Disparities in the utilization of magnetic resonance imaging for prostate cancer detection: a population-based study

This study revealed increased MRI utilization over time, including among individuals 75 years of age and older. Racial disparities decreased, while wide urban-rural disparities remained. Targeted public health interventions should focus on geographical factors because urban or rural designations and US region were associated with the most prominent disparities. Future research should explore pathways contributing to these disparities by using a multidisciplinary approach, including geographical studies, to help eliminate health-care inequities.

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Institutions

Publication Details

Journal
JNCI Journal of the National Cancer Institute
Published
2024-09-23
DOI
https://doi.org/10.1093/jnci/djae234
Citations
11
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
4.45

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article

Disparities in the utilization of magnetic resonance imaging for prostate cancer detection: a population-based study

Scott W. Keith, Stephen J. Freedland, Christiane J. El Khoury, Amy L. Shaver et al.
11 citations
JNCI Journal of the National Cancer Institute
Radiomics and Machine Learning in Medical Imaging
4.45
article

Disparities in the utilization of magnetic resonance imaging for prostate cancer detection: a population-based study

Scott W. Keith, Stephen J. Freedland, Christiane J. El Khoury, Amy L. Shaver, Grace Lu‐Yao, Swapnil Sharma, Wm. Kevin Kelly, Krupa Gandhi, Nikita Nikita
article en
11 citations

Abstract

This study revealed increased MRI utilization over time, including among individuals 75 years of age and older. Racial disparities decreased, while wide urban-rural disparities remained. Targeted public health interventions should focus on geographical factors because urban or rural designations and US region were associated with the most prominent disparities. Future research should explore pathways contributing to these disparities by using a multidisciplinary approach, including geographical studies, to help eliminate health-care inequities.

JNCI Journal of the National Cancer InstituteVol. 117(2)
Cedars-Sinai Medical Center (US), Thomas Jefferson University (US), Stony Brook School (US), Sidney Kimmel Cancer Center (US), Durham VA Health Care System (US)
Pennsylvania Department of Health, California Department of Public Health, University of Southern California, National Institutes of Health, Centers for Disease Control and Prevention, University of California, San Francisco, National Cancer Institute
Openalex Percentile: Top 5%
Radiomics and Machine Learning in Medical Imaging
4.45
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