Use of empiric methods to inform prostate cancer health disparities: Comparison of neighborhood-wide association study "hits" in black and white men

Comparison of Medical Research Abstracts Written by Surgical Trainees and Senior Surgeons or Generated by Large Language Models

Importance

Artificial intelligence (AI) has permeated academia, especially OpenAI Chat Generative Pretrained Transformer (ChatGPT), a large language model. However, little has been reported on its use in medical research.

Objective

To assess a chatbot's capability to generate and grade medical research abstracts.

Design, Setting, and Participants

In this cross-sectional study, ChatGPT versions 3.5 and 4.0 (referred to as chatbot 1 and chatbot 2) were coached to generate 10 abstracts by providing background literature, prompts, analyzed data for each topic, and 10 previously presented, unassociated abstracts to serve as models. The study was conducted between August 2023 and February 2024 (including data analysis).

Exposure

Abstract versions utilizing the same topic and data were written by a surgical trainee or a senior physician or generated by chatbot 1 and chatbot 2 for comparison. The 10 training abstracts were written by 8 surgical residents or fellows, edited by the same senior surgeon, at a high-volume hospital in the Southeastern US with an emphasis on outcomes-based research. Abstract comparison was then based on 10 abstracts written by 5 surgical trainees within the first 6 months of their research year, edited by the same senior author.

Main Outcomes and Measures

The primary outcome measurements were the abstract grades using 10- and 20-point scales and ranks (first to fourth). Abstract versions by chatbot 1, chatbot 2, junior residents, and the senior author were compared and judged by blinded surgeon-reviewers as well as both chatbot models. Five academic attending surgeons from Denmark, the UK, and the US, with extensive experience in surgical organizations, research, and abstract evaluation served as reviewers.

Results

Surgeon-reviewers were unable to differentiate between abstract versions. Each reviewer ranked an AI-generated version first at least once. Abstracts demonstrated no difference in their median (IQR) 10-point scores (resident, 7.0 [6.0-8.0]; senior author, 7.0 [6.0-8.0]; chatbot 1, 7.0 [6.0-8.0]; chatbot 2, 7.0 [6.0-8.0]; P = .61), 20-point scores (resident, 14.0 [12.0-7.0]; senior author, 15.0 [13.0-17.0]; chatbot 1, 14.0 [12.0-16.0]; chatbot 2, 14.0 [13.0-16.0]; P = .50), or rank (resident, 3.0 [1.0-4.0]; senior author, 2.0 [1.0-4.0]; chatbot 1, 3.0 [2.0-4.0]; chatbot 2, 2.0 [1.0-3.0]; P = .14). The abstract grades given by chatbot 1 were comparable to the surgeon-reviewers' grades. However, chatbot 2 graded more favorably than the surgeon-reviewers and chatbot 1. Median (IQR) chatbot 2-reviewer grades were higher than surgeon-reviewer grades of all 4 abstract versions (resident, 14.0 [12.0-17.0] vs 16.9 [16.0-17.5]; P = .02; senior author, 15.0 [13.0-17.0] vs 17.0 [16.5-18.0]; P = .03; chatbot 1, 14.0 [12.0-16.0] vs 17.8 [17.5-18.5]; P = .002; chatbot 2, 14.0 [13.0-16.0] vs 16.8 [14.5-18.0]; P = .04). When comparing the grades of the 2 chatbots, chatbot 2 gave higher median (IQR) grades for abstracts than chatbot 1 (resident, 14.0 [13.0-15.0] vs 16.9 [16.0-17.5]; P = .003; senior author, 13.5 [13.0-15.5] vs 17.0 [16.5-18.0]; P = .004; chatbot 1, 14.5 [13.0-15.0] vs 17.8 [17.5-18.5]; P = .003; chatbot 2, 14.0 [13.0-15.0] vs 16.8 [14.5-18.0]; P = .01).

Conclusions and Relevance

In this cross-sectional study, trained chatbots generated convincing medical abstracts, undifferentiable from resident or senior author drafts. Chatbot 1 graded abstracts similarly to surgeon-reviewers, while chatbot 2 was less stringent. These findings may assist surgeon-scientists in successfully implementing AI in medical research.

Patient-Derived Conditionally Reprogrammed Cells in Prostate Cancer Research

Prostate cancer (PCa) remains a leading cause of mortality among American men, with metastatic and recurrent disease posing significant therapeutic challenges due to a limited comprehension of the underlying biological processes governing disease initiation, dormancy, and progression. The conventional use of PCa cell lines has proven inadequate in elucidating the intricate molecular mechanisms driving PCa carcinogenesis, hindering the development of effective treatments. To address this gap, patient-derived primary cell cultures have been developed and play a pivotal role in unraveling the pathophysiological intricacies unique to PCa in each individual, offering valuable insights for translational research. This review explores the applications of the conditional reprogramming (CR) cell culture approach, showcasing its capability to rapidly and effectively cultivate patient-derived normal and tumor cells. The CR strategy facilitates the acquisition of stem cell properties by primary cells, precisely recapitulating the human pathophysiology of PCa. This nuanced understanding enables the identification of novel therapeutics. Specifically, our discussion encompasses the utility of CR cells in elucidating PCa initiation and progression, unraveling the molecular pathogenesis of metastatic PCa, addressing health disparities, and advancing personalized medicine. Coupled with the tumor organoid approach and patient-derived xenografts (PDXs), CR cells present a promising avenue for comprehending cancer biology, exploring new treatment modalities, and advancing precision medicine in the context of PCa. These approaches have been used for two NCI initiatives (PDMR: patient-derived model repositories; HCMI: human cancer models initiatives).

Effect of neighborhood and individual-level socioeconomic factors on breast cancer screening adherence in a multi-ethnic study

BACKGROUND

Although mammography can significantly reduce breast cancer mortality, many women do not receive their annual breast cancer screening. Differences in screening adherence exist by race/ethnicity, socioeconomic status (SES), and insurance status. However, more detailed investigations into the impact of neighborhood disadvantage and access to resources on screening adherence are lacking.

METHODS

We comprehensively examined the effect of individual social, economic, and demographic factors (n = 34 variables), as well as neighborhood level SES (nSES) indicators (n = 10 variables) on breast cancer screening adherence across a multi-ethnic population (n = 472). In this cross-sectional study, participants were surveyed from 2017 to 2018. The data was analyzed using univariate regression and LASSO for variable reduction. Significant predictors were carried forward into final multivariable mixed-effect logistic regression models where odds ratios (OR), 95% confidence intervals and p-values were reported.

RESULTS

Nineteen percent of participants were non-adherent to breast screening guidelines. Race/ethnicity was not associated with adherence; however, increasing age (OR = 0.97, 95%CI = 0.95-0.99, p = 0.01), renting a home (OR = 0.53, 95%CI = 0.30-0.94, p = 0.04), food insecurity (OR 0.46, 95%CI = 0.22-0.94, p = 0.01), and overcrowding (OR = 0.58, 95% CI = 0.32-0.94, p = 0.01) were significantly associated with lower breast cancer screening adherence.

CONCLUSION

Socioeconomic indicators at the individual and neighborhood levels impact low breast cancer screening adherence and may help to inform future screening interventions.

Influence of Neighborhood Social and Natural Environment on Prostate Tumor Histology in a Cohort of Male Health Professionals

Adverse neighborhood social and natural (green space) environments may contribute to the etiology of prostate cancer (CaP), but mechanisms are unclear. We examined associations between neighborhood environment and prostate intratumoral inflammation in 967 men diagnosed with CaP with available tissue samples from 1986-2009 in the Health Professionals Follow-up Study. Exposures were linked to work or residential addresses in 1988. We estimated indices of neighborhood socioeconomic status (nSES) and segregation (Index of Concentration at the Extremes (ICE)) using US Census tract-level data. Surrounding greenness was estimated using seasonal averaged Normalized Difference Vegetation Index (NDVI) data. Surgical tissue underwent pathological review for acute and chronic inflammation, corpora amylacea, and focal atrophic lesions. Adjusted odds ratios (aORs) for inflammation (ordinal) and focal atrophy (binary) were estimated using logistic regression. No associations were observed for acute or chronic inflammation. Each interquartile-range increase in NDVI within 1,230 m of the participant's work or home address (aOR = 0.74, 95% confidence interval (CI): 0.59, 0.93), in ICE-income (aOR = 0.79, 95% CI: 0.61, 1.04), and in ICE-race/income (aOR = 0.79, 95% CI: 0.63, 0.99) was associated with lower odds of postatrophic hyperplasia. Interquartile-range increases in nSES (aOR = 0.76, 95% CI: 0.57, 1.02) and ICE-race/income (aOR = 0.73, 95% CI: 0.54, 0.99) were associated with lower odds of tumor corpora amylacea. Histopathological inflammatory features of prostate tumors may be influenced by neighborhood.

The role of area-level socioeconomic disadvantage in racial disparities in cancer incidence in metropolitan Detroit

BACKGROUND

Neighborhood deprivation is associated with both race and cancer incidence, but there is a need to better understand the effect of structural inequities on racial cancer disparities. The goal of this analysis was to evaluate the relationship between a comprehensive measure of neighborhood-level social disadvantage and cancer incidence within the racially diverse population of metropolitan Detroit.

METHODS

We estimated breast, colorectal, lung, and prostate cancer incidence rates using Metropolitan Detroit Cancer Surveillance System and US decennial census data. Neighborhood socioeconomic disadvantage was measured by the Area Deprivation Index (ADI) using Census Bureau's American Community Survey data at the Public Use Microdata Areas (PUMA) level. Associations between ADI at time of diagnosis and cancer incidence were estimated using Poisson mixed-effects models adjusting for age and sex. Attenuation of race-incidence associations by ADI was quantified using the "mediation" package in R.

RESULTS

ADI was inversely associated with incidence of breast cancer for both non-Hispanic White (NHW) and non-Hispanic Black (NHB) women (NHW: per-quartile RR = 0.92, 95% CI 0.88-0.96; NHB: per-quartile RR = 0.94, 95% CI 0.91-0.98) and with prostate cancer incidence only for NHW men (per-quartile RR = 0.94, 95% CI 0.90-0.97). ADI was positively associated with incidence of lung cancer for NHWs and NHBs (NHW: per-quartile RR = 1.12, 95% CI 1.04-1.21; NHB: per-quartile RR = 1.37, 95% CI 1.25-1.51) and incidence of colorectal cancer (CRC) only among NHBs (per-quartile RR = 1.11, 95% CI 1.02-1.21). ADI significantly attenuated the relationship between race and hormone receptor positive, HER2-negative breast cancer (proportion attenuated = 8.5%, 95% CI 4.1-16.6%) and CRC cancer (proportion attenuated = 7.3%, 95% CI 3.7 to 12.8%), and there was a significant interaction between race and ADI for lung (interaction RR = 1.22, p < 0.0001) and prostate cancer (interaction RR = 1.09, p = 0.00092).

CONCLUSIONS

Area-level socioeconomic disadvantage is associated with risk of common cancers in a racially diverse population and plays a role in racial differences in cancer incidence.

Systematic review of neighborhood socioeconomic indices studied across the cancer control continuum

BACKGROUND

There is extensive interest in understanding how neighborhood socioeconomic status (nSES) may affect cancer incidence or survival. However, variability regarding items included and approaches used to form a composite nSES index presents challenges in summarizing overall associations with cancer. Given recent calls for standardized measures of neighborhood sociodemographic effects in cancer disparity research, the objective of this systematic review was to identify and compare existing nSES indices studied across the cancer continuum (incidence, screening, diagnosis, treatment, survival/mortality) and summarize associations by race/ethnicity and cancer site to inform future cancer disparity studies.

METHODS

Using PRISMA guidelines, peer-reviewed articles published between 2010 and 2019 containing keywords related to nSES and cancer were identified in PubMed.

RESULTS

Twenty-four nSES indices were identified from 75 studies. In general, findings indicated a significant association between nSES and cancer outcomes (n = 64/75 studies; 85.33%), with 42/64 (65.63%) adjusting for highly-correlated individual SES factors (e.g., education). However, the direction of association differed by cancer site, race/ethnicity, and nSES index.

CONCLUSIONS

This review highlights several methodologic and conceptual issues surrounding nSES measurement and potential associations with cancer disparities. Recommendations pertaining to the selection of nSES measures are provided, which may help inform disparity-related disease processes and improve the identification of vulnerable populations in need of intervention.

Variation in Cancer Incidence Rates Among Non-Hispanic Black Individuals Disaggregated by Nativity and Birthplace, 2005-2017: A Population-Based Cancer Registry Analysis

Objectives

Compared to other racial and ethnic groups, little to no disaggregated cancer incidence data exist for subgroups of non-Hispanic Blacks (NHBs), despite heterogeneity in sociodemographic characteristics and cancer risk factors within this group. Our objective was to examine age-adjusted cancer incidence by nativity and birthplace among NHB cancer cases diagnosed in New Jersey.

Methods

Race, ethnicity, and birthplace data from the New Jersey State Cancer Registry were used to classify NHB cancer cases diagnosed between 2005-2017. Thirteen waves of population estimates (by county, nativity, gender, age-group) were derived from the American Community Survey using Integrated Public-Use Microdata to approximate yearly demographics. Age-adjusted cancer incidence rates (overall and by site) by birthplace were generated using SEER*Stat 8.3.8. Bivariate associations were assessed using chi-square and Fisher’s exact tests. Trend analyses were performed using Joinpoint 4.7.

Results

Birthplace was available for 62.3% of the 71,019 NHB cancer cases. Immigrants represented 12.3%, with African-born, Haitian-born, Jamaican-born, ‘other-Caribbean-born’, and ‘other-non-American-born’ accounting for 18.5%, 17.7%, 16.5%, 10.6%, and 36.8%, respectively. Overall, age-adjusted cancer incidence rates were lower for NHB immigrants for all sites combined and for several of the top five cancers, relative to American-born NHBs. Age-adjusted cancer incidence was lower among immigrant than American-born males (271.6 vs. 406.8 per 100,000) and females (191.9 vs. 299.2 per 100,000). Age-adjusted cancer incidence was lower for Jamaican-born (114.6 per 100,000) and other-Caribbean-born females (128.8 per 100,000) than African-born (139.4 per 100,000) and Haitian-born females (149.9 per 100,000). No significant differences in age-adjusted cancer incidence were observed by birthplace among NHB males. Age-adjusted cancer incidence decreased for all sites combined from 2005-2017 among American-born males, immigrant males, and American-born females, while NHB immigrant female rates remained relatively stable.

Conclusions

There is variation in age-adjusted cancer incidence rates across NHB subgroups, highlighting the need for more complete birthplace information in population-based registries to facilitate generating disaggregated cancer surveillance statistics by birthplace. This study fills a knowledge gap of critical importance for understanding and ultimately addressing cancer inequities.

Population-Level Patterns of Prostate Cancer Occurrence: Disparities in Virginia

Prostate cancer is the most common cancer and the second leading cause of cancer-related deaths among men in the United States. In Virginia, which is a representative, ethnically diverse state of more than 8 million people that was established nearly 400 years ago, prostate cancer has the highest rate of new detection for any type of cancer. All men are at risk of developing prostate cancer regardless of demographics, but some men have an increased mortality risk due to cancer metastasis. Notably, one in five African American men will be diagnosed with prostate cancer in their lifetime and they have the highest prostate cancer mortality rate of any ethnic group in the United States, including Virginia. A person's genetic profile and family history are important biological determinants of prostate cancer risk, but modifiable environmental factors (e.g., pollution) appear to be correlated with patterns of disease prevalence and risk. In this review, we examine current perspectives on population-level spatial patterns of prostate cancer in Virginia. For context, recent, publicly available data from the Centers for Disease Control and Prevention are highlighted and presented in spatial format. In addition, we explore possible co-morbidities of prostate cancer that may have demographic underpinnings highlighted in recent health disparity studies.

Calling Attention to the Role of Race-Driven Societal Determinants of Health on Aggressive Tumor Biology: A Focus on Black Americans

Blacks have the highest incidence and mortality from most cancers. The reasons for these disparities remain unclear. Blacks are exposed to adverse social determinants because of historic and contemporary racist polices; however, how these determinants affect the disparities that Blacks experience is understudied. As a result of discriminatory community policies, like redlining, Blacks have higher exposure to air pollution and neighborhood deprivation. Studies investigating how these factors affect tumor biology are emerging. We highlight the literature that connects racism-related community exposure to the tumor biology in breast, lung, prostate, and colorectal cancer. Further investigations that clarify the link between adverse social determinants that result from systemic racism and aggressive tumor biology are required if health equity is to be achieved. Without recognition that racism is a public health risk with carcinogenic impact, health care delivery and cancer care will never achieve excellence. In response, health systems ought to establish corrective actions to improve Black population health and bring medical justice to marginalized racialized groups.

Effect of Neighborhood and Individual-Level Socioeconomic Factors on Colorectal Cancer Screening Adherence

Despite the effectiveness of screenings in reducing colorectal cancer (CRC) mortality, ~25% of US adults do not adhere to screening guidelines. Prior studies associate socioeconomic status (SES) with low screening adherence and suggest that neighborhood deprivation can influence CRC outcomes. We comprehensively investigated the effect of neighborhood SES circumstances (nSES), individual SES, and race/ethnicity on adherence to CRC screening in a multiethnic cross-sectional study. Participant surveys assessing 32 individual-level socioeconomic and healthcare access measures were administered from 2017 to 2018. Participant data were joined with nine nSES measures from the US Census at the census tract level. Univariate, LASSO, and multivariable mixed-effect logistic regression models were used for variable reduction and evaluation of associations. The total study population included 526 participants aged 50–85; 29% of participants were non-adherent. In the final multivariable model, age (p = 0.02) and Non-Hispanic Black race (p = 0.02) were associated with higher odds of adherence. Factors associated with lower adherence were home rental (vs. ownership) (p = 0.003), perception of low healthcare quality (p = 0.006), no routine checkup within two years (p = 0.002), perceived discrimination (p = 0.02), and nSES deprivation (p = 0.02). After comprehensive variable methods were applied, socioeconomic indicators at the neighborhood and individual level were found to contribute to low CRC screening adherence.

The Impact of African Ancestry on Prostate Cancer Disparities in the Era of Precision Medicine

Prostate cancer disproportionately affects men of African ancestry at nearly twice the rate of men of European ancestry despite the advancement of treatment strategies and prevention. In this review, we discuss the underlying causes of these disparities including genetics, environmental/behavioral, and social determinants of health while highlighting the implications and challenges that contribute to the stark underrepresentation of men of African ancestry in clinical trials and genetic research studies. Reducing prostate cancer disparities through the development of personalized medicine approaches based on genetics will require a holistic understanding of the complex interplay of non-genetic factors that disproportionately exacerbate the observed disparity between men of African and European ancestries.

The effect of neighborhood social environment on prostate cancer development in black and white men at high risk for prostate cancer

INTRODUCTION

Neighborhood socioeconomic (nSES) factors have been implicated in prostate cancer (PCa) disparities. In line with the Precision Medicine Initiative that suggests clinical and socioenvironmental factors can impact PCa outcomes, we determined whether nSES variables are associated with time to PCa diagnosis and could inform PCa clinical risk assessment.

MATERIALS AND METHODS

The study sample included 358 high risk men (PCa family history and/or Black race), aged 35-69 years, enrolled in an early detection program. Patient variables were linked to 78 nSES variables (employment, income, etc.) from previous literature via geocoding. Patient-level models, including baseline age, prostate specific antigen (PSA), digital rectal exam, as well as combined models (patient plus nSES variables) by race/PCa family history subgroups were built after variable reduction methods using Cox regression and LASSO machine-learning. Model fit of patient and combined models (AIC) were compared; p-values<0.05 were significant. Model-based high/low nSES exposure scores were calculated and the 5-year predicted probability of PCa was plotted against PSA by high/low neighborhood score to preliminarily assess clinical relevance.

RESULTS

In combined models, nSES variables were significantly associated with time to PCa diagnosis. Workers mode of transportation and low income were significant in White men with a PCa family history. Homeownership (%owner-occupied houses with >3 bedrooms) and unemployment were significant in Black men with and without a PCa family history, respectively. The 5-year predicted probability of PCa was higher in men with a high neighborhood score (weighted combination of significant nSES variables) compared to a low score (e.g., Baseline PSA level of 4ng/mL for men with PCa family history: White-26.7% vs 7.7%; Black-56.2% vs 29.7%).

DISCUSSION

Utilizing neighborhood data during patient risk assessment may be useful for high risk men affected by disparities. However, future studies with larger samples and validation/replication steps are needed.