The United States Department of Energy and National Institutes of Health Collaboration: Medical Care Advances via Discovery in Physical Sciences.

Over several months, representatives from the U.S. Department of Energy (DOE) Office of Science and National Institutes of Health (NIH) had a number of meetings that lead to the conclusion that innovations in the Nation's health care could be realized by more directed interactions between NIH and DOE. It became clear that the expertise amassed and instrumentation advances developed at the DOE physical science laboratories to enable cutting-edge research in particle physics could also feed innovation in medical healthcare. To meet their scientific mission, the DOE laboratories created advances in such technologies as particle beam generation, radioisotope production, high-energy particle detection and imaging, superconducting particle accelerators, superconducting magnets, cryogenics, high-speed electronics, artificial intelligence, and big data. To move forward, NIH and DOE initiated the process of convening a joint workshop which occurred on July 12th and 13th, 2021. This Special Report presents a summary of the findings of the collaborative workshop and introduces the goals of the next one.

The State of Preclinical Modeling for Early Phase Cancer Trials Using Molecularly Targeted Agents with Radiation.

Preclinical studies inform and guide the development of novel treatment combination strategies that bridge the laboratory with the clinic. We aimed to evaluate approaches cancer researchers used to justify advancing new combinations of molecularly targeted agents and radiation treatment into early-phase human clinical trials. Unsolicited early phase clinical trial proposals submitted to the National Cancer Institute's Cancer Therapy Evaluation Program between January 2016 and July 2020 were curated to quantify key characteristics and proportion of preclinical data provided by trialists seeking to conduct molecularly targeted agent-radiation combination studies in cancer patients. These data elucidate the current landscape for how the rationale for a molecularly targeted agent-radiation combination therapy is supported by preclinical research and illustrate unique challenges faced in translation at the intersection of precision medicine and radiation oncology.

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.

Multispecialty Enterprise Imaging Workgroup Consensus on Interactive Multimedia Reporting Current State and Road to the Future: HIMSS-SIIM Collaborative White Paper.

Diagnostic and evidential static image, video clip, and sound multimedia are captured during routine clinical care in cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, endoscopic procedural specialties, and other medical disciplines. Providers typically describe the multimedia findings in contemporaneous electronic health record clinical notes or associate a textual interpretative report. Visual communication aids commonly used to connect, synthesize, and supplement multimedia and descriptive text outside medicine remain technically challenging to integrate into patient care. Such beneficial interactive elements may include hyperlinks between text, multimedia elements, alphanumeric and geometric annotations, tables, graphs, timelines, diagrams, anatomic maps, and hyperlinks to external educational references that patients or provider consumers may find valuable. This HIMSS-SIIM Enterprise Imaging Community workgroup white paper outlines the current and desired clinical future state of interactive multimedia reporting (IMR). The workgroup adopted a consensus definition of IMR as "interactive medical documentation that combines clinical images, videos, sound, imaging metadata, and/or image annotations with text, typographic emphases, tables, graphs, event timelines, anatomic maps, hyperlinks, and/or educational resources to optimize communication between medical professionals, and between medical professionals and their patients." This white paper also serves as a precursor for future efforts toward solving technical issues impeding routine interactive multimedia report creation and ingestion into electronic health records.

National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation.

PURPOSE: Artificial intelligence (AI) is about to touch every aspect of radiation therapy, from consultation to treatment planning, quality assurance, therapy delivery, and outcomes modeling. There is an urgent need to train radiation oncologists and medical physicists in data science to help shepherd AI solutions into clinical practice. Poorly trained personnel may do more harm than good when attempting to apply rapidly developing and complex technologies. As the amount of AI research expands in our field, the radiation oncology community needs to discuss how to educate future generations in this area. METHODS AND MATERIALS: The National Cancer Institute (NCI) Workshop on AI in Radiation Oncology (Shady Grove, MD, April 4-5, 2019) was the first of 2 data science workshops in radiation oncology hosted by the NCI in 2019. During this workshop, the Training and Education Working Group was formed by volunteers among the invited attendees. Its members represent radiation oncology, medical physics, radiology, computer science, industry, and the NCI. RESULTS: In this perspective article written by members of the Training and Education Working Group, we provide and discuss action points relevant for future trainees interested in radiation oncology AI: (1) creating AI awareness and responsible conduct; (2) implementing a practical didactic curriculum; (3) creating a publicly available database of training resources; and (4) accelerating learning and funding opportunities. CONCLUSION: Together, these action points can facilitate the translation of AI into clinical practice.

Understanding High-Dose, Ultra-High Dose Rate, and Spatially Fractionated Radiation Therapy.

The National Cancer Institute's Radiation Research Program, in collaboration with the Radiosurgery Society, hosted a workshop called Understanding High-Dose, Ultra-High Dose Rate and Spatially Fractionated Radiotherapy on August 20 and 21, 2018 to bring together experts in experimental and clinical experience in these and related fields. Critically, the overall aims were to understand the biological underpinning of these emerging techniques and the technical/physical parameters that must be further defined to drive clinical practice through innovative biologically based clinical trials.

Monte Carlo Processing on a Chip (MCoaC)-preliminary experiments toward the realization of optimal-hardware for TOPAS/Geant4 to drive discovery.

Amongst the scientific frameworks powered by the Monte Carlo (MC) toolkit Geant4 (Agostinelli et al., 2003), the TOPAS (Tool for Particle Simulation) (Perl et al., 2012) is one. TOPAS focuses on providing ease of use, and has significant implementation in the radiation oncology space at present. TOPAS functionality extends across the full capacity of Geant4, is freely available to non-profit users, and is being extended into radiobiology via TOPAS-nBIO (Ramos-Mendez et al., 2018). A current "grand problem" in cancer therapy is to convert the dose of treatment from physical dose to biological dose, optimized ultimately to the individual context of administration of treatment. Biology MC calculations are some of the most complex and require significant computational resources. In order to enhance TOPAS's ability to become a critical tool to explore the definition and application of biological dose in radiation therapy, we chose to explore the use of Field Programmable Gate Array (FPGA) chips to speedup the Geant4 calculations at the heart of TOPAS, because this approach called "Reconfigurable Computing" (RC), has proven able to produce significant (around 90x) (Sajish et al., 2012) speed increases in scientific computing. Here, we describe initial steps to port Geant4 and TOPAS to be used on FPGA. We provide performance analysis of the current TOPAS/Geant4 code from an RC implementation perspective. Baseline benchmarks are presented. Achievable performance figures of the subsections of the code on optimal hardware are presented; Aspects of practical implementation of "Monte Carlo on a chip" are also discussed.

NCTN Assessment on Current Applications of Radiomics in Oncology.

Radiomics is a fast-growing research area based on converting standard-of-care imaging into quantitative minable data and building subsequent predictive models to personalize treatment. Radiomics has been proposed as a study objective in clinical trial concepts and a potential biomarker for stratifying patients across interventional treatment arms. In recognizing the growing importance of radiomics in oncology, a group of medical physicists and clinicians from NRG Oncology reviewed the current status of the field and identified critical issues, providing a general assessment and early recommendations for incorporation in oncology studies.

Feasibility of implementing stereotactic body radiation therapy using a non-commercial volumetric modulated arc therapy treatment planning system for early stage lung cancer.

Nearly 25% of patients diagnosed with early-stage non-small cell lung carcinomas (NSCLC) are medically inoperable. For these patients, the radial stereotactic body radiation therapy (SBRT), planned and delivered with intensity modulated radiation therapy (IMRT) techniques, offers the only curative option. However, IMRT-SBRT has three significant deficiencies: an elevated beam-on time (MU); a reduced MU-to-cGy coefficient; and a prolonged delivery time. To address these issues, we have developed our in-house version of volumetric modulated arc therapy (VMAT). In this preliminary study, we compared VMAT-SBRT with IMRT-SBRT in terms of optimization, dosimetry, and delivery. Our goal was to investigate the feasibility of replacing the exiting IMRT-SBRT with VMAT-SBRT as a safe and viable alternative radiation modality for early-stage NSCLC.

The development of a novel radiation treatment modality - volumetric modulated arc therapy.

Recent theoretical studies and clinical investigations have indicated that volumetric modulated arc therapy (VMAT) can produce equal or better treatment plans than intensity modulated radiation therapy (IMRT), while achieving a significant reduction in treatment time. Built upon the concept of aperture-based multi-level beam source sampling optimization, VMAT has overcome many engineering constraints and become a clinically viable radiation treatment modality. At this point in time, however, there are only two commercial VMAT treatment planning systems (TPS) on the market, which severely limit the dissemination of this novel technology. To address this issue, we recently have successfully developed our own version of VMAT TPS. In this paper, we present our preliminary test results.

Dosimetric effect of respiratory motion in external beam radiotherapy of the lung.

BACKGROUND AND PURPOSE: To study the effect of breathing motion on gross tumor volume (GTV) coverage for lung tumors using dose-volume histograms and relevant dosimetric indices. PATIENTS AND METHODS: Treatment plans were chosen for 12 patients treated at our institution for lung carcinoma. GTV volumes of these patients ranged from 1.2 to 97.3 cm(3). A margin of 1-2 cm was used to generate the planning target volume (PTV). Additional margins of 0.6-1.0 cm were added to the PTV when designing treatment portals. For the purposes of TCP calculation, the prescription dose was assumed to be 70 Gy to remove the effects of prescription differences. Setup error was incorporated into the evaluation of treatment plans with a systematic component of sigma(RL) = 0.2 cm, sigma(AP) = 0.2 cm, and sigma(SI) = 0.3 cm and a random component of sigma(RL) = 0.3 cm, sigma(AP) = 0.3 cm, and sigma(SI) = 0.3 cm. Breathing motion was incorporated into these plans based on an independent analysis of fluoroscopic movies of the diaphragm for 7 patients. The systematic component of breathing motion (sigma(RL) = 0.3 cm, sigma(AP) = 0.2 cm, and sigma(SI) = 0.6 cm) was incorporated into the treatment plans on a slice by slice basis. The intrafractional component of breathing motion (sigma(RL) = 0.3 cm, sigma(AP) = 0.2 cm, and sigma(SI) = 0.6 cm) was incorporated by averaging the dose calculation over all displacements of the breathing cycle. Each patient was simulated 500 times to discern the range of possible outcomes. The simulations were repeated for a worst case scenario which used only breathing data with a large diaphragmatic excursion, both with and without intrafractional breathing motion. RESULTS: Dose to 95% of the GTV (D95), volume of the GTV receiving 95% of the prescription dose (V95) and TCP changed an average of -1.4+/-4.2, -1.0+/-3.3, and -1.4+/-3.8%, respectively, with the incorporation of normal breathing effects. In the worst case scenario (heavy breathers), D95 and V95 changed an average of -9.8+/-10.1 and -8.3+/-11.3%, respectively, and TCP changed by -8.1+/-9.1%. GTVs with volumes greater than 60 cm(3) showed stronger sensitivity to breathing especially if the shape was non-ellipsoidal. In the normal breathing case, the probability of a decrease in D95, V95, or TCP of a magnitude greater than 10% is less than 4%, and in the worse case scenario this probability is approximately 30-40% with intrafractional breathing motion included, and less than 10% with intrafractional breathing motion not included. CONCLUSIONS: With the PTV margins routinely used at our center, the effects of normal breathing on coverage are small on the average, with a less than 4% chance of a 10% or greater decrease in D95, V95, or TCP. However, in patients with large respiration-induced motion, the effect can be significant and efforts to identify such patients are important.