Lung Cancer Screening and Epigenetics in African Americans: The Role of the Socioecological Framework

Exploring Factors Influencing Cervical Cancer Screening Participation among Singaporean Women: A Social Ecological Approach

Background/Objectives: Cervical cancer screening uptake in Singapore remains suboptimal. This study employed the Social Ecological Model (SEM) to investigate factors influencing cervical cancer screening participation among Singaporean women. Methods: The study included 665 women, aged 25-69 years, who reported awareness of cancer screening and no personal cancer history. Data were collected through a previously described online survey. Hierarchical logistic regression analysis was conducted to identify significant factors influencing screening participation. Results: Only 30% of participants reported cervical cancer screening participation. Women aged 25-29 years (OR = 0.33; 95% CI = 0.12-0.77), Malay women (OR = 0.42; 95% CI = 0.20-0.83), and unmarried women (OR = 0.30; 95% CI = 0.18-0.48) were less likely to be screened. Positive associations with screening participation were observed with good cervical cancer screening knowledge (OR = 2.90; 95% CI = 1.96-4.32), awareness of primary care providers' role in delivering screening services (OR = 1.94; 95% CI = 1.24-3.10), cancer information seeking behavior (OR = 1.59; 95% CI = 1.07-2.39), and acceptance of self-sampling options (OR = 1.81; 95% CI = 1.22-2.70). Conclusions: Our study highlights the cumulative impact of factors at various SEM levels on screening participation and underscores the necessity for more targeted and multi-pronged strategies to improve cervical cancer screening uptake in Singapore.

A qualitative assessment of the acceptability of human papillomavirus self-sampling and informational materials among diverse populations

BACKGROUND

Disparities in cervical cancer screening rates among marginalized groups is a driver of inequalities in cervical cancer. Self-sampling for human papillomavirus (HPV) testing is a newly emerging alternative to clinician-performed testing to screen for cervical cancer, and has high potential to reduce screening barriers in under-screened and marginalized groups. We study the acceptability in of HPV self-sampling and informational materials among Black/African American, Hispanic/Spanish speaking, American Indian/Alaska Native and transgender/nonbinary populations.

METHODS

We conducted qualitative interviews with patients, ages 30-65, who were Black/African American, Hispanic, American Indian, and/or transgender/nonbinary individuals assigned female at birth. Telephone interviews were conducted in English or Spanish. Patients did not complete the test, rather were asked about the attractiveness, comprehensibility, and acceptability of the HPV self-test, instructions, and messaging.

RESULTS

Among 23 completed interviews (5 American Indian/Alaska Native, 7 Hispanic [2 bilingual, 5 Spanish-speaking], 5 Black/African American, and 6 transgender/nonbinary), patients from all groups thought the test was straightforward and convenient, and they would complete the test at home or in clinic. The transgender/nonbinary patients preferred at-home testing. American Indian and transgender/nonbinary patients liked that the test might avoid pain, discomfort, and invasiveness. All patients liked the letter and instructions. All groups had specific suggestions for making the materials more culturally acceptable.

CONCLUSIONS

The HPV self-test and the instructions and materials for use were acceptable for a diverse group of patients. Tailored outreach and messaging should be considered to reduce screening disparities among groups that have been historically underserved by the medical system.

Identification of CT-based non-invasive radiomic biomarkers for overall survival prediction in oral cavity squamous cell carcinoma

This study addresses the limited non-invasive tools for Oral Cavity Squamous Cell Carcinoma (OSCC) survival prediction by identifying Computed Tomography (CT)-based biomarkers to improve prognosis prediction. A retrospective analysis was conducted on data from 149 OSCC patients, including CT radiomics and clinical information. An ensemble approach involving correlation analysis, score screening, and the Sparse-L1 algorithm was used to select functional features, which were then used to build Cox Proportional Hazards models (CPH). Our CPH achieved a 0.70 concordance index in testing. The model identified two CT-based radiomics features, Gradient-Neighboring-Gray-Tone-Difference-Matrix-Strength (GNS) and normalized-Wavelet-LLL-Gray-Level-Dependence-Matrix-Large-Dependence-High-Gray-Level-Emphasis (HLE), as well as stage and alcohol usage, as survival biomarkers. The GNS group with values above 14 showed a hazard ratio of 0.12 and a 3-year survival rate of about 90%. Conversely, the GNS group with values less than or equal to 14 had a 49% survival rate. For normalized HLE, the high-end group (HLE > - 0.415) had a hazard ratio of 2.41, resulting in a 3-year survival rate of 70%, while the low-end group (HLE ≤ - 0.415) had a 36% survival rate. These findings contribute to our knowledge of how radiomics can be used to predict the outcome so that treatment plans can be tailored for patients people with OSCC to improve their survival.

Identification of CT-based non-invasive Radiographic Biomarkers for Overall Survival Stratification in Oral Cavity Squamous Cell Carcinoma

This study addresses the limited non-invasive tools for Oral Cavity Squamous Cell Carcinoma OSCC survival prediction by identifying Computed Tomography (CT)-based biomarkers for improved prognosis. A retrospective analysis was conducted on data from 149 OSCC patients, including radiomics and clinical. An ensemble approach involving correlation analysis, score screening, and the Sparse-L1 algorithm was used to select functional features, which were then used to build Cox Proportional Hazards models (CPH). Our CPH achieved a 0.70 concordance index in testing. The model identified two CT-based radiomics features, Gradient-Neighboring-Gray-Tone-Difference-Matrix-Strength (GNS) and normalized-Wavelet-LLL-Gray-Level-Dependence-Matrix-Large-Dependence-High-Gray-Level-Emphasis (HLE), as well as smoking and alcohol usage, as survival biomarkers. The GNS group with values above 14 showed a hazard ratio of 0.12 and a 3-year survival rate of about 90%. Conversely, the GNS group with values less than or equal to 14 had a 49% survival rate. For normalized HLE, the high-end group (HLE > -0.415) had a hazard ratio of 2.41, resulting in a 3-year survival rate of 70%, while the low-end group (HLE <= -0.415) had a 36% survival rate. These findings contribute to our knowledge of how radiomics can be used to anticipate the outcome and tailor treatment plans from people with OSCC.

A Study Protocol for Increasing Access to Smoking Cessation Treatments for Low-Income Minority Smokers

Background: Smoking rates among low-income patients are double those of the general population. Access to health care is an essential social determinant of health. Federally qualified health care centers (FQHC) are government-supported and community-based centers to increase access to health care for non-insured and underinsured patients. However, barriers to implementation impact adherence and sustainability of evidence-based smoking cessation within FQHC settings. To address this implementation barrier, our multi-disciplinary team proposes Mi QUIT CARE (Mile Square QUIT Community-Access-Referral-Expansion) to establish the acceptability, feasibility, and capacity of an FQHC system to deliver an evidence-based and multi-level intervention to increase patient engagement with a state tobacco quitline. Methods: A mixed-method approach, rooted in an implementation science framework of RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance), will be used in this hybrid effectiveness-implementation design. We aim to evaluate the efficacy of a novel delivery system (patient portal) for increasing access to smoking cessation treatment. In preparation for a future randomized clinical trial of Mi QUIT CARE, we will conduct the following developmental research: (1) Examine the burden of tobacco among patient populations served by our partner FQHC, (2) Evaluate among FQHC patients and health care providers, knowledge, attitudes, barriers, and facilitators related to smoking cessation and our intervention components, (3) Evaluate the use of tailored communication strategies and patient navigation to increase patient portal uptake among patients, and (4) To test the acceptability, feasibility, and capacity of the partner FQHC to deliver Mi QUIT CARE. Discussion: This study provides a model for developing and implementing smoking and other health promotion interventions for low-income patients delivered via patient health portals. If successful, the intervention has important implications for addressing a critical social determinant of cancer and other tobacco-related morbidities. Trial Registration: U.S. National Institutes of Health Clinical Trials, NCT04827420, https://clinicaltrials.gov/ct2/show/NCT04827420.

A transdisciplinary approach to understand the epigenetic basis of race/ethnicity health disparities

Health disparities correspond to differences in disease burden and mortality among socially defined population groups. Such disparities may emerge according to race/ethnicity, socioeconomic status and a variety of other social contexts, and are documented for a wide range of diseases. Here, we provide a transdisciplinary perspective on the contribution of epigenetics to the understanding of health disparities, with a special emphasis on disparities across socially defined racial/ethnic groups. Scientists in the fields of biological anthropology, bioinformatics and molecular epidemiology provide a summary of theoretical, statistical and practical considerations for conducting epigenetic health disparities research, and provide examples of successful applications from cancer research using this approach.

Redlining, structural racism, and lung cancer screening disparities

OBJECTIVE

The objective of this study was to understand the effect of historical redlining (preclusion from home loans and wealth-building for Black Americans) and its downstream factors on the completion of lung cancer screening in Boston.

METHODS

Patients within our institution were identified as eligible for lung cancer screening on the basis of the United State Preventive Service Task Force criteria and patient charts were reviewed to determine if patients completed low-dose computed tomography screening. Individual addresses were geocoded and overlayed with original 1930 Home Owner Loan Corporation redlining vector files. Structural equation models were used to estimate the odds of screening for Black and White patients, interacted with sex, in redlined and nonredlined areas.

RESULTS

Black patients had a 44% lower odds of screening compared with White (odds ratio [OR], 0.66; 95% CI, 0.52-0.85). With race as a mediator, Black patients in redlined areas were 61% less likely to undergo screening than White patients (OR, 0.39; 95% CI, 0.24-0.64). Similarly, in redlined areas Black women had 61% (OR, 0.39; 95% CI, 0.21-0.73) and Black men 47% (OR, 0.53; 95% CI, 0.29-0.98) lower odds of screening compared with White men in redlined areas.

CONCLUSIONS

Despite higher rates of lung cancer screening in redlined areas, Black race mediated worse screening rates in these areas, suggesting racist structural factors contributing to the disparities in lung cancer screening completion among Black and White patients. Furthermore, these disparities were more apparent in Black women, suggesting that racial and gender intersectional discrimination are important in lung cancer screening completion.

Multi-institutional development and external validation of machine learning-based models to predict relapse risk of pancreatic ductal adenocarcinoma after radical resection

BACKGROUND

Surgical resection is the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC) and the survival of patients after radical resection is closely related to relapse. We aimed to develop models to predict the risk of relapse using machine learning methods based on multiple clinical parameters.

METHODS

Data were collected and analysed of 262 PDAC patients who underwent radical resection at 3 institutions between 2013 and 2017, with 183 from one institution as a training set, 79 from the other 2 institution as a validation set. We developed and compared several predictive models to predict 1- and 2-year relapse risk using machine learning approaches.

RESULTS

Machine learning techniques were superior to conventional regression-based analyses in predicting risk of relapse of PDAC after radical resection. Among them, the random forest (RF) outperformed other methods in the training set. The highest accuracy and area under the receiver operating characteristic curve (AUROC) for predicting 1-year relapse risk with RF were 78.4% and 0.834, respectively, and for 2-year relapse risk were 95.1% and 0.998. However, the support vector machine (SVM) model showed better performance than the others for predicting 1-year relapse risk in the validation set. And the k neighbor algorithm (KNN) model achieved the highest accuracy and AUROC for predicting 2-year relapse risk.

CONCLUSIONS

By machine learning, this study has developed and validated comprehensive models integrating clinicopathological characteristics to predict the relapse risk of PDAC after radical resection which will guide the development of personalized surveillance programs after surgery.

Does Race or Ethnicity Impact Complications After Pulmonary Lobectomy for Patients With Lung Cancer?

BACKGROUND

Racial disparity in surgical access and postoperative outcomes after pulmonary lobectomy continues to be a concern and target for improvement; however, evidence of independent impact of race on complications is lacking. The objective of this study was to investigate the impact of race/ethnicity on surgical outcomes after lobectomy for lung cancer and estimate the distribution of racial/ethnic groups among expected resectable lung cancer cases using a large national database.

METHODS

Patients who underwent lobectomy for lung cancer between 2005 and 2016 were identified in the American College of Surgeon National Surgical Quality Improvement Program. Preoperative characteristics and postoperative outcomes were compared between race/ethnicity groups in all patients and in propensity-matched cohorts, controlling for pertinent risk factors. Distribution of each race/ethnicity in the database was calculated relative to estimated numbers of patients with resectable lung cancer in the United States.

RESULTS

A total of 10,202 patients (age 67.6 ± 9.7, 46.7% male, 86.4% white) underwent nonemergent lobectomy (46.8% thoracoscopic). Blacks had higher rates of baseline risk factors. In propensity score-matched cohorts of whites, blacks, and Hispanics/Asians (n = 498 each), postoperatively, blacks had higher rates of prolonged intubation and longer hospital stay while whites had a higher rate of pneumonia. Race was independently associated with these adverse outcomes on multivariate analysis. Proportion of blacks and Hispanics in the American College of Surgeon National Surgical Quality Improvement Program was lower than their respective proportion of resectable lung cancer in the United States.

CONCLUSIONS

In a large national-level surgical database, there was lower than expected representation of black and Hispanic patients. Black race was independently associated with extended length of stay and prolonged intubation, whereas white was independently associated with postoperative pneumonia.