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

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.

Optimizing energy threshold selection for low-concentration contrast agent quantification in small animal photon-counting CT.

Gold nanoparticles (GNPs) are widely used for biological research and applications. The in-vivo concentration of GNPs is usually low due to biological safety concerns, thus posing a challenge for imaging. This work investigates on optimal energy threshold selection in photon-counting detector(PCD)-based CT (PCCT) for the quantification of low-concentration GNPs.

Case report: Atypical young case of MV1 Creutzfeldt-Jakob disease with unusually long survival.

Creutzfeldt-Jakob disease (CJD) is a rare, fatal, rapidly progressive neurodegenerative disease resulting from an accumulation of misfolded prion proteins (PrP). CJD affects 1-2 new individuals per million each year, and the sporadic type accounts for 90% of those cases. Though the median age at onset and disease duration vary depending on the subtype of sporadic CJD (sCJD), the disease typically affects middle-aged to elderly individuals with a median survival of 4-6 months.

Joint k-b space diffusion-weighted image reconstruction and apparent diffusion coefficient fitting for diffusion-weighted turbo-spin-echo imaging.

Background: Diffusion-weighted (DW) turbo-spin-echo (TSE) imaging offers improved geometric fidelity compared to single-shot echo-planar-imaging (EPI). However, it suffers from low signal-to-noise ratio (SNR) and prolonged acquisition times, thereby restricting its applications in diagnosis and MRI-guided radiotherapy (MRgRT).

Purpose: To develop a joint k-b space reconstruction algorithm for concurrent reconstruction of DW-TSE images and the apparent diffusion coefficient (ADC) map with enhanced image quality and more accurate quantitative measurements.

Design and construction of gradient coils for an MRI-guided small animal radiation platform.

Small animal radiation experiments use a dedicated hardware platform to deliver radiation to small animals to support pre-clinical radiobiological studies. Image guidance is critical to achieve experiment accuracy. MR-based image guidance became recently available in human radiation therapy by integrating an MR scanner with a medical linear accelerator.

Global Workforce and Access: Demand, Education, Quality.

There has long existed a substantial disparity in access to radiotherapy globally. This issue has only been exacerbated as the growing disparity of cancer incidence between high-income countries (HIC) and low and middle-income countries (LMICs) widens, with a pronounced increase in cancer cases in LMICs. Even within HICs, iniquities within local communities may lead to a lack of access to care.

Advancing the Collaboration Between Imaging and Radiation Oncology.

The fusion of cutting-edge imaging technologies with radiation therapy (RT) has catalyzed transformative breakthroughs in cancer treatment in recent decades. It is critical for us to review our achievements and preview into the next phase for future synergy between imaging and RT. This paper serves as a review and preview for fostering collaboration between these two domains in the forthcoming decade.

Gold Nanoparticle Transport in the Injured Kidneys with Elevated Renal Function Biomarkers.

Renal function biomarkers such as serum blood urea nitrogen (BUN) and creatinine (Cr) serve as key indicators for guiding clinical decisions before administering kidney-excreted small-molecule agents. With engineered nanoparticles increasingly designed to be renally clearable to expedite their clinical translation, understanding the relationship between renal function biomarkers and nanoparticle transport in diseased kidneys becomes crucial to their biosafety in future clinical applications. In this study, renal-clearable gold nanoparticles (AuNPs) are used as X-ray contrast agents to noninvasively track their transport and retention in cisplatin-injured kidneys with varying BUN and Cr levels.

Joint - Space Image Reconstruction and Data Fitting for Chemical Exchange Saturation Transfer Magnetic Resonance Imaging.

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is a novel MRI technology to image certain compounds at extremely low concentrations. Long acquisition time to measure signals at a set of offset frequencies of the Z-spectra and to repeat measurements to reduce noise pose significant challenges to its applications. This study explores correlations of CEST MR images along the spatial and Z-spectral dimensions to improve MR image quality and robustness of magnetization transfer ratio (MTR) asymmetry estimation via a joint -ω reconstruction model.

Human-like intelligent automatic treatment planning of head and neck cancer radiation therapy.

Automatic treatment planning of radiation therapy (RT) is desired to ensure plan quality, improve planning efficiency, and reduce human errors. We have proposed an Intelligent Automatic Treatment Planning framework with a virtual treatment planner (VTP), an artificial intelligence robot built using deep reinforcement learning, autonomously operating a treatment planning system (TPS). This study extends our previous successes in relatively simple prostate cancer RT planning to head-and-neck (H&N) cancer, a more challenging context even for human planners due to multiple prescription levels, proximity of targets to critical organs, and tight dosimetric constraints.

Clinical VMAT machine parameter optimization for localized prostate cancer using deep reinforcement learning.

Background: Volumetric modulated arc therapy (VMAT) machine parameter optimization (MPO) remains computationally expensive and sensitive to input dose objectives creating challenges for manual and automatic planning. Reinforcement learning (RL) involves machine learning through extensive trial-and-error, demonstrating performance exceeding humans, and existing algorithms in several domains.

Purpose: To develop and evaluate an RL approach for VMAT MPO for localized prostate cancer to rapidly and automatically generate deliverable VMAT plans for a clinical linear accelerator (linac) and compare resultant dosimetry to clinical plans.

Predicting treatment plan approval probability for high-dose-rate brachytherapy of cervical cancer using adversarial deep learning.

Predicting the probability of having the plan approved by the physician is important for automatic treatment planning. Driven by the mathematical foundation of deep learning that can use a deep neural network to represent functions accurately and flexibly, we developed a deep-learning framework that learns the probability of plan approval for cervical cancer high-dose-rate brachytherapy (HDRBT).The system consisted of a dose prediction network (DPN) and a plan-approval probability network (PPN).

The role of self-related information in the sense of agency.

Sense of agency (SoA) refers to the subjective experience of controlling one's actions and their subsequent consequences. The present study endeavors to investigate the impact of how different degrees of self-related stimuli as action outcomes on the sense of agency by observing the temporal binding effect. Results showed that self-related sound significantly altered temporal binding, notably influencing outcome binding.

Definitive radiation for advanced cervix cancer is not associated with vaginal shortening-a prospective vaginal length and dose correlation.

Purpose: Prospectively measure change in vaginal length after definitive chemoradiation (C-EBRT) with Intracavitary Brachytherapy (ICBT) for locally advanced cervix cancer (LACC) and correlate with vaginal dose (VD).

Materials And Methods: Twenty one female patients with LACC receiving C-EBRT and ICBT underwent serial vaginal length (VL) measurements. An initial measurement was made at the time of the first ICBT procedure and subsequently at 3 month intervals up to 1 year post radiation.

Virtual dosimetry study with three cone-beam breast computed tomography scanners using a fast GPU-based Monte Carlo code.

. To compare the dosimetric performance of three cone-beam breast computed tomography (BCT) scanners, using real-time Monte Carlo-based dose estimates obtained with the virtual clinical trials (VCT)-BREAST graphical processing unit (GPU)-accelerated platform dedicated to VCT in breast imaging. A GPU-based Monte Carlo (MC) code was developed for replicatingthe geometric, x-ray spectra and detector setups adopted, respectively, in two research scanners and one commercial BCT scanner, adopting 80 kV, 60 kV and 49 kV tube voltage, respectively.

Feasibility of Synchrotron-Based Ultra-High Dose Rate (UHDR) Proton Irradiation with Pencil Beam Scanning for FLASH Research.

Background: This study aims to present the feasibility of developing a synchrotron-based proton ultra-high dose rate (UHDR) pencil beam scanning (PBS) system.

Methods: The RF extraction power in the synchrotron system was increased to generate 142.4 MeV pulsed proton beams for UHDR irradiation at ~100 nA beam current.

Spectral CT image reconstruction using a constrained optimization approach-An algorithm for AAPM 2022 spectral CT grand challenge and beyond.

Background: CT reconstruction is of essential importance in medical imaging. In 2022, the American Association of Physicists in Medicine (AAPM) sponsored a Grand Challenge to investigate the challenging inverse problem of spectral CT reconstruction, with the aim of achieving the most accurate reconstruction results. The authors of this paper participated in the challenge and won as a runner-up team.

Content specificity of attentional bias to COVID-19 threat-related information in trait anxiety.

Introduction: Anxious individuals selectively attend to threatening information, but it remains unclear whether attentional bias can be generalized to traumatic events, such as the COVID-19 pandemic. Previous studies suggested that specific threats related to personal experiences can elicit stronger attentional bias than general threats. The current study aimed to investigate the relationship between content-specific attentional bias and trait anxiety during the COVID-19 pandemic.

ART2Dose: A comprehensive dose verification platform for online adaptive radiotherapy.

Background: Online adaptive radiotherapy (ART) involves the development of adaptable treatment plans that consider patient anatomical data obtained right prior to treatment administration, facilitated by cone-beam computed tomography guided adaptive radiotherapy (CTgART) and magnetic resonance image-guided adaptive radiotherapy (MRgART). To ensure accuracy of these adaptive plans, it is crucial to conduct calculation-based checks and independent verification of volumetric dose distribution, as measurement-based checks are not practical within online workflows. However, the absence of comprehensive, efficient, and highly integrated commercial software for secondary dose verification can impede the time-sensitive nature of online ART procedures.

DAART: a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancer.

Purpose: The study aimed to implement a novel, deeply accelerated adaptive radiation therapy (DAART) approach for lung cancer radiotherapy (RT). Lung cancer is the most common cause of cancer-related death, and RT is the preferred medically inoperable treatment for early stage non-small cell lung cancer (NSCLC). In the current lengthy workflow, it takes a median of four weeks from diagnosis to RT treatment, which can result in complete restaging and loss of local control with delay.

Implementation and evaluation of an intelligent automatic treatment planning robot for prostate cancer stereotactic body radiation therapy.

Purpose: We previously developed a virtual treatment planner (VTP), an artificial intelligence robot, operating a treatment planning system (TPS). Using deep reinforcement learning guided by human knowledge, we trained the VTP to autonomously adjust relevant parameters in treatment plan optimization, similar to a human planner, to generate high-quality plans for prostate cancer stereotactic body radiation therapy (SBRT). This study describes the clinical implementation and evaluation of VTP.

Development of metaverse for intelligent healthcare.

The metaverse integrates physical and virtual realities, enabling humans and their avatars to interact in an environment supported by technologies such as high-speed internet, virtual reality, augmented reality, mixed and extended reality, blockchain, digital twins and artificial intelligence (AI), all enriched by effectively unlimited data. The metaverse recently emerged as social media and entertainment platforms, but extension to healthcare could have a profound impact on clinical practice and human health. As a group of academic, industrial, clinical and regulatory researchers, we identify unique opportunities for metaverse approaches in the healthcare domain.