Racial Disparities in Lung Cancer Diagnosis and Treatment: A Single Center, Retrospective Study in Central Indiana

Background/Objective: Lung cancer is the second most common cancer and the leading cause of death from cancer in the United States. However, there is a disparity in incidence and mortality between African Americans and Caucasians. This study aims to analyze factors that could describe this difference, such as treatment, socioeconomic, or behavioral differences using information from an Indiana University Simon Cancer Center (IUSCC) lung cancer registry. We hypothesized that African Americans will have a higher lung cancer stage at diagnosis and mortality, associated with less timely, stage-appropriate treatment. Methods: Using data collected from patients diagnosed with lung cancer at IUSCC from 2000-2016, we compared racial differences in diagnoses and subsequent management. Patients were categorized by race and clinical stage at diagnosis. Further categorization by sex, vital status, age at diagnosis, time from diagnosis to treatment and death, tobacco use, surgery, chemotherapy, insurance coverage, and histology was performed. We determined the rates of surgery or chemotherapy by stage at diagnosis. Statistical analyses are by student t-test or 2-way ANOVA. Results: African Americans were younger than Caucasians at lung cancer diagnosis (average 63.4 vs. 61.2 years p-value < 0.001). African American race was associated with a longer time from diagnosis to treatment (36.4 vs. 32.1 days, p=0.023) and shorter time from diagnosis to death (475.1 vs. 623.7 days, p=0.001). The data suggests that African Americans have a later stage at diagnosis, are more likely to be uninsured and less likely to be covered by private insurance. The data suggests African Americans have a lower rate of surgery (Stages 1-3) and chemotherapy (Stages 3B and 4). Conclusion and Potential Impact: This data suggests racial differences in lung cancer diagnosis, treatment and outcomes. Future analyses will focus on multiple comparisons to determine possible impacts of socioeconomic and environmental factors on these outcomes at IUSCC and other university-affiliated health care systems.

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The impact of ethnicity and insurance status on stage of cancer at diagnosis and overall survival of breast cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2011.29.27_suppl.154 ◽

2011 ◽

Vol 29 (27_suppl) ◽

pp. 154-154

Author(s):

M. Omaira ◽

M. Mozayen ◽

R. Mushtaq ◽

K. Katato

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

African Americans ◽

Private Insurance ◽

Ethnic Disparities ◽

The United States ◽

Insurance Status ◽

Advanced Stage ◽

Stage At Diagnosis ◽

The Impact

154 Background: Major advances in early diagnosis and treatment of breast cancer (BC) have been achieved with significant declines in mortality. However, not all segments of the United States population have experienced equal benefits from this progress. Though ethnic disparities in BC outcome have been attributed to lack of adequate health insurance, the differences in outcome when insurance and socioeconomic status are similar still exist. We elected to examine the effect of insurance status at diagnosis, and whether race is an independent risk of poor outcome in a population from a community-based cancer database. Methods: A retrospective study on BC among patients aged 18 to 64 years were identified, between 1993 and 2005, using data from the Tumor Registry at Hurley Medical Center in Flint, Michigan. Patient’s characteristics included age, race, stage at diagnosis, and primary payer. Insurance status was classified as uninsured/Medicaid, private insurance, and Medicare disability (Medicare under age 65). The 5-year overall survival (OS) was calculated, in respect to patient ethnicity, and compared between the three insurance groups using Fisher’s exact test. Results: A total of 779 patients have been identified with diagnosis of BC. 147 patients were excluded due to incomplete data. 632 patients were analyzed. African Americans were 228 (36%), Caucasians 391 (62%), and other ethnicities 13 (2%). Mean age at diagnosis was (49.21) for African Americans versus (51.35) for Caucasians (p = 0.002). African Americans were more likely to present at advanced stage (III, IV) than Caucasians (17% versus 10%, p = 0.017). However, this difference was not statistically significant when adjusting for insurance status. Although both ethnicities had similar OS in respect of their insurance group, patients with Medicaid/uninsured had significantly lower OS compared to patients with Medicare disability (p = 0.006) and private insurance (p < 0.0001) respectively. Conclusions: Uninsured/Medicaid patients with breast cancer have worse outcome when compared to patients with Medicare or private insurance. Ethnicity is not an independent risk factor of advanced stage at diagnosis and poorer outcome.

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Racial differences in the perception of lung cancer: Data from the 2005 Health Information National Trends Survey (HINTS)

Journal of Clinical Oncology ◽

10.1200/jco.2007.25.18_suppl.6556 ◽

2007 ◽

Vol 25 (18_suppl) ◽

pp. 6556-6556

Author(s):

C. S. Lathan ◽

C. Okechukwu ◽

B. F. Drake ◽

G. Bennett

Keyword(s):

Lung Cancer ◽

African Americans ◽

Racial Differences ◽

Lung Cancer Mortality ◽

Weighting Method ◽

Cancer Data ◽

Black And White ◽

Incidence And Mortality ◽

Nationally Representative ◽

White Patients

6556 Background: Black men have the highest rates of lung cancer incidence and mortality in the US, and yet continue to obtain treatment at lower rates than White patients. Racial differences in the perception of lung cancer in the population could contribute to racial disparities in seeking timely treatment. Methods: Data are from the 2005 HINTS survey. Sample design was random digit dialing of listed telephone exchanges in US. Complete interviews were conducted on 5491 adults, of which 1872 respondents were assigned to receive questions pertaining to lung cancer. All analyses were conducted on this subset of respondents. SAS callable SUDAAN was used to calculate χ2 tests and perform logistic regression analyses to model racial differences in perceptions of lung cancer. All estimates were weighted to be nationally representative of US population; jack knife weighting method was used for parameter estimation. Results: Black and White patients shared many of the same beliefs about lung cancer mortality, and etiology. African Americans were more likely than Whites to agree that its hard to follow recommendations about preventing lung cancer (OR 2.05 1.19–3.53 95% CI), to avoid evaluation for lung cancer due to fear of having the disease (OR 3.32 1.84–5.98 95% CI), and to believe that patients with lung cancer would have pain or other symptoms before diagnosis (OR 2.20 1.27–3.79 95% CI). Conclusions: African Americans are more likely to hold beliefs about lung cancer that could interfere with prevention and treatment of lung cancer. No significant financial relationships to disclose. [Table: see text]

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Beyond the Lung Cancer Diagnosis: Leveraging the Oncology Clinic Setting for Actively Smoking Family Members

Case Medical Research ◽

10.31525/ct1-nct03966807 ◽

2019 ◽

Author(s):

Keyword(s):

Lung Cancer ◽

Cancer Diagnosis ◽

Family Members ◽

Clinic Setting ◽

Lung Cancer Diagnosis

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Computer-Aided Lung Cancer Diagnosis Approaches Based on Deep Learning

Journal of Computer-Aided Design & Computer Graphics ◽

10.3724/sp.j.1089.2018.16919 ◽

2018 ◽

Vol 30 (1) ◽

pp. 90 ◽

Cited By ~ 3

Author(s):

Peng Zhang ◽

Xinnan Xu ◽

Hongwei Wang ◽

Yuanli Feng ◽

Haozhe Feng ◽

...

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Cancer Diagnosis ◽

Lung Cancer Diagnosis ◽

Computer Aided

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Integration of Anatomic and Pathogenetic Bases for Early Lung Cancer Diagnosis

10.21236/ada468685 ◽

2007 ◽

Author(s):

Wei Qian

Keyword(s):

Lung Cancer ◽

Cancer Diagnosis ◽

Lung Cancer Diagnosis ◽

Early Lung Cancer

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Biased Lung Cancer Risk Perceptions: Smokers are Misinformed

Jahrbücher für Nationalökonomie und Statistik ◽

10.1515/jbnst-2018-0017 ◽

2018 ◽

Vol 238 (5) ◽

pp. 395-421 ◽

Cited By ~ 1

Author(s):

Nicolas R. Ziebarth

Keyword(s):

Lung Cancer ◽

Cancer Risk ◽

Cancer Diagnosis ◽

Mortality Risk ◽

Risk Perceptions ◽

Lung Cancer Risk ◽

Lifetime Risk ◽

Lung Cancer Diagnosis ◽

Cancer Risk Perceptions ◽

Overall Mortality

Abstract This paper empirically investigates biased beliefs about the risks of smoking. First, it confirms the established tendency of people to overestimate the lifetime risk of a smoker to contract lung cancer. In this paper’s survey, almost half of all respondents overestimate this risk. However, 80% underestimate lung cancer deadliness. In reality, less than one in five patients survive five years after a lung cancer diagnosis. Due to the broad underestimation of the lung cancer deadliness, the lifetime risk of a smoker to die of lung cancer is underestimated by almost half of all respondents. Smokers who do not plan to quit are significantly more likely to underestimate this overall mortality risk.

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AKT question relating to lung cancer diagnosis

InnovAiT Education and inspiration for general practice ◽

10.1177/17557380211014008s ◽

2021 ◽

Vol 14 (7) ◽

pp. e74-e74

Keyword(s):

Lung Cancer ◽

Cancer Diagnosis ◽

Lung Cancer Diagnosis

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An Efficient DA-Net Architecture for Lung Nodule Segmentation

Mathematics ◽

10.3390/math9131457 ◽

2021 ◽

Vol 9 (13) ◽

pp. 1457

Author(s):

Muazzam Maqsood ◽

Sadaf Yasmin ◽

Irfan Mehmood ◽

Maryam Bukhari ◽

Mucheol Kim

Keyword(s):

Computed Tomography ◽

Lung Cancer ◽

Ct Scan ◽

Cancer Diagnosis ◽

Lung Nodule ◽

Search Space ◽

Lung Nodules ◽

Suggested Model ◽

Lung Cancer Diagnosis ◽

Segmentation Framework

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.

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