Motion artifacts in kidney stone imaging using single-source and dual-source dual-energy CT scanners: a phantom study

PURPOSE: Dual-energy computed tomography (DECT) has shown the capability of differentiating uric acid (UA) from non-UA stones with 90-100% accuracy. With the invention of dual-source (DS) scanners, both low- and high-energy images are acquired simultaneously. However, DECT can also be performed by sequential acquisition of both images on single-source (SS) scanners. The objective of this study is to investigate the effects of motion artifacts on stone classification using both SS-DECT and DS-DECT. METHODS: 114 kidney stones of different types and sizes were imaged on both DS-DECT and SS-DECT scanners with tube voltages of 80 and 140 kVp with and without induced motion. Postprocessing was conducted to create material-specific images from corresponding low- and high-energy images. The dual-energy ratio (DER) and stone material were determined and compared among different scans. RESULTS: For the motionless scans, all stones were correctly classified with SS-DECT, while two cystine stones were misclassified with DS-DECT. When motion was induced, 94% of the stones were misclassified with SS-DECT versus 11% with DS-DECT (P < 0.0001). Stone size was not a factor in stone misclassification under motion. Stone type was not a factor in stone misclassification under motion with SS-DECT, although with DS-DECT, cystine showed higher number of stone misclassification. CONCLUSIONS: Motion artifacts could result in stone misclassification in DECT. This effect is more pronounced in SS-DECT versus DS-DECT, especially if stones of different types lie in close proximity to each other. Further, possible misinterpretation of the number of stones (i.e., missing one, or thinking that there are two) in DS-DECT could be a potentially significant problem

Detection of different kidney stone types: an ex vivo comparison of ultrashort echo time MRI to reference standard CT

BACKGROUND AND PURPOSE: With the development of ultrashort echo time (UTE) sequences, it may now be possible to detect kidney stones by using magnetic resonance imaging (MRI). In this study, kidney stones of varying composition and sizes were imaged using both UTE MRI as well as the reference standard of computed tomography (CT), with different surrounding materials and scan setups. METHODS: One hundred and fourteen kidney stones were inserted into agarose and urine phantoms and imaged both on a dual-energy CT (DECT) scanner using a standard renal stone imaging protocol and on an MRI scanner using the UTE sequence with both head and body surface coils. A subset of the stones representing all composition types and sizes was then inserted into the collecting system of porcine kidneys and imaged in vitro with both CT and MRI. RESULTS: All of the stones were visible on both CT and MRI imaging. DECT was capable of differentiating between uric acid and nonuric acid stones. In MRI imaging, the choice of coil and large field of view (FOV) did not affect stone detection or image quality. The MRI images showed good visualization of the stones' shapes, and the stones' dimensions measured from MRI were in good agreement with the actual values (R(2)=0.886, 0.895, and 0.81 in the agarose phantom, urine phantom, and pig kidneys, respectively). The measured T2 relaxation times ranged from 4.2 to 7.5ms, but did not show significant differences among different stone composition types. CONCLUSIONS: UTE MRI compared favorably with the reference standard CT for imaging stones of different composition types and sizes using body surface coil and large FOV, which suggests potential usefulness of UTE MRI in imaging kidney stones in vivo

Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019.

The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019) provided systematic estimates of incidence, morbidity, and mortality to inform local and international efforts toward reducing cancer burden. To estimate cancer burden and trends globally for 204 countries and territories and by Sociodemographic Index (SDI) quintiles from 2010 to 2019. The GBD 2019 estimation methods were used to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life years (DALYs) in 2019 and over the past decade. Estimates are also provided by quintiles of the SDI, a composite measure of educational attainment, income per capita, and total fertility rate for those younger than 25 years. Estimates include 95% uncertainty intervals (UIs). In 2019, there were an estimated 23.6 million (95% UI, 22.2-24.9 million) new cancer cases (17.2 million when excluding nonmelanoma skin cancer) and 10.0 million (95% UI, 9.36-10.6 million) cancer deaths globally, with an estimated 250 million (235-264 million) DALYs due to cancer. Since 2010, these represented a 26.3% (95% UI, 20.3%-32.3%) increase in new cases, a 20.9% (95% UI, 14.2%-27.6%) increase in deaths, and a 16.0% (95% UI, 9.3%-22.8%) increase in DALYs. Among 22 groups of diseases and injuries in the GBD 2019 study, cancer was second only to cardiovascular diseases for the number of deaths, years of life lost, and DALYs globally in 2019. Cancer burden differed across SDI quintiles. The proportion of years lived with disability that contributed to DALYs increased with SDI, ranging from 1.4% (1.1%-1.8%) in the low SDI quintile to 5.7% (4.2%-7.1%) in the high SDI quintile. While the high SDI quintile had the highest number of new cases in 2019, the middle SDI quintile had the highest number of cancer deaths and DALYs. From 2010 to 2019, the largest percentage increase in the numbers of cases and deaths occurred in the low and low-middle SDI quintiles. The results of this systematic analysis suggest that the global burden of cancer is substantial and growing, with burden differing by SDI. These results provide comprehensive and comparable estimates that can potentially inform efforts toward equitable cancer control around the world.Funding/Support: The Institute for Health Metrics and Evaluation received funding from the Bill & Melinda Gates Foundation and the American Lebanese Syrian Associated Charities. Dr Aljunid acknowledges the Department of Health Policy and Management of Kuwait University and the International Centre for Casemix and Clinical Coding, National University of Malaysia for the approval and support to participate in this research project. Dr Bhaskar acknowledges institutional support from the NSW Ministry of Health and NSW Health Pathology. Dr Bärnighausen was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, which is funded by the German Federal Ministry of Education and Research. Dr Braithwaite acknowledges funding from the National Institutes of Health/ National Cancer Institute. Dr Conde acknowledges financial support from the European Research Council ERC Starting Grant agreement No 848325. Dr Costa acknowledges her grant (SFRH/BHD/110001/2015), received by Portuguese national funds through Fundação para a Ciência e Tecnologia, IP under the Norma Transitória grant DL57/2016/CP1334/CT0006. Dr Ghith acknowledges support from a grant from Novo Nordisk Foundation (NNF16OC0021856). Dr Glasbey is supported by a National Institute of Health Research Doctoral Research Fellowship. Dr Vivek Kumar Gupta acknowledges funding support from National Health and Medical Research Council Australia. Dr Haque thanks Jazan University, Saudi Arabia for providing access to the Saudi Digital Library for this research study. Drs Herteliu, Pana, and Ausloos are partially supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. Dr Hugo received support from the Higher Education Improvement Coordination of the Brazilian Ministry of Education for a sabbatical period at the Institute for Health Metrics and Evaluation, between September 2019 and August 2020. Dr Sheikh Mohammed Shariful Islam acknowledges funding by a National Heart Foundation of Australia Fellowship and National Health and Medical Research Council Emerging Leadership Fellowship. Dr Jakovljevic acknowledges support through grant OI 175014 of the Ministry of Education Science and Technological Development of the Republic of Serbia. Dr Katikireddi acknowledges funding from a NHS Research Scotland Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2), and the Scottish Government Chief Scientist Office (SPHSU17). Dr Md Nuruzzaman Khan acknowledges the support of Jatiya Kabi Kazi Nazrul Islam University, Bangladesh. Dr Yun Jin Kim was supported by the Research Management Centre, Xiamen University Malaysia (XMUMRF/2020-C6/ITCM/0004). Dr Koulmane Laxminarayana acknowledges institutional support from Manipal Academy of Higher Education. Dr Landires is a member of the Sistema Nacional de Investigación, which is supported by Panama’s Secretaría Nacional de Ciencia, Tecnología e Innovación. Dr Loureiro was supported by national funds through Fundação para a Ciência e Tecnologia under the Scientific Employment Stimulus–Institutional Call (CEECINST/00049/2018). Dr Molokhia is supported by the National Institute for Health Research Biomedical Research Center at Guy’s and St Thomas’ National Health Service Foundation Trust and King’s College London. Dr Moosavi appreciates NIGEB's support. Dr Pati acknowledges support from the SIAN Institute, Association for Biodiversity Conservation & Research. Dr Rakovac acknowledges a grant from the government of the Russian Federation in the context of World Health Organization Noncommunicable Diseases Office. Dr Samy was supported by a fellowship from the Egyptian Fulbright Mission Program. Dr Sheikh acknowledges support from Health Data Research UK. Drs Adithi Shetty and Unnikrishnan acknowledge support given by Kasturba Medical College, Mangalore, Manipal Academy of Higher Education. Dr Pavanchand H. Shetty acknowledges Manipal Academy of Higher Education for their research support. Dr Diego Augusto Santos Silva was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil Finance Code 001 and is supported in part by CNPq (302028/2018-8). Dr Zhu acknowledges the Cancer Prevention and Research Institute of Texas grant RP210042

Forecasting the effect of the change in timing of the ABR diagnostic radiology examinations: results of the ACR survey of practice leaders

The results of a survey sent to practice leaders in the ACR Practice of Radiology Environment Database show that the majority of responding groups will continue to hire recently trained residents and fellows even though they have been unable to take the final ABR diagnostic radiology certifying examination. However, a significant minority of private practice groups will not hire these individuals. The majority of private practices expect the timing change for the ABR certifying examinations to affect their groups' function. In contrast, the majority of academic medical school practices expect little or no impact. Residents and fellows should not expect work time off or protected time to study for the certifying examination or for their maintenance of certification examinations in the future

Cancer Incidence, Mortality, Years of Life Lost, Years Lived with Disability, and Disability-Adjusted Life Years for 29 Cancer Groups from 2010 to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019

Importance: The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019) provided systematic estimates of incidence, morbidity, and mortality to inform local and international efforts toward reducing cancer burden. Objective: To estimate cancer burden and trends globally for 204 countries and territories and by Sociodemographic Index (SDI) quintiles from 2010 to 2019. Evidence Review: The GBD 2019 estimation methods were used to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life years (DALYs) in 2019 and over the past decade. Estimates are also provided by quintiles of the SDI, a composite measure of educational attainment, income per capita, and total fertility rate for those younger than 25 years. Estimates include 95% uncertainty intervals (UIs). Findings: In 2019, there were an estimated 23.6 million (95% UI, 22.2-24.9 million) new cancer cases (17.2 million when excluding nonmelanoma skin cancer) and 10.0 million (95% UI, 9.36-10.6 million) cancer deaths globally, with an estimated 250 million (235-264 million) DALYs due to cancer. Since 2010, these represented a 26.3% (95% UI, 20.3%-32.3%) increase in new cases, a 20.9% (95% UI, 14.2%-27.6%) increase in deaths, and a 16.0% (95% UI, 9.3%-22.8%) increase in DALYs. Among 22 groups of diseases and injuries in the GBD 2019 study, cancer was second only to cardiovascular diseases for the number of deaths, years of life lost, and DALYs globally in 2019. Cancer burden differed across SDI quintiles. The proportion of years lived with disability that contributed to DALYs increased with SDI, ranging from 1.4% (1.1%-1.8%) in the low SDI quintile to 5.7% (4.2%-7.1%) in the high SDI quintile. While the high SDI quintile had the highest number of new cases in 2019, the middle SDI quintile had the highest number of cancer deaths and DALYs. From 2010 to 2019, the largest percentage increase in the numbers of cases and deaths occurred in the low and low-middle SDI quintiles. Conclusions and Relevance: The results of this systematic analysis suggest that the global burden of cancer is substantial and growing, with burden differing by SDI. These results provide comprehensive and comparable estimates that can potentially inform efforts toward equitable cancer control around the world.