Older Adults with Cancer and Common Comorbidities-Challenges and Opportunities in Improving Their Cancer Treatment Outcomes.

The older American population is rapidly increasing, and millions of older adults will be cancer survivors with comorbidities. This population faces specific challenges regarding treatment and has unique clinical needs. Recognizing this need, the National Cancer Institute (NCI), in collaboration with the National Institute on Aging (NIA), hosted a webinar series, entitled "Cancer, Aging, and Comorbidities." This commentary provides a reflection of five thematic areas covered by the webinar series, which was focused on improving cancer treatment for older adults with cancer and comorbidities: i) the impact of comorbidities on treatment tolerability and patient outcomes; ii) the impact of comorbidities on cancer clinical trial design; iii) the development of wearable devices in measuring comorbidities in cancer treatment; iv) the effects of nutrition and the microbiome on cancer therapy and; v) the role of senescence and senotherapy in age-related diseases. While advances have been made in these areas, many gaps and challenges exist and are discussed in this commentary. To improve cancer survivorship in older populations with comorbidities, aging and comorbidities must be jointly considered and incorporated across the spectrum of cancer research. This includes more basic research of the mechanisms linking comorbidities and cancer development and treatment response, building critical resources and infrastructure (eg, preclinical models and patient samples), conducting clinical trials focused on the older population, integrating geriatric assessment into cancer treatment, and incorporating novel technologies, such as wearable devices into clinical trials and cancer care.

NRG Oncology White Paper on the Relative Biological Effectiveness in Proton Therapy.

This position paper, led by the NRG Oncology Particle Therapy Work Group, focuses on the concept of relative biologic effect (RBE) in clinical proton therapy (PT), with the goal of providing recommendations for the next-generation clinical trials with PT on the best practice of investigating and using RBE, which could deviate from the current standard proton RBE value of 1.1 relative to photons. In part 1, current clinical utilization and practice are reviewed, giving the context and history of RBE. Evidence for variation in RBE is presented along with the concept of linear energy transfer (LET). The intertwined nature of tumor radiobiology, normal tissue constraints, and treatment planning with LET and RBE considerations is then reviewed. Part 2 summarizes current and past clinical data and then suggests the next steps to explore and employ tools for improved dynamic models for RBE. In part 3, approaches and methods for the next generation of prospective clinical trials are explored, with the goal of optimizing RBE to be both more reflective of clinical reality and also deployable in trials to allow clinical validation and interpatient comparisons. These concepts provide the foundation for personalized biologic treatments reviewed in part 4. Finally, we conclude with a summary including short- and long-term scientific focus points for clinical PT. The practicalities and capacity to use RBE in treatment planning are reviewed and considered with more biological data in hand. The intermediate step of LET optimization is summarized and proposed as a potential bridge to the ultimate goal of case-specific RBE planning that can be achieved as a hypothesis-generating tool in near-term proton trials.

Advanced Technologies in Radiation Research.

The U.S. Government is committed to maintaining a robust research program that supports a portfolio of scientific experts who are investigating the biological effects of radiation exposure. On August 17 and 18, 2023, the Radiation and Nuclear Countermeasures Program, within the National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), partnered with the National Cancer Institute, NIH, the National Aeronautics and Space Administration, and the Radiation Injury Treatment Network to convene a workshop titled, Advanced Technologies in Radiation Research (ATRR), which focused on the use of advanced technologies under development or in current use to accelerate radiation research. This meeting report provides a comprehensive overview of the research presented at the workshop, which included an assembly of subject matter experts from government, industry, and academia. Topics discussed during the workshop included assessments of acute and delayed effects of radiation exposure using modalities such as clustered regularly interspaced short palindromic repeats (CRISPR) - based gene editing, tissue chips, advanced computing, artificial intelligence, and immersive imaging techniques. These approaches are being applied to develop products to diagnose and treat radiation injury to the bone marrow, skin, lung, and gastrointestinal tract, among other tissues. The overarching goal of the workshop was to provide an opportunity for the radiation research community to come together to assess the technological landscape through sharing of data, methodologies, and challenges, followed by a guided discussion with all participants. Ultimately, the organizers hope that the radiation research community will benefit from the workshop and seek solutions to scientific questions that remain unaddressed. Understanding existing research gaps and harnessing new or re-imagined tools and methods will allow for the design of studies to advance medical products along the critical path to U.S. Food and Drug Administration approval.

NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions.

Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS. These commercial tools have drawn clinics' attention thanks to their significant benefit in reducing the workload from manual contouring and shortening the duration of treatment planning. However, challenges occur when applying these commercial AI-based segmentation models to diverse clinical scenarios, particularly in uncontrolled environments. Contouring nomenclature and guideline standardization has been the main task undertaken by the NRG Oncology. AI auto-segmentation holds the potential clinical trial participants to reduce interobserver variations, nomenclature non-compliance, and contouring guideline deviations. Meanwhile, trial reviewers could use AI tools to verify contour accuracy and compliance of those submitted datasets. In recognizing the growing clinical utilization and potential of these commercial AI auto-segmentation tools, NRG Oncology has formed a working group to evaluate the clinical utilization and potential of commercial AI auto-segmentation tools. The group will assess in-house and commercially available AI models, evaluation metrics, clinical challenges, and limitations, as well as future developments in addressing these challenges. General recommendations are made in terms of the implementation of these commercial AI models, as well as precautions in recognizing the challenges and limitations.

Retinopathy, Optic Neuropathy, and Cataract in Childhood Cancer Survivors Treated With Radiation Therapy: A PENTEC Comprehensive Review.

PURPOSE: Few reports describe the risks of late ocular toxicities after radiation therapy (RT) for childhood cancers despite their effect on quality of life. The Pediatric Normal Tissue Effects in the Clinic (PENTEC) ocular task force aims to quantify the radiation dose dependence of select late ocular adverse effects. Here, we report results concerning retinopathy, optic neuropathy, and cataract in childhood cancer survivors who received cranial RT. METHODS AND MATERIALS: A systematic literature search was performed using the PubMed, MEDLINE, and Cochrane Library databases for peer-reviewed studies published from 1980 to 2021 related to childhood cancer, RT, and ocular endpoints including dry eye, keratitis/corneal injury, conjunctival injury, cataract, retinopathy, and optic neuropathy. This initial search yielded abstracts for 2947 references, 269 of which were selected as potentially having useful outcomes and RT data. Data permitting, treatment and outcome data were used to generate normal tissue complication probability models. RESULTS: We identified sufficient RT data to generate normal tissue complication probability models for 3 endpoints: retinopathy, optic neuropathy, and cataract formation. Based on limited data, the model for development of retinopathy suggests 5% and 50% risk of toxicity at 42 and 62 Gy, respectively. The model for development of optic neuropathy suggests 5% and 50% risk of toxicity at 57 and 64 Gy, respectively. More extensive data were available to evaluate the risk of cataract, separated into self-reported versus ophthalmologist-diagnosed cataract. The models suggest 5% and 50% risk of self-reported cataract at 12 and >40 Gy, respectively, and 50% risk of ophthalmologist-diagnosed cataract at 9 Gy (>5% long-term risk at 0 Gy in patients treated with chemotherapy only). CONCLUSIONS: Radiation dose effects in the eye are inadequately studied in the pediatric population. Based on limited published data, this PENTEC comprehensive review establishes relationships between RT dose and subsequent risks of retinopathy, optic neuropathy, and cataract formation.

Operational Ontology for Oncology (O3): A Professional Society-Based, Multistakeholder, Consensus-Driven Informatics Standard Supporting Clinical and Research Use of Real-World Data From Patients Treated for Cancer.

PURPOSE: The ongoing lack of data standardization severely undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), radiation oncology information systems, treatment planning systems, and other cancer care and outcomes databases. We sought to create a standardized ontology for clinical data, social determinants of health, and other radiation oncology concepts and interrelationships. METHODS AND MATERIALS: The American Association of Physicists in Medicine's Big Data Science Committee was initiated in July 2019 to explore common ground from the stakeholders' collective experience of issues that typically compromise the formation of large inter- and intra-institutional databases from EHRs. The Big Data Science Committee adopted an iterative, cyclical approach to engaging stakeholders beyond its membership to optimize the integration of diverse perspectives from the community. RESULTS: We developed the Operational Ontology for Oncology (O3), which identified 42 key elements, 359 attributes, 144 value sets, and 155 relationships ranked in relative importance of clinical significance, likelihood of availability in EHRs, and the ability to modify routine clinical processes to permit aggregation. Recommendations are provided for best use and development of the O3 to 4 constituencies: device manufacturers, centers of clinical care, researchers, and professional societies. CONCLUSIONS: O3 is designed to extend and interoperate with existing global infrastructure and data science standards. The implementation of these recommendations will lower the barriers for aggregation of information that could be used to create large, representative, findable, accessible, interoperable, and reusable data sets to support the scientific objectives of grant programs. The construction of comprehensive "real-world" data sets and application of advanced analytical techniques, including artificial intelligence, holds the potential to revolutionize patient management and improve outcomes by leveraging increased access to information derived from larger, more representative data sets.

Framework for Quality Assurance of Ultrahigh Dose Rate Clinical Trials Investigating FLASH Effects and Current Technology Gaps.

FLASH radiation therapy (FLASH-RT), delivered with ultrahigh dose rate (UHDR), may allow patients to be treated with less normal tissue toxicity for a given tumor dose compared with currently used conventional dose rate. Clinical trials are being carried out and are needed to test whether this improved therapeutic ratio can be achieved clinically. During the clinical trials, quality assurance and credentialing of equipment and participating sites, particularly pertaining to UHDR-specific aspects, will be crucial for the validity of the outcomes of such trials. This report represents an initial framework proposed by the NRG Oncology Center for Innovation in Radiation Oncology FLASH working group on quality assurance of potential UHDR clinical trials and reviews current technology gaps to overcome. An important but separate consideration is the appropriate design of trials to most effectively answer clinical and scientific questions about FLASH. This paper begins with an overview of UHDR RT delivery methods. UHDR beam delivery parameters are then covered, with a focus on electron and proton modalities. The definition and control of safe UHDR beam delivery and current and needed dosimetry technologies are reviewed and discussed. System and site credentialing for large, multi-institution trials are reviewed. Quality assurance is then discussed, and new requirements are presented for treatment system standard analysis, patient positioning, and treatment planning. The tables and figures in this paper are meant to serve as reference points as we move toward FLASH-RT clinical trial performance. Some major questions regarding FLASH-RT are discussed, and next steps in this field are proposed. FLASH-RT has potential but is associated with significant risks and complexities. We need to redefine optimization to focus not only on the dose but also on the dose rate in a manner that is robust and understandable and that can be prescribed, validated, and confirmed in real time. Robust patient safety systems and access to treatment data will be critical as FLASH-RT moves into the clinical trials.

Intensive Multimodality Therapy for Extraocular Retinoblastoma: A Children's Oncology Group Trial (ARET0321).

PURPOSE: Metastatic retinoblastoma has a poor prognosis when treated with conventional chemotherapy and radiation therapy (RT). Intensified therapy may improve the outcome. METHODS: A prospective, international trial enrolled patients with extraocular retinoblastoma. Patients with stage II or III (locoregional) retinoblastoma received four cycles of chemotherapy, followed by involved field RT (45 Gy). Patients with stage IVa or IVb (metastatic or trilateral) retinoblastoma also received four cycles of chemotherapy and those with ≥ partial response then received one cycle of high-dose carboplatin, thiotepa, and etoposide with autologous hematopoietic stem-cell support. Patients with stage IVa or IVb with residual tumor postchemotherapy received RT. The proportion of patients who achieved event-free survival would be reported and compared with historical controls separately for each of the three groups of patients. RESULTS: Fifty-seven eligible patients were included in the analyses. Event-free survival at 1 year was 88.1% (90% CI, 66.6 to 96.2) for stage II-III, 82.6% (90% CI, 61.0 to 92.9) for stage IVa, and 28.3% (90% CI, 12.7 to 46.2) for stage IVb/trilateral. Toxicity was significant as expected and included two therapy-related deaths. CONCLUSION: Intensive multimodality therapy is highly effective for patients with regional extraocular retinoblastoma and stage IVa metastatic retinoblastoma. Although the study met its aim for stage IVb, more effective therapy is still required for patients with CNS involvement (ClinicalTrials.gov identifier: NCT00554788).

A roadmap to clinical trials for FLASH.

While FLASH radiation therapy is inspiring enthusiasm to transform the field, it is neither new nor well understood with respect to the radiobiological mechanisms. As FLASH clinical trials are designed, it will be important to ensure we can deliver dose consistently and safely to every patient. Much like hyperthermia and proton therapy, FLASH is a promising new technology that will be complex to implement in the clinic and similarly will require customized credentialing for multi-institutional clinical trials. There is no doubt that FLASH seems promising, but many technologies that we take for granted in conventional radiation oncology, such as rigorous dosimetry, 3D treatment planning, volumetric image guidance, or motion management, may play a major role in defining how to use, or whether to use, FLASH radiotherapy. Given the extended time frame for patients to experience late effects, we recommend moving deliberately but cautiously forward toward clinical trials. In this paper, we review the state of quality assurance and safety systems in FLASH, identify critical pre-clinical data points that need to be defined, and suggest how lessons learned from previous technological advancements will help us close the gaps and build a successful path to evidence-driven FLASH implementation.

Predictive Radiation Oncology - A New NCI-DOE Scientific Space and Community.

With a widely attended virtual kickoff event on January 29, 2021, the National Cancer Institute (NCI) and the Department of Energy (DOE) launched a series of 4 interactive, interdisciplinary workshops-and a final concluding "World Café" on March 29, 2021-focused on advancing computational approaches for predictive oncology in the clinical and research domains of radiation oncology. These events reflect 3,870 human hours of virtual engagement with representation from 8 DOE national laboratories and the Frederick National Laboratory for Cancer Research (FNL), 4 research institutes, 5 cancer centers, 17 medical schools and teaching hospitals, 5 companies, 5 federal agencies, 3 research centers, and 27 universities. Here we summarize the workshops by first describing the background for the workshops. Participants identified twelve key questions-and collaborative parallel ideas-as the focus of work going forward to advance the field. These were then used to define short-term and longer-term "Blue Sky" goals. In addition, the group determined key success factors for predictive oncology in the context of radiation oncology, if not the future of all of medicine. These are: cross-discipline collaboration, targeted talent development, development of mechanistic mathematical and computational models and tools, and access to high-quality multiscale data that bridges mechanisms to phenotype. The workshop participants reported feeling energized and highly motivated to pursue next steps together to address the unmet needs in radiation oncology specifically and in cancer research generally and that NCI and DOE project goals align at the convergence of radiation therapy and advanced computing.

Moving Forward in the Next Decade: Radiation Oncology Sciences for Patient-Centered Cancer Care.

In a time of rapid advances in science and technology, the opportunities for radiation oncology are undergoing transformational change. The linkage between and understanding of the physical dose and induced biological perturbations are opening entirely new areas of application. The ability to define anatomic extent of disease and the elucidation of the biology of metastases has brought a key role for radiation oncology for treating metastatic disease. That radiation can stimulate and suppress subpopulations of the immune response makes radiation a key participant in cancer immunotherapy. Targeted radiopharmaceutical therapy delivers radiation systemically with radionuclides and carrier molecules selected for their physical, chemical, and biochemical properties. Radiation oncology usage of "big data" and machine learning and artificial intelligence adds the opportunity to markedly change the workflow for clinical practice while physically targeting and adapting radiation fields in real time. Future precision targeting requires multidimensional understanding of the imaging, underlying biology, and anatomical relationship among tissues for radiation as spatial and temporal "focused biology." Other means of energy delivery are available as are agents that can be activated by radiation with increasing ability to target treatments. With broad applicability of radiation in cancer treatment, radiation therapy is a necessity for effective cancer care, opening a career path for global health serving the medically underserved in geographically isolated populations as a substantial societal contribution addressing health disparities. Understanding risk and mitigation of radiation injury make it an important discipline for and beyond cancer care including energy policy, space exploration, national security, and global partnerships.

Overview and Lessons From the Preclinical Chemoradiotherapy Testing Consortium.

PURPOSE: In the current molecular-targeted cancer treatment era, many new agents are being developed so that optimizing therapy with a combination of radiation and drugs is complex. The use of emerging laboratory technologies to further biological understanding of drug-radiation mechanisms of action will enhance the efficiency of the progression from preclinical studies to clinical trials. In 2017, the National Cancer Institute (NCI) solicited proposals through PAR 16-111 to conduct preclinical research combining targeted anticancer agents in the Cancer Therapy Evaluation Program's portfolio with chemoradiation. METHODS AND MATERIALS: The Preclinical Chemo-Radiotherapy Testing Consortium (PCRTC) was formed with 4 U01 programs supported to generate validated high-quality preclinical data on the effects of molecular therapeutics when added to standard-of-care therapies with a concentration on cancers of the pancreas, lung, head and neck, gastrointestinal tract, and brain. RESULTS: The PCRTC provides a rational basis for prioritizing NCI-supported investigational new drugs or agents most likely to have clinical activity with chemoradiotherapy and accelerate the pace at which combined modality treatments with greater efficacy are identified and incorporated into standard treatment practices. CONCLUSIONS: Herein, we introduce and summarize the course of the PCRTC to date and report 3 preliminary observations from the consortium's work to date.