Increased Disparities in Patients Diagnosed with Metastatic Lung Cancer Following Lung CT Screening in the United States

Ganesh, Ashwin; Katipally, Rohan; Pasquinelli, Mary; Feldman, Lawrence E.; Spiotto, Michael T.; Koshy, Matthew

doi:10.1016/j.cllc.2021.11.006

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…In the past year, analyses based on these large, collaborative data repositories have yielded insights into cancer disparities, as outlined here. While cancer screening programs are leading to better disease detection and im-proved outcome, this improvement may not be shared by racial minorities and those with lower educational attainment [20,21]. Race has also been shown to influence treatment recommendations, with Black patients less likely to be offered surgical resection for certain skull-base tumors [22].…”

Section: Big Data and Real-world Datamentioning

confidence: 99%

Clinical Informatics Approaches to Understand and Address Cancer Disparities

2022

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Objectives: Disparities in cancer incidence and outcomes across race, ethnicity, gender, socioeconomic status, and geography are well-documented, but their etiologies are often poorly understood and multifactorial. Clinical informatics can provide tools to better understand and address these disparities by enabling high-throughput analysis of multiple types of data. Here, we review recent efforts in clinical informatics to study and measure disparities in cancer. Methods: We carried out a narrative review of clinical informatics studies related to cancer disparities and bias published from 2018-2021, with a focus on domains such as real-world data (RWD) analysis, natural language processing (NLP), radiomics, genomics, proteomics, metabolomics, and metagenomics. Results: Clinical informatics studies that investigated cancer disparities across race, ethnicity, gender, and age were identified. Most cancer disparities work within clinical informatics used RWD analysis, NLP, radiomics, and genomics. Emerging applications of clinical informatics to understand cancer disparities, including proteomics, metabolomics, and metagenomics, were less well represented in the literature but are promising future research avenues. Algorithmic bias was identified as an important consideration when developing and implementing cancer clinical informatics techniques, and efforts to address this bias were reviewed. Conclusions: In recent years, clinical informatics has been used to probe a range of data sources to understand cancer disparities across different populations. As informatics tools become integrated into clinical decision-making, attention will need to be paid to ensure that algorithmic bias does not amplify existing disparities. In our increasingly interconnected medical systems, clinical informatics is poised to untap the full potential of multi-platform health data to address cancer disparities.

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Section: Big Data and Real-world Datamentioning

confidence: 99%