A comparison of the imaging appearance of breast cancer in African American women with non-Latina white women

Wang, Jieqi; Chang, Jen Jen; Liu, Ying; Bennett, Debbie L.; Poplack, Steven P.

doi:10.1016/j.clinimag.2022.11.004

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…Breast density is known to depend on age ( 10 ), which can in turn impact mammographic imaging. Race and ethnicity have been shown to influence both brain ( 11 ) and breast imaging ( 12 ). Aside from anatomic differences, sex can also influence tumor biology ( 13 ); thus, cancer imaging can be sex dependent ( 14 ).…”

Section: Introductionmentioning

confidence: 99%

Disparities in the Demographic Composition of The Cancer Imaging Archive

Dulaney,

Virostko

2024

Radiology: Imaging Cancer

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Purpose To characterize the demographic distribution of The Cancer Imaging Archive (TCIA) studies and compare them with those of the U.S. cancer population. Materials and Methods In this retrospective study, data from TCIA studies were examined for the inclusion of demographic information. Of 189 studies in TCIA up until April 2023, a total of 83 human cancer studies were found to contain supporting demographic data. The median patient age and the sex, race, and ethnicity proportions of each study were calculated and compared with those of the U.S. cancer population, provided by the Surveillance, Epidemiology, and End Results Program and the Centers for Disease Control and Prevention U.S. Cancer Statistics Data Visualizations Tool. Results The median age of TCIA patients was found to be 6.84 years lower than that of the U.S. cancer population ( P = .047) and contained more female than male patients (53% vs 47%). American Indian and Alaska Native, Black or African American, and Hispanic patients were underrepresented in TCIA studies by 47.7%, 35.8%, and 14.7%, respectively, compared with the U.S. cancer population. Conclusion The results demonstrate that the patient demographics of TCIA data sets do not reflect those of the U.S. cancer population, which may decrease the generalizability of artificial intelligence radiology tools developed using these imaging data sets. Keywords: Ethics, Meta-Analysis, Health Disparities, Cancer Health Disparities, Machine Learning, Artificial Intelligence, Race, Ethnicity, Sex, Age, Bias Published under a CC BY 4.0 license. See also the commentary by Miles and Porras in this issue

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Section: Introductionmentioning

confidence: 99%

Disparities in the Demographic Composition of The Cancer Imaging Archive

Dulaney,

Virostko

2024

Radiology: Imaging Cancer

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Radiologic imaging biomarkers in triple-negative breast cancer: a literature review about the role of artificial intelligence and the way forward

Bhalla,

Xiao,

Luna

et al. 2024

BJR|Artificial Intelligence

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Breast cancer is one of the most common and deadly cancers in women. Triple-negative breast cancer (TNBC) accounts for approximately 10-15% of breast cancer diagnoses and is an aggressive molecular breast cancer subtype associated with important challenges in its diagnosis, treatment, and prognostication. This poses an urgent need for developing more effective and personalized imaging biomarkers for TNBC. Toward this direction, artificial intelligence (AI) for radiologic imaging holds a prominent role, leveraging unique advantages of radiologic breast images, being used routinely for TNBC diagnosis, staging, and treatment planning, and offering high-resolution whole-tumor visualization, combined with the immense potential of AI to elucidate anatomical and functional properties of tumors that may not be easily perceived by the human eye. In this review, we synthesize the current state-of-the-art radiologic imaging applications of AI in assisting TNBC diagnosis, treatment, and prognostication. Our goal is to provide a comprehensive overview of radiomic and deep-learning-based AI developments and their impact on advancing TNBC management over the last decade (2013-2024). For completeness of the review, we start with a brief introduction of AI, radiomics and deep learning. Next, we focus on clinically relevant AI-based diagnostic, predictive and prognostic models for radiologic breast images evaluated in TNBC. We conclude with opportunities and future directions for AI towards advancing diagnosis, treatment response predictions and prognostic evaluations for TNBC.

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A comparison of the imaging appearance of breast cancer in African American women with non-Latina white women

Cited by 2 publications

References 32 publications

Disparities in the Demographic Composition of The Cancer Imaging Archive

Disparities in the Demographic Composition of The Cancer Imaging Archive

Radiologic imaging biomarkers in triple-negative breast cancer: a literature review about the role of artificial intelligence and the way forward

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