Organ-based and DLP-based effective dose as representations of radiation risk in a population of 8946 patients with cumulative effective dose greater than 100 mSv

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2021-12-01

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Abstract

Purpose. Recent studies have shown that it is not uncommon for a patient to undergo multiple CT exams resulting in high cumulative dose above 100 mSv, the radiation risk associated with which is not negligible. The purpose of this study was to compare the estimated radiation risk in a large population of such cases based on effective dose to estimates of risk index including patient age.
Materials and Methods. This IRB-approved study included 8946 patients who underwent multiple CT exams over 5 years resulting in a cumulative effective dose over 100 mSv. Organ doses were estimated using Monte Carlo methods. DLP-based effective dose (Ek) and organ-dose based effective dose (EOD) were calculated following ICRP 102 and ICRP 103 publications. The organ-dose based risk index (RI) was calculated according to BEIR VII risk coefficients. A linear regression was applied to assess each metric’s dependency to RI, assumed to be the closest patient risk surrogate. The relative sensitivity of EOD and Ek to the estimated risk was calculated in six age groups (30 to 80 years old) in terms of a Risk Sensitivity Index (RSI) computed as a normalized fit slope by the ratio of the mean value of RI for each metric. Results. Across all patients, EOD for the 100 mSv+ cohort ranged between 100.2 and 1184.7 mSv, Ek between 54.1 and 1031.9 mSv, and RI between 152.9 and 7785.1 cancers per 105 patients. Per each age group, the fit R2 was <0.004 for the linear regression of Ek vs. RI and between 0.72 and 0.97 for EOD vs. RI implying that RI and EOD are linearly related. As anticipated, the fit slope increased with patient age. The RSI was <3.15×10-4 for Ek and ranged between 0.01 and 0.26 for EOD. Conclusion. For patient exposed to high cumulative dose (>100 mSv), care should be exercised to properly assess the risk figures and to draw risk predictions from metrics unrepresentative of population risk. Compared to effective dose drawn from DLP, effective dose based on organ doses provides a closer representation of patient and population risk, provided stratification by specific age groups. Clinical Relevance statement. When patients undergo recurring CT exams, the radiation induced risks should be carefully estimated using metrics that incorporate organ dose and patient age.

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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.

Madan Mohan Rehani

Adjunct Professor in the Department of Radiology
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 more than 1400 scientific publications including more than 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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