Justification of radiological procedures in COVID-19 pandemic based on radiation risk only

Abstract

Purpose. Radiologic procedures are recommended based on benefit-to-risk justification. In X-ray imaging, while the benefit is often immediate for the patient, the associated radiation burden risk is a longer-term effect. Such a temporal gap can bias the justification process in imaging utilization, particularly during a spreading pandemic like COVID-19 in which fast and accurate diagnostic tools are highly needed. Chest CT and chest radiography (CXR) have shown promising results in the diagnosis and management of COVID-19, providing support to the standard RT-PCR test. However, several institutions are discouraging the use of imaging for this purpose, partly due to radiation risk. This study aims to provide quantitative data towards an effective risk-to benefit analysis for the justification of radiological studies in the diagnosis and management of COVID-19 to guide clinicians and decision making. Materials and Methods. The analysis was performed in terms of mortality rate per age group. COVID-19 mortality was extracted from epidemiological data across 159,107 patients in Italy. For radiological risk, the study considered 659 Chest CT scans performed in adult patients. Organ doses were estimated using a Monte Carlo based method and then used to calculate a risk index that was converted into a related 5-year mortality rate (SEER, NCI). Results. COVID-19 mortality showed a rapid rise for ages >30 years old (min: 0.30%; max: 30.20%). Only 1 death was reported in the analyzed patient cohort for ages <20 years old. The mortality rates based on radiation exposure decreased across age groups. The median mortality rate across all ages for Chest CT and CXR were 0.72% (min: 0.46%; max: 1.10%) and 0.03% (min: 0.02%; max: 0.04%), respectively. Conclusions. Radiation risk is not the only factor that should be taken into account for justifying the use of imaging in COVID care; nonetheless, it is an essential factor of consideration. The risk associated with COVID-19, CT, and CXR exhibited different magnitudes and trends across age groups. In higher ages, the risk of COVID-19 far outweighed that of radiological exams. Based on risk comparison alone, CXR and Chest CT are justified for COVID-19 care of patients older than 30 and 50 years old, respectively.

Clinical Relevance statement (max 200 characters, with spaces) Towards a comprehensive radiological procedures risk-to-benefit assessment, CT and CXR should not be a priori excluded in the diagnosis and management of the COVID-19.

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Scholars@Duke

Ria

Francesco Ria

Assistant Professor of Radiology

Dr. Francesco Ria is a medical physicist and he serves as an Assistant Professor in the Department of Radiology. Francesco has an extensive expertise in the assessment of procedure performances in radiology. In particular, his research activities focus on the simultaneous evaluation of radiation dose and image quality in vivo in computed tomography providing a comprehensive evaluation of radiological exams. Moreover, Francesco is developing and investigating novel mathematical models that, uniquely in the radiology field, can incorporate a comprehensive and quantitative risk-to-benefit assessment of the procedures; he is continuing to apply his expertise towards the definition of new patient specific risk metrics, and in the assessment of image quality in vivo also using state-of-the-art imaging technology, such as photon counting computed tomography scanners, and machine learning reconstruction algorithms.

Dr. Ria is a member of the American Association of Physicists in Medicine task group 392 (Investigation and Quality Control of Automatic Exposure Control System in CT), of the American Association of Physicists in Medicine Public Education working group (WGATE), and of the Italian Association of Medical Physics task group Dose Monitoring in Diagnostic Imaging.

Segars

William Paul Segars

Professor in Radiology

Our current research involves the use of computer-generated phantoms and simulation techniques to investigate and optimize medical imaging systems and methods. Medical imaging simulation involves virtual experiments carried out entirely on the computer using computational models for the patients as well as the imaging devices. Simulation is a powerful tool for characterizing, evaluating, and optimizing medical imaging systems. A vital aspect of simulation is to have realistic models of the subject's anatomy as well as accurate models for the physics of the imaging process. Without this, the results of the simulation may not be indicative of what would occur in actual clinical studies and would, therefore, have limited practical value. We are leading the development of realistic simulation tools for use toward human and small animal imaging research.

These tools have a wide variety of applications in many different imaging modalities to investigate the effects of anatomical, physiological, physical, and instrumentational factors on medical imaging and to research new image acquisition strategies, image processing and reconstruction methods, and image visualization and interpretation techniques. We are currently applying them to the field of x-ray CT. The motivation for this work is the lack of sufficiently rigorous methods for optimizing the image quality and radiation dose in x-ray CT to the clinical needs of a given procedure. The danger of unnecessary radiation exposure from CT applications, especially for pediatrics, is just now being addressed. Optimization is essential in order for new and emerging CT applications to be truly useful and not represent a danger to the patient. Given the relatively high radiation doses required of current CT systems, thorough optimization is unlikely to ever be done in live patients. It would be prohibitively expensive to fabricate physical phantoms to simulate a realistic range of patient sizes and clinical needs especially when physiologic motion needs to be considered. The only practical approach to the optimization problem is through the use of realistic computer simulation tools developed in our work.

Samei

Ehsan Samei

Reed and Martha Rice Distinguished Professor of Radiology

Dr. Ehsan Samei, PhD, DABR, FAAPM, FSPIE, FAIMBE, FIOMP, FACR is a Persian-American medical physicist. He is the Reed and Martha Rice Distinguished Professor of Radiology, and Professor of Medical Physics, Biomedical Engineering, Physics, and Electrical and Computer Engineering at Duke University. He serves as the Chief Imaging Physicist for Duke University Health System, the Director of the Carl E Ravin Advanced Imaging Laboratories and the Center for Virtual Imaging Trials (CVIT), and co-PI of one the five Centers of Excellence in Regulatory Science and Innovation (CERSI), Triangle CERSI. He is certified by the American Board of Radiology, recognized as a Distinguished Investigator by the Academy of Radiology Research, and awarded Fellow by five professional organization. He founded/co-founded the Duke Medical Physics Program, the Duke Imaging Physics Residency Program, the Duke Clinical Imaging Physics Group, the Center for Virtual Imaging Trials, and the Society of Directors of Academic Medical Physics Programs (SDAMPP). He has held senior leadership positions in the AAPM, SPIE, SDAMPP, and RSNA, including election to the presidency of the SEAAPM (2010-2011), SDAMPP (2011), and AAPM (2023).

Dr. Samei’s scientific expertise include x-ray imaging, theoretical imaging models, simulation methods, and experimental techniques in medical image formation, quantification, and perception.  His research aims to bridge the gap between scientific scholarship and clinical practice, in the meaningful realization of translational research, and in clinical processes that are informed by scientific evidence. He has advanced image quality and safety metrics and radiometrics that are clinically relevant and that can be used to design, optimize, and monitor interpretive and quantitative performance of imaging techniques. These have been implemented in advanced imaging performance characterization, procedural optimization, and clinical dose and quality analytics. His most recent research interests have been virtual clinical trial across a broad spectrum of oncologic, pulmonary, cardiac, and vascular diseases, and developing methodological advances that provide smart fusions of principle-informed and AI-based, data-informed approaches to scientific inquiry.

Dr. Samei has mentored over 140 trainees (graduate and postgraduate). He has >1400 scientific publications including >360 referred journal articles, ~600 conference presentations, and 4 books. Citations to his work is reflected in an h-index of 74 and a Weighted Relative Citation Ratio of 613. His laboratory of over 20 researchers has been supported continuously over two decades by 44 extramural grants, culminating in a NIH Program Project grant in 2021 to establish the national Center for Virtual Imaging Trials (CVIT), joining a small number of prominent Biomedical Technology Research Centers across the nation. In 2023, he, along with 3 other PIs, was awarded to lead one of five national Centers of Excellence in Regulatory Science and Innovation (Triangle CERSI) by the FDA.


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