LET distributions and volume histograms for IMPT and IPO-IMPT. Adapted from SIEMAC solution for IPO-IMPT. 47 Abbreviations: IMPT = intensity modulated proton therapy; IPO = Integrated Physical Optimization; LET = linear energy transfer; SIEMAC = Simultaneous Intensity Energy Modulation and Compensation. Radiopharmaceutical Dosimetry Definitions and concepts Radiopharmaceutical therapy (RPT) aims to deliver targeted radionuclides to concentrate radiation in cancer cells while minimizing off-target cellular exposure. Precision RPT requires quantitative imaging and dosimetry to optimize tumor irradiation and minimize normal organ exposure. 33 Key concepts in RPT dosimetry include reconstructing time-activity curves to determine the radioactivity in organs over time by quantitative imaging at multiple time points post-injection. The magnitude of radioactive decay occurring in each organ, known as the cumulated activity, is calculated by integrating these time-activity curves. The cumulated activity is then converted to the radiation energy deposited per unit mass (Gy) in each organ, called the absorbed dose, using radionuclide-specific S-values. For high-LET radiation like alpha particles, an additional factor called the RBE accounts for the increased biological effect per Gy compared with low-LET radiation. 34 Implications for RPT clinical trials Rigorous RPT dosimetry is critical for correlating delivered radiation doses with observed tumor responses and normal tissue toxicities. 33 These dose-response data from clinical trials are essential for developing personalized, evidence-based treatment protocols. However, implementing dosimetry in multicenter RPT trials involves several challenges. Standardizing imaging protocols, single-photon emission computed tomography (SPECT)/positron emission tomography (PET) scanners, and administered activities across sites is necessary to enable quantitative comparisons. Each imaging system must be carefully calibrated with standardized phantoms to convert counts to absolute activity. The patient-administered radiopharmaceutical activity, which serves as the input for dosimetry calculations, must be accurately determined. Compliance with intensive imaging protocols requiring multiple scans over many days can be difficult for trial sites and patients. 35 Alpha-particle RPT warrants especially meticulous dosimetry because of the high potency and short range of alpha particles. 34 , 36 – 41 Although enabling therapeutic efficacy at low activities, these characteristics also raise risks of toxicity from exposure of organs at risk to higher than expected doses or from tumor progression because of inadequate dose delivery to tumor. Alpha particles have an LET approximately 200 times higher than beta particles or external beam radiation, resulting in a 3- to 8-fold increased RBE per absorbed dose. Their limited travel range confines their potent damaging effect to the immediate area around targeted cancer cells or clusters. 34 Early clinical trials beyond phase 1 show that alpha-emitting RPT significantly enhances survival in advanced, chemotherapy-resistant solid tumors, and hematological malignancies. 42 – 44 Alpha particles’ high radiobiological effectiveness enables therapeutic efficacy at low activities (2–8 MBq or 50–200 μ Ci per treatment cycle), complicating quantitative imaging-based dosimetry compared with beta emitter RPT. Nevertheless, advanced SPECT reconstruction methods using multiple energy windows allow tracking of both long-lived parent and short-lived daughter alpha-emitting radionuclides. 45 – 47 Ongoing improvements in SPECT technologies, particularly cadmium zinc telluride (CZT)-based scanners, may enable routine quantitative alpha SPECT imaging in clinical settings. 48 Although macroscopic organ-level pharmacokinetics are measured by clinical nuclear imaging, the ultrashort particle range requires accounting for micro-distribution within organs through microdosimetry calculations. 49 Pathways forward The NCI’s National Clinical Trial Network can facilitate rigorous implementation of RPT dosimetry in large multicenter trials. Dosimetry credentialing criteria for participating institutions could be established, potentially through the IROC. Image acquisition, reconstruction, phantoms, and analysis protocols should be standardized to maximize quantitative accuracy and reproducibility across sites. Advancing SPECT technologies, such as multiple energy window methods 45 – 47 and CZT detectors, 48 can improve quantitation of low-activity alpha emitters. Phantoms and procedures for site-specific QA of RPT dosimetry need to be developed. Patient-specific factors like radionuclide carrier pharmacokinetics should be incorporated into dosimetry calculations. Modeling microscale dose distributions in tumors and normal tissues using preclinical data and computational techniques is important for understanding the biological effects of heterogeneous alpha RPT distributions. 49 Delivered tumor and normal organ doses, spatial distributions, and radiobiological factors must be comprehensively reported and correlated with outcomes. Progress in quantitative SPECT imaging, voxel-level dosimetry calculations leveraging micro-distribution data, and rigorous clinical implementation of these tools promise to unlock the full theranostic potential of alpha-particle therapy for personalized precision cancer care. Proton Therapy and RBE Historical context and current understanding The RBE value of 1.1 has been widely used in proton therapy since the 1970s, based on averaged results from in vitro and in vivo experiments. This value was chosen to maintain tumor control similar to photon therapy, despite some evidence suggesting higher RBE values. 50 However, recent research indicates that proton RBE likely depends on various biological and physical factors, especially LET. 51 , 52 Clinical studies have revealed differences in dose-response relationships between protons and photons, particularly for pediatric brain injury. 53 These findings suggest that proton RBE in normal tissues may exceed 1.1, with one study estimating an RBE up to 3 for lung fibrosis. 54 Comparisons of proton and photon therapies have shown mixed toxicity results, with some studies reporting increased survival or lower toxicity with protons, 55 attributed to lower integral proton dose. However, the toxicity advantages depend on the dose distribution and RBE. 56 Regarding late effects, protons have demonstrated either comparable 57 or reduced 58 , 59 secondary malignancy rates compared with photons. Implications for treatment planning and optimization To address variable RBE in treatment planning, proton-specific dose constraints have been proposed based on pediatric brain data. 60 Although no accepted LET constraints currently exist, developing these could help elucidate associations between LET and toxicity. 61 LET guided optimization may enhance proton therapy by lowering LET in organs at risk, 62 – 64 and track-end optimization can optimize proton range. 65 Despite recognizing the potential benefits of LET optimization, current RBE models remain inadequate for clinical use because of their reliance on in vitro data when whole-system data are what are arguably what RBE modeling requires. 66 Ongoing clinical trials evaluating variable RBE models and associated outcomes will provide crucial patient data to improve these models. Treatment plan reporting should include dose, LET, and ideally proton fluence to enable retrospective analyses of LET and RBE effects. Clinical trial implications and future endeavors Ultimately, large multi-institutional datasets will be necessary to delineate the effects of proton RBE on normal tissues. More preclinical work is needed to analyze LET effects on normal tissues at all dose rates. Trials then that compare variable LET-weighted planning and delivery capacity are needed to evaluate translation of the findings in the model systems to human. Although variable RBE merits continued investigation, its clinical use is premature given the uncertainties in current modeling approaches. 50 Until RBE models sufficiently mature, LET analyses in treatment planning and research should continue to advance our understanding of RBE variations and their associations with toxicity. This is further developed in the recent NRG Working Group white paper on this subject. 66 The field needs these data to be the core of a new, larger database looking at organ-specific predicted toxicity based on, ultimately, a patient’s unique biology. This database would be similar, albeit much larger, to the magnetic resonance imaging (MRI)-LINAC Momentum database. 67 Radiation Therapy and Immunotherapy This section encompasses 2 closely interconnected domains pertaining to radiation therapy and immunotherapy. The first domain concentrates on spatially fractionated radiation therapy (SFRT), whereas the second domain delves into the synergistic combination of radiation therapy and immunotherapy. Each domain is systematically explored by providing a comprehensive definition of the underlying concepts, identifying the inherent challenges, and elucidating the implications for clinical trial design and implementation. SFRT SFRT is a novel technique that delivers a heterogeneous dose distribution within the target volume, characterized by alternating high-dose “peaks” and low-dose “valleys.” 68 , 69 This approach diverges from conventional radiation therapy principles that aim to maximize target coverage and dose homogeneity. Typical SFRT regimens deliver 10–20 Gy per fraction, 70 and techniques have evolved from the use of 2D brass grids to modern technologies such as volumetric modulated arc therapy 71 and proton therapy. 72 The potential synergy between SFRT and immunotherapy has garnered significant interest, because the heterogeneous dose distribution may enhance immunogenic cell death and immune activation in high-dose regions while sparing radiosensitive immune cells in low-dose areas. 73 Proposed mechanisms underlying this synergy include differential vascular effects, bystander signaling, and abscopal responses. 74 – 76 Although retrospective studies have demonstrated tumor response and pain relief with SFRT, 77 , 78 there is a lack of randomized data comparing SFRT to conventional regimens and determining optimal dosing and delivery techniques. Ongoing prospective clinical trials evaluating the combination of SFRT with immunotherapy ( NCT03139318 , NCT05443971 ) are expected to provide crucial information regarding efficacy and toxicity. However, further research is needed to elucidate the immunological mechanisms of SFRT and to conduct large-scale clinical trials through cooperative groups. These efforts will be essential in determining appropriate applications for SFRT, establishing its potential benefits over conventional regimens, and developing consensus guidelines for its use in clinical practice. Combining radiation therapy and immunotherapy The combination of radiation therapy and immunotherapy has emerged as a promising approach in cancer treatment, with the potential for enhanced therapeutic benefit. 79 However, despite many publications and work in this space, the optimal integration of these modalities remains uncertain, necessitating ongoing clinical trials to collect additional data and to try new methods even in the context of standard fractionation. The issue may be optimizing the host in other ways, such as using other agents or cell infusions. Proposed mechanisms underlying the synergy between radiation therapy and immunotherapy include radiation-mediated improvements in tumor antigen visibility, increased recruitment of tumor-infiltrating lymphocytes, and immunomodulation of the tumor microenvironment. 79 A detailed review of the complex relationships between radiation therapy and immunotherapy was beyond the scope of the CIRO retreat and is beyond the scope of this paper but can be better reviewed focused literature reviews 80 on this specific subject. The abscopal phenomenon, which hypothesizes that localized radiation therapy could induce systemic antitumor immune effects, is an area of active investigation. 81 – 83 Immune checkpoint inhibitors, particularly those targeting cytotoxic T-lymphocyte associated protein 4 (CTLA-4) and programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1), are among the most common immunotherapies being combined with radiation in clinical trials. Ongoing studies are evaluating the optimal choice of agents, radiation dosing and fractionation, timing of administration, modalities, and disease sites ( NCT02221739 , NCT03383094 , NCT03085719 , NCT03795207 , NCT02635360 , NCT03738228 , NCT03307759 ). The identification of biomarkers could further inform trial design and patient selection. 84 – 86 However, it is important to recognize that immunotherapy may also negatively impact radiation effects through immune cell cytotoxicity 87 , 88 and tumor microenvironment immunosuppression. 89 Treatment sequencing and fractionation must be carefully considered to balance the radiosensitizing and immunosuppressive effects of these therapies. The mechanistic uncertainties surrounding the combination of radiation therapy and immunotherapy currently impede the optimization of these treatments. A recent detailed dosimetry analysis of the NRG-RTOG 0617 stage III lung cancer trial showed that increasing Effective Radiation Dose to the Immune Cells was significantly associated negatively with tumor control and survival. 90 To address these challenges, large datasets evaluating the efficacy and toxicity of combined radiation therapy and immunotherapy are essential. These data will help determine appropriate applications, recognize limitations, and guide the development of protocols that maximize the potential synergy between these modalities. Collaborative efforts among researchers, clinicians, and patient advocates will be crucial in advancing our understanding of this promising therapeutic approach and translating its benefits to clinical practice. Advances in IGRT Innovations in IGRT techniques Recent advances in IGRT are enabling enhanced target localization, reduced uncertainties, decreased margins, improved normal tissue sparing, and potentially higher tumor control across various radiation therapy modalities, including photon, proton, MR guided radiation therapy, and biology guided radiation therapy. Iterative reconstruction algorithms are being implemented for cone beam CT IGRT, improving image quality for alignment at low-dose levels. 91 Dual-energy CT shows promise for artifact reduction and improved soft tissue contrast by acquiring data at 2 energy spectra. 92 Dual-energy CT improves material decomposition accuracy for functional lung imaging and reduces stopping power ratio uncertainty, enhancing proton therapy dose precision. 93 , 94 The next generation of photon-counting CT scanners will provide unprecedented CT guidance via spectral information, further advancing CT-based IGRT capabilities. 95 MRI-linacs integrate onboard MRI for daily plan adaptation based on anatomy, functional imaging such as diffusion-weighted MRI for treatment response assessment, and beam gating using cine MRI. 96 Radiomics, the extraction of quantitative imaging features imperceptible to the human eye, may predict outcomes and enhance decision support, provided technical validation is performed across scanners. 97 PET-linacs use the PET signal for real-time tumor localization to guide highly conformal dose delivery, with superior normal tissue sparing compared with surrogate-based methods. 98 – 100 However, optimized workflows must ensure target coverage when using PET-linacs. Data analytics and clinical implementation Data analytics present opportunities to leverage large imaging datasets for improved IGRT, but require careful data curation, privacy protection, predictive modeling, uncertainty management, and clinician trust-building. 101 Ongoing studies continue to evaluate margin reduction with advanced IGRT for both photon and proton therapies protons. 102 – 104 Although protons offer dosimetric advantages, range and setup uncertainties warrant robust evaluation. 103 The successful implementation of advanced IGRT techniques in clinical practice relies on rigorous QA and clinical validation. Future directions and challenges Advanced adaptive radiation therapy techniques show great promise in refining radiation therapy targeting and improving the therapeutic ratio. However, their full potential to improve patient outcomes can only be realized through continued technological innovations, rigorous physics research, and well-designed prospective clinical trials. Future research should focus on developing standardized protocols for the acquisition, analysis, and interpretation of multiscale adaptive data, as well as establishing guidelines for the integration of advanced adaptive techniques into existing radiation therapy workflows. The development of robust predictive models and decision support tools based on large, diverse datasets will be crucial in guiding personalized treatment strategies and optimizing patient outcomes. Re-irradiation: Challenges and Opportunities Assessing cumulative radiation doses Re-irradiation, the delivery of additional radiation to a previously irradiated area, is a challenging yet potentially beneficial treatment for some patients. Accurately assessing cumulative radiation doses is critical for the safe and effective implementation of re-irradiation. However, this process is complicated by variability in prior treatment details, elapsed time since initial treatment and anatomical changes that may have occurred during the intervening period. The use of digital imaging and communications in medicine (DICOM) data enables more sophisticated dose summation approaches through image registration and biological modeling. However, in cases where prior treatment records are limited, simple maximum dose summations may suffice. Deformable image registration facilitates comparison across treatment courses but requires careful algorithm selection and validation for longitudinal applications, as per the recommendations of AAPM TG 132. 105 Biological modeling and dose summation approaches The linear-quadratic model is a commonly used tool for enabling biological correction in re-irradiation planning. However, this model has uncertainties at high doses-per-fraction, and applying a dose recovery factor based on elapsed time is an emerging method for addressing these limitations. Directly evaluating composite, biologically corrected dose distributions can aid in the assessment of reirradiation plans, but this approach is complex and requires careful consideration of the underlying assumptions and uncertainties. When feasible, the use of modalities that minimize dose to previously irradiated tissues, such as proton therapy, is preferred for re-irradiation. Adaptive re-irradiation, which involves modifying the treatment plan based on changes in patient anatomy or tumor response, poses similar challenges to those encountered in initial treatment planning. Challenges and future directions Re-irradiation is a promising but high-risk technique that requires meticulous attention to cumulative dose estimation. This process involves a comprehensive evaluation of prior treatment data completeness, image registration accuracy, biological modeling assumptions, dose summation approaches, modality selection, plan adaptation strategies, and method consistency. 106 , 107 Ongoing clinical trials and research efforts should be focused on building consensus frameworks to improve the evidence base guiding re-irradiation. These efforts will help to establish best practices for patient selection, treatment planning, and follow-up, ultimately improving outcomes for patients who may benefit from this approach. Figure 2 illustrates a potential process for implementing re-irradiation, highlighting the key steps and considerations involved in this complex treatment approach. Fig. 2. Open in a new tab Re-irradiation as an area of focus for the field. This figure illustrates the factors critical for accurate cumulative dose determination in re-irradiation clinical trials: standardized image registration, appropriate dose calculation techniques, and biological correction/dose summation methods. Artificial Intelligence-Driven Solutions in Radiation Therapy Clinical Trials AI is transforming the field of radiation oncology, with its applications spanning the entire radiation therapy workflow. The integration of AI in radiation therapy has significant implications for clinical trials. 108 – 110 AI in image analysis and treatment planning AI is revolutionizing radiation therapy by enhancing image quality, generating synthetic images, and facilitating traditionally labor-intensive tasks such as image registration. 111 , 112 AI enables auto-segmentation of tumors and organs at risk, 113 , 114 achieving expert-level accuracy. 115 – 117 Knowledge-based planning leverages machine learning to predict optimal organ dose-volume histograms, 118 , 119 whereas deep learning predicts doses and fluences and intelligently optimizes treatment plans. 120 – 123 Additionally, AI can facilitate automated plan validation, 124 – 126 knowledge-based planning quality evaluation, 118 and machine/patient-specific QA. 124 , 127 , 128 These advancements collectively enhanced patient treatment safety, process efficiency, and practice standardization. Integrated AI platforms are enabling adaptive radiation therapy. 129 – 132 AI identifies patients likely to benefit from adaptation, improves cone beam CT image quality, monitors motion through onboard imaging, detects fiducial markers, and performs critical online segmentation and adaptive replanning. For imaging, equipping AI models to handle variability in acquisition protocols across scanner models, and institutions remains an open problem. For knowledge-based AI planning, care must be taken to avoid perpetuating suboptimal historical practices, and platforms must also demonstrate reliable performance across diverse patient populations. AI in patient selection and outcome prediction AI also assists with patient selection, early disease detection, clinical decision support, risk stratification, and outcome prediction. 133 Deep learning has demonstrated superhuman accuracy for medical image classification tasks and can predict risks and outcomes from population-level electronic health record data at an individual patient level. AI-driven clinical trials are already underway globally across medical disciplines. 134 AI models may also enable collaborative learning without sharing raw data. Additionally, large language models can facilitate communications and extract insights from unstructured clinical text. 135 , 136 AI enables personalized radiation therapy by considering inter- and intra-tumor heterogeneity and patient-specific factors. By integrating diverse data like multi-omics, medical records, and patient history, AI systems can enable personalized treatment planning. Comprehensive analysis of biological data using AI may lead to improved diagnosis, prognosis, treatment response predictions, and identification of new biomarkers and endpoints. 110 Transforming clinical trials with AI AI demonstrates the immense potential to transform clinical trials through enhancements to protocol design, patient recruitment, data collection/management, analysis, and safety monitoring. 137 , 138 Ongoing radiation therapy trials are already utilizing AI for treatment planning and delivery. AI-driven solutions aim to increase the accuracy, precision, efficiency, cost-effectiveness, and overall quality of clinical trials. Challenges and future directions In radiation oncology, challenges arise in obtaining sufficient data, particularly for rare diseases or specialized treatments, from institutions that often operate in silos, and each possesses small datasets. Studies have also revealed challenges in model robustness, stability, interpretability, and avoiding inherent biases. 139 – 142 AI solutions intrinsically depend on the specific data used for training. Clinical trials that incorporate AI components need to consider and address these challenges, ensuring data quality and heterogeneity to avoid AI biases, rigorously validating AI models before deploying, and achieving standardized data curation and stringent privacy regulations. In this context, AI has the potential to strongly democratize cancer care in that expertise and experience broadly can be brought to remote locations with limited resources to potentially positively impact decision-making and treatment processes. Moreover, concerns regarding data privacy, patient consent, and data sharing agreements between institutions should be addressed before starting a clinical trial involving AI software. National and international organizations, such as NRG Oncology, FAIR (Findable, Accessible, Interoperable and Reusable), and AAPM/ASTRO Operational Oncology Ontology, 143 are exploring various guidelines and frameworks to ensure sufficient high-quality and quantity data that can help us leverage the power of AI. AI models must be rigorously validated, with their recommendations carefully evaluated by human experts. For imaging, equipping AI models to handle variability in acquisition protocols across scanner models and institutions remains an open problem. For knowledge-based AI planning, care must be taken to avoid perpetuating suboptimal historical practices. Platforms must also demonstrate reliable performance across diverse patient populations. With thoughtful trial design and diligent human oversight, AI technologies offer tremendous opportunities to enhance the efficiency, quality, and cost-effectiveness of clinical trials. AI can help drive evidence-based advancements of radiation therapy techniques and quality improvement through the analysis of multidimensional data at a scale beyond human capability. Human expertise, however, remains essential to evaluate AI’s performance for safety, reliability, and appropriateness. Ultimately, AI aims to augment human intelligence rather than replace it. Quantum Information Science Quantum science and DT technology represent emerging paradigms with profound implications for the field of radiation oncology. Although some applications may be realized in the near term, others are expected to have a more long-term impact. The following sections provide a more detailed description of these 2 technologies and their potential impact. Quantum information science (QIS) is a rapidly evolving field that harnesses the principles of quantum mechanics to generate new technologies, knowledge, and ideas across 3 primary areas: quantum sensing (QS), quantum computing (QC), and quantum communications. Although quantum communications may not have an immediate impact on radiation oncology, QS and QC are poised to revolutionize the field. 144 QC represents a paradigm shift from traditional digital computing, which relies on binary code consisting of zeros and ones. In contrast, QC utilizes quantum bits (qubits) that can exist as superpositions of 0 and 1 simultaneously. This unique property enables quantum computers to evaluate vast numbers of solutions in parallel. For instance, although a conventional computer would need to check maze paths sequentially, a quantum computer could evaluate all possible paths at once. QC implementations vary, 145 but generally require extreme cooling to near absolute 0 and are not applicable to most problems. 146 QS holds the potential for more immediate impacts through ultra-sensitive measurement. Quantum materials can provide up to 106 better signal-to-noise ratios compared with conventional sensors. This could enable biological imaging with reduced radiation exposure, more accurate diagnostic sensors, and better characterization of tissue samples with less damage to smaller sample volumes. For example, quantum sensors may allow longitudinal monitoring of living tissue models over weeks instead of days by mitigating sample degradation from the thermal effects of conventional sensors. 147 What this may mean for a radiation oncologist is that we will be better able to do preclinical research over time on smaller samples or more frequently without causing tissue damage, so sensors in animals (and even ultimately humans) may become more useful and more sensitive to changes. Sensors of this nature may be able to study the effects of radiation on the heart in real time, noninvasively. 148 QIS is an active worldwide pursuit with multibillion-dollar national investments. In the United States, the 2018 National Quantum Initiative formalized coordination between government agencies to advance QIS capabilities. 149 Quantum technologies are expected to transform clinical radiation oncology through enhanced imaging, biological measurement of ever smaller samples, and basic research. Realizing these advances will require sustained research and development of quantum platforms optimized for biomedical applications. DTs Although not formally developed in the workshop, DTs were discussed informally and are likely to significantly impact the field sooner than quantum devices or methods. On January 30, 2024, the National Academies of Science, Engineering, and Medicine released their report on DTs, with the primary example being a cancer patient. 150 In the report, a DT is defined as “a set of virtual information constructs that mimics the structure, context, and behavior of a natural, engineered, or social system (or system-of-systems), is dynamically updated with data from its physical twin, has a predictive capability, and informs decisions that realize value. The bidirectional interaction between the virtual and the physical is central to the digital twin.” 150 The first 2 DT-focused funding opportunities have been published in the context of biomedical science in only the last few months. The first, led by the NCI and the National Institutes of Health (NIH) Office of the Director, focused on developing a DT in a clinical radiation oncology context and offered administrative supplements to NCI grants. 151 The second funding opportunity, led by the National Science Foundation in collaboration with the NIH and the Food and Drug Administration, aims to advance DT research more broadly. 152 DTs can range from relatively simple to quite complex, but over time, it is hoped that they will work with each other and new types of multiscale data to allow clinicians and others to model and predict clinical outcomes and perhaps to optimize clinical outcomes further. They may also allow patients to gain more insight into their own medical issues and offer them the opportunity to share their data to help others. Some examples of possible DTs in radiation include the following: how do we best schedule follow-ups for patients? Rather than just based on what we “have always done” such scheduling could instead include models for failure risk, financial toxicity, and patient response to therapy (dynamic rates of response during treatment to date before the “follow-up imaging” has been collected, etc.). Another possible issue could be the collection of solutions of treatment plans on “tough” cases globally across a trial or even across multiple trials with similar organ at risk matrices so that treatment teams could gain insights and have a jump start on solutions to a given problem—but with outcome data, toxicity data, and other issues included that are not currently part of plan libraries. Discussion, Synopsis, and Outlook As the field of radiation therapy continues to evolve, it is essential to ensure that the benefits of these advanced techniques are accessible to all patients, regardless of their geographic location or socioeconomic status. This will require a concerted effort to address disparities in access to advanced radiation therapy technologies and to promote education and training for health care professionals in the use of these techniques. In summary, the white paper explores various pioneering technologies that have the potential to revolutionize clinical trials in radiation therapy. These include radiopharmaceutical dosimetry, FLASH radiation therapy, adaptive radiation therapy, LET/RBE, SFRT, the combination of radiation therapy and immunotherapy, AI, QIS, and DTs. We summarized the key focus areas of the discussed technologies in Table 1 . Specifically, we recommend the following: Standardize imaging protocols and dosimetry methods across sites for RPT trials, especially for alpha-particle emitters. Collect comprehensive spatiotemporal dose delivery data in FLASH radiation therapy trials to correlate with outcomes and adapt planning systems and QA accordingly. Integrate advanced adaptive techniques, such as MRI-linacs and PET-linacs, to enable reduced margins, biological image guided dose escalation, and treatment response assessment. Develop consensus frameworks for re-irradiation trials, incorporating rigorous prior dose summation, biological correction models, and techniques like proton therapy or SFRT to minimize toxicity risks. Leverage AI to enhance efficiency, consistency, and QA in clinical trials, while addressing challenges related to data quality, standardization, and infrastructure. Investigate the potential of QIS, particularly QS, to enable ultra-sensitive biological imaging and tissue characterization with reduced radiation exposure. Explore the use of DTs to model and predict clinical outcomes, optimize treatment strategies, and facilitate personalized medicine in efficient and understandable ways for practitioners and patients alike. Table 1. Critical innovations required to advance modern radiation therapy technologies: key focus areas from the NRG Oncology CIRO Technology retreat Technology area Critical innovations needed FLASH Radiation therapy • Development of robust validation methods for FDA approval and quality assurance • Enhanced temporal recording systems for ultra-high dose rate dosimetry • Adaptation of treatment planning systems for modality-specific FLASH delivery • Standardization of dose rate definitions across different treatment modalities • Development of new phantoms and dose calculation algorithms specific to FLASH Radiopharmaceutical dosimetry • Standardization of imaging protocols and analysis across multiple sites • Advanced SPECT reconstruction methods for alpha-emitting radionuclides • Development of dosimetry credentialing criteria for institutions • Implementation of microdosimetry calculations for organ-level distributions • Integration of patient-specific pharmacokinetics into dosimetry calculations Proton therapy • Enhancement of RBE models with whole-system clinical data • Development of accepted LET constraints and optimization techniques • Creation of proton-specific dose constraints • Establishment of organ-specific toxicity prediction database • Implementation of track-end optimization methods Radiation therapy and immunotherapy • Development of optimal timing protocols for combined therapy • Identification of key biomarkers for patient selection • Creation of consensus guidelines for SFRT implementation • Optimization of combined dosing strategies • Methods to balance radiosensitizing and immunosuppressive effects Advances in IGRT • Implementation of advanced reconstruction algorithms for improved image quality • Integration of real-time tumor localization techniques • Development of standardized protocols for adaptive radiation therapy • Enhancement of dual-energy CT capabilities • Creation of robust predictive models for personalized treatment Re-irradiation • Enhancement of dose summation approaches through image registration • Development of improved biological modeling for high doses • Creation of consensus frameworks for patient selection • Implementation of adaptive re-irradiation techniques • Establishment of comprehensive follow-up protocols AI-driven solutions • Development of robust, unbiased training datasets • Creation of standardized data curation and privacy protocols • Enhancement of model interpretability and validation techniques • Integration of AI with treatment planning systems • Development of AI-driven clinical decision support tools Open in a new tab Abbreviations: AI = artificial intelligence; CT = computed tomography; FDA = Food and Drug Administration; IGRT = image guided radiation therapy; LET = linear energy transfer; RBE = relative biological effect; SFRT = spatially fractionated radiation therapy; SPECT = single-photon emission computed tomography. Common to all of the above new areas of science is advanced data management science and indirectly then equitable access—to retreat a patient, we need to be able to know what happened and to use AI, we need to know what went into the model to make sure the AI model reflects all sorts of people, not just those living near major centers of AI research. The use of Internet-based computer technologies, such as cloud computing, big data processing, and machine learning, has shown potential in improving treatment planning and dose delivery. 153 However, challenges such as data interoperability, data quality, and privacy concerns need to be addressed to fully utilize the captured medical information. 143 Radiation oncology is poised to be changed by the use and integration of data, and the development of robust, ethical, and Findable, Accessible, Interoperable and Reusable databases and related infrastructure will be critical to the field. The successful integration of these technologies into clinical trials requires collaborative efforts among researchers, clinicians, industry partners, and regulatory agencies. By harnessing the power of these emerging technologies and the data they generate and need, we can anticipate a future of personalized, precise, and predictive cancer care that will ultimately improve patient outcomes and quality of life. White Paper Statement: This white paper was developed to ensure consistency in the diagnosis and treatment of patients enrolled in NRG clinical trials and represents a consensus of the authors regarding their view of the optimal approaches in that setting. The ideas presented in this paper should not be interpreted as generalized treatment guidelines and cannot guarantee any specific outcome. Diagnosis and treatment decision-making is dependent on physician judgment. The information contained herein is subject to periodic revision as necessary due to the advancement of medical knowledge, technology, and practice. Acknowledgments C.G.-H. declares in the last 36 months grants or contracts with National Cancer Institute of the National, Institutes of Health, R01HL153720 grant as PI, GE Healthcare grant as Co-I, RaySearch as Master Research Agreement PI, LeoCare as Master Research Agreement PI and unpaid collaborations with Medscint, Modus Medical, Inc, patents planned, issued, or pending for flexible modular phantom and method for medical applications using interchangeable inserts patent pending outside the submitted work. S.G. declares in the last 36 months grants or contracts for sponsored research through my academic role at the University of Iowa from Innervate Radiopharma, Novartis, NMCTG, NIH/NCI, DOD, Aktis Oncology, consulting fees from Rayzebio, CDE Dosimetry Inc., Fusion Radiopharma, payment or honoraria for lectures, presentations, speakers, bureaus, manuscript writing or educational events from the University of Arizona, support for attending meetings and/or travel from SNMMI, AAPM, ASTRO. S.F.K. declares all support for the present manuscript from NCI under grant U24CA180803. Q.-T.L. declares in the last 36 months leadership or fiduciary role in other board, society, committee, or advocacy group, paid or unpaid as RTOGF Chair at NRG Oncology. L.L. declares in the last 36 months patents planned, issued, or pending as Emory OTT has filed a patent for IPO-IMPT and SIEMAC, leadership or fiduciary role in other board, society, committee, or advocacy group, paid or unpaid as Chair of NRG Oncology SBRT workgroup, Chair of PTCOG/NRG Oncology joint workgroup of Lung FLASH treatment recommendation, other financial or nonfinancial interests as cofounder of Radiotherapy Biological Optimization Solutions LLC. M.N. declares in the last 36 months grants or contracts from RaySearch Laboratories for a research agreement not related to this manuscript, Oklahoma Shared Clinical and Translational Research Pilot Grant for a research agreement not related to this manuscript. H.P. declares all support for the present manuscript as an employee of Massachusetts General Hospital, declares in the last 36 months grants or contracts from NIH/NCI P01CA261669. Royalties or licenses from CRC/Taylor & Francis book on “Proton Therapy Physics.” E.R. declares in the last 36 months grants or contracts paid to institution from NIH, payment or honoraria for lectures, presentations, speakers, bureaus, manuscript writing, or educational events from AAPM Northern California Chapter and AAPM Southern California Chapter as invited lecturer, support for attending meetings and/or travel from AAPM Southern California Chapter, leadership or fiduciary role in other board, society, committee, or advocacy group, paid or unpaid for GATE Steering Committee as unpaid opensource software collaborator. Y.R. declares in the last 36 months grants or contracts NCR R44CA254844 “Automatic Organ Segmentation Tool for Radiation Treatment Planning of Cancers,” payment or honoraria for lectures, presentations, speakers, bureaus, manuscript writing or educational events for service as Medical Physics Deputy Editor. G.S. declares in the last 36 months grants or contracts R01CA187037, R01CA116477, R01CA240779, U01EB031798, and P01CA272222 from NIH. C.B.S. declares in the last 36 months payment or honoraria for lectures, presentations, speakers, bureaus, manuscript writing, or educational events from Varian Medical Systems, leadership or fiduciary role in other board, society, committee, or advocacy group, paid or unpaid as NRG Oncology, Chair of the Particle Therapy Work Group. P.A.T. declares in the last 36 months grants or contracts from NIH grant CA180803, leadership or fiduciary role in other board, society, committee, or advocacy group, paid or unpaid as Chair, Board of Directors for health care for the Homeless Houston. Y.X. declares in the last 36 months grants or contracts 2U24CA180803–06 (IROC) and 2U10CA180868–06 (NRG). J.W.Z. declares in the last 36 months grants or contracts 1P01CA257904–01A1, leadership or fiduciary role in other board, society, committee, or advocacy group, paid or unpaid as group leader for NRG FLASH Physics workgroup. This project was supported by grants U10CA180868 (NRG Oncology Operations), U24CA180803 (IROC), and CTEP from the National Cancer Institute (NCI). Footnotes No statistical analyses were performed. Disclosures: J.C.B., Y.C., X.J., H.L., M. Machtay, M. Matuszak, X.S.Q., J.L.S., L.T., M.W., and E.J.W. have nothing to disclose. 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