Browsing by Author "Zhang, Anru"

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    A mathematical framework to quantitatively balance clinical and radiation risk in Computed Tomography
    (2021-12-01) Ria, Francesco; Zhang, Anru; Lerebours, Reginald; Erkanli, Alaattin; Solomon, justin; Marin, Daniele; Samei, Ehsan
    Purpose: Risk in medical imaging is a combination of radiation risk and clinical risk, which is largely driven by the effective diagnosis. While radiation risk has traditionally been the main focus of Computed Tomography (CT) optimization, such a goal cannot be achieved without considering clinical risk. The purpose of this study was to develop a comprehensive mathematical framework that considers both radiation and clinical risks based on the specific task, the investigated disease, and the interpretive performance (i.e., false positive and false negative rates), tested across a representative clinical CT population. Methods and Materials: The proposed mathematical framework defined the radiation risk to be a linear function of the radiation dose, the population prevalence of the disease, and the false positive rate. The clinical risk was defined to be a function of the population prevalence, the expected life-expectancy loss for an incorrect diagnosis, and the interpretative performance in terms of the AUC as a function of radiation dose. A Total Risk (TR) was defined as the sum of the radiation risk and the clinical risk. With IRB approval, the mathematical function was applied to a dataset of 80 adult CT studies investigating localized stage liver cancer (LLC) for a specific false positive rate of 5% reconstructed with both Filtered Back Projection (FBP) and Iterative Reconstruction (IR) algorithm. Linear mixed effects models were evaluated to determine the relationship between radiation dose and radiation risk and interpretative performance, respectively. Lastly, the analytical minimum of the TR curve was determined and reported. Results: TR is largely affected by clinical risk for low radiation dose whereas radiation risk is dominant at high radiation dose. Concerning the application to the LLC population, the median minimum risk in terms of mortality per 100 patients was 0.04 in FBP and 0.03 in IR images; the corresponding CTDIvol values were 38.5 mGy and 25.7 mGy, respectively. Conclusions: The proposed mathematical framework offers a complete quantitative description of risk in CT enabling a comprehensive risk-to-benefit assessment essential in the effective justification of radiological procedures and in the design of optimal clinical protocols. Clinical Relevance/Application: The quantification of both radiation and clinical risk using comparable units allows the calculation of the overall risk paving the road towards a comprehensive risk-to-benefit assessment in CT.
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    Clinical and radiation risk across one million patients in Computed Tomography: influence of age, size, and race
    (2023-11-26) Ria, Francesco; Lerebours, Reginald; Zhang, Anru; Erkanli, Alaattin; Abadi, Ehsan; SOLOMON, justin; Marin, Daniele; Samei, Ehsan
    Purpose. We recently developed a mathematical model to balance radiation risk and clinical risk, namely the risk of misdiagnosis due to insufficient image quality. In this work, we applied this model to a population of one million CT imaging cases to evaluate the risk stratification with different ages, sexes, and races. Materials and Methods. The demographics were informed by literature and census information simulating a clinical liver cancer population. The Total Risk (TR) was calculated as the linear combination of radiation risk and clinical risk. The model included factors for the radiation burden for different age and sex; the prevalence of the disease; the false positive rate; the expected life-expectancy loss for an incorrect diagnosis for different ages, sex, and race; and a typical false positive rate of 5%. It was assumed that each case received an average radiologist interpretative performance of 0.75 AUC for a hypothetical lesion without any changes in radiation dose beyond routine practice. We further, for each patient, simulated 2,000 imaging conditions with CTDIvol varying from 0.1 and 200 mGy with 0.1 mGy increments. Per each CTDIvol value, the anticipated AUC was calculated by applying the established asymptotic relationships between CTDIvol and image quality. The AUC distribution was then used to calculate the theoretical minimum total risk (TRmin) per each patient. Results. For the routine practice, the median theoretical total risk was estimated to be 0.058 deaths per 100 patients (range: 0.002 – 0.154) comprising of the median radiation risk of 0.009 (range: 0.001 – 0.069), and of the median clinical risk of 0.049 (range: 7.0x10-5 – 0.094). Considering the varying scanner output conditions, the median TRmin was 0.054 deaths per 100 patients for White male patients, 0.054 for Blacks, 0.057 for Hispanics, and 0.065 for Asians. For female patients, the median TRmin values were 0.049, 0.056, 0.054, and 0.061 deaths per 100 patients, respectively. Conclusion. For each demography condition, the clinical risk was found to largely outweigh the radiation risk by at least 500%. Total risk showed different stratifications with patient age and race. Clinical Relevance Statement. To optimize CT conditions for specific patients and/or population, both radiation risk and clinical risks should be all accounted for together with demographic information. We demonstrated a methodology that allows a complete depiction of total risk in CT, considering radiation and clinical risks at comparable units, and patient demographic.
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    Comparative Risk Assessment of Clinical and Radiation Risk across a Cohort of Patient and Individualized Risk Optimization
    (https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.16525, 2023-07-23) Ria, Francesco; Lerebours, Reginald; Zhang, Anru; Erkanli, Alaattin; Abadi, Ehsan; SOLOMON, justin; Marin, daniele; Samei, Ehsan
    Purpose Informed by a recent mathematical framework, we formulated an imaging strategy to balance interpretative performance-based clinical risk (i.e., false positive and false negative rates) and radiation risk as a risk-versus-risk assessment. The model was applied to a population of one million cases simulating a clinical liver cancer scenario. Moreover, a model was developed to predict individualized risk-versus-risk optimization. Methods The proposed model defined a Total Risk (TR) as the linear combination of radiation risk and clinical risk defined as functions of the radiation burden, the disease prevalence disease, the false positive rate, the expected life-expectancy loss for an incorrect diagnosis, and the radiologist interpretative performance (i.e., AUC). The mathematical framework was applied to a simulated dataset of 1,000,000 CT studies investigating localized stage liver cancer assuming a typical false positive rate of 5% and optimal imaging conditions (AUC=0.75). Demographic information was simulated according with literature and census data including male and female for different patient races (white, black, Asian, and Hispanic). Following BEIR-VII report, organ-specific radiation doses were used to calculate the radiation Risk Index per each patient. The model was then extended to predict the optimal scanner output associated with the TR for specific patients. Results Across all races and sexes, median radiation risk ranged between 0.008 and 0.012 number of deaths per 100 patients; median clinical risk ranged between 0.042 and 0.076; and medial total risk ranged between 0.010 and 0.088 deaths per 100 patients. The mathematical model was then generalized to estimate individualized optimal imaging condition minimizing TR. Conclusion A mathematical framework to describe total risk in CT was robustly tested in a simulated dataset of 1,000,000 CT studies. The results highlighted the dominance of clinical risk at typical CT examination dose levels. The generalization of the mathematical model allowed the prediction of individualized risk optimization.
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