Oxidative stress, a condition characterized by an imbalance between pro-oxidant molecules and antioxidant defense systems, is increasingly recognized as a key contributor to cancer development. This is because the reactive oxygen species (ROS) generated during oxidative stress can damage DNA, proteins, and lipids to facilitate mutations and other cellular changes that promote cancer growth. Antioxidant supplementation is a potential strategy for decreasing cancer incidence; by reducing oxidative stress, DNA damage and other deleterious cellular changes may be attenuated. Several clinical trials have been conducted to investigate the role of antioxidant supplements in cancer prevention. Some studies have found that antioxidant supplements, such as vitamin A, vitamin C, and vitamin E, can reduce the risk of certain types of cancer. On the other hand, some studies posit an increased risk of cancer with antioxidant supplement use. In this review, we will provide an overview of the current understanding of the role of oxidative stress in cancer formation, as well as the potential benefits of antioxidant supplementation in cancer prevention. Additionally, we will discuss both preclinical and clinical studies highlighting the potentials and limitations of preventive antioxidant strategies.

Introduction: Melanoma, a deadly form of skin cancer, has witnessed a notable increase in incidence over the past decades. Despite advancements in treatment, it remains a significant cause of cancer mortality. Understanding demographic trends and variations in melanoma mortality is crucial for addressing disparities and implementing effective interventions. Methods: Using the Centers for Disease Control Wide Ranging Online Data for Epidemiologic Research (CDC WONDER) database, we analyzed melanoma mortality data in the United States from 1999 to 2020. Data were stratified by demographic and regional variables, and age-adjusted mortality rates were calculated. Descriptive analysis was performed and Joinpoint regression analysis was employed to identify temporal trends. Results: Between 1999 and 2020, there were 184,416 melanoma-related deaths in the United States Overall, the age-adjusted mortality rate declined from 2.7 to 2.0 per 100,000 people at a rate of -1.3% annually, with significant variations across demographic groups and regions. Men, non-Hispanic White individuals, and those aged > 65 experienced higher mortality rates. Non-Hispanic White individuals noted the steepest decrease in AAMR after 2013 at a rate of -6.1% annually. Disparities were seen by geographic density, with rural populations exhibiting higher mortality compared to their urban and suburban counterparts. Conclusion: The study highlights a significant reduction in melanoma mortality in the U.S. since 2013, potentially attributed to advancements in diagnostic techniques such as dermoscopy and the introduction of immune checkpoint inhibitors. Disparities persist, particularly among rural populations. Targeted interventions focusing on increased screening and education are warranted to further mitigate melanoma mortality and address demographic disparities.

Introduction: Machine learning (ML) techniques have gained increasing attention in the field of healthcare, including predicting outcomes in patients with lung cancer. ML has the potential to enhance prognostication in lung cancer patients and improve clinical decision-making. In this systematic review and meta-analysis, we aimed to evaluate the performance of ML models compared to logistic regression (LR) models in predicting overall survival in patients with lung cancer.Methods: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A comprehensive search was conducted in Medline, Embase, and Cochrane databases using a predefined search query. Two independent reviewers screened abstracts and conflicts were resolved by a third reviewer. Inclusion and exclusion criteria were applied to select eligible studies. Risk of bias assessment was performed using predefined criteria. Data extraction was conducted using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) checklist. Meta-analytic analysis was performed to compare the discriminative ability of ML and LR models.Results: The literature search resulted in 3,635 studies, and 12 studies with a total of 211,068 patients were included in the analysis. Six studies reported confidence intervals and were included in the meta-analysis. The performance of ML models varied across studies, with C-statistics ranging from 0.60 to 0.85. The pooled analysis showed that ML models had higher discriminative ability compared to LR models, with a weighted average C-statistic of 0.78 for ML models compared to 0.70 for LR models.Conclusion: Machine learning models show promise in predicting overall survival in patients with lung cancer, with superior discriminative ability compared to logistic regression models. However, further validation and standardization of ML models are needed before their widespread implementation in clinical practice. Future research should focus on addressing the limitations of the current literature, such as potential bias and heterogeneity among studies, to improve the accuracy and generalizability of ML models for predicting outcomes in patients with lung cancer. Further research and development of ML models in this field may lead to improved patient outcomes and personalized treatment strategies.

Introduction: Pancreatic cancer is a significant public health concern and a leading cause of cancer-related deaths worldwide. This study aimed to investigate pancreatic cancer mortality trends and disparities in the United States (US) from 1999 to 2020. Methods: Data were obtained from the Centers for Disease Control (CDC) Wide-Ranging Online Data for Epidemiologic Research database. Mortality rates were age-adjusted and standardized to the year 2000 US population. Joinpoint regression was used to analyze temporal trends in age-adjusted mortality rates (AAMRs) by sociodemographic and geographic variables. Results: Between 1999 and 2020, pancreatic cancer led to a total of 810,628 deaths in the US, an average mortality of nearly 39,000 deaths per year. The AAMR slightly increased from 10.6 in 1999 to 11.1 in 2020, with an associated annual percent change (APC) of 0.2. Mortality rates were highest among individuals aged 65 and older. Black individuals experienced the highest overall pancreatic cancer-related AAMR at 13.8. Despite this, Black individuals experienced a decreasing mortality trend over time (APC −0.2) while White individuals experienced an increasing trend in mortality (APC 0.4). Additionally, individuals residing in rural areas experienced steeper rates of mortality increase than those living in urban areas (APC 0.6 for rural vs −0.2 for urban). White individuals in urban and rural populations experienced an increase in mortality, while Black individuals in urban environments experienced a decrease in mortality, and Black individuals in rural environments experienced stable mortality trends. Conclusions: Mortality from pancreatic cancer continues to increase in the US, with racial and regional disparities identified in minorities and rural-dwelling individuals. These disparate findings highlight the importance of ongoing efforts to understand and address pancreatic cancer treatment and outcomes disparities in the US, and future studies should further investigate the underlying etiologies of these disparities and potential for novel therapies to reduce the mortality.

Background: Lung cancer is the leading cause of cancer-related mortality in the United States and is projected to account for 127,070 deaths in 2023. Although the lung cancer mortality rate has been decreasing over the last decade, demographic disparities in mortality still exist. We sought to determine the impact of demographic factors on lung cancer mortality and trends in the United States. Patients and Methods: We queried the Centers for Disease Control and Prevention (CDC) database for mortality statistics with an underlying cause of death of lung and bronchus cancer from 1999 through 2020. Age-adjusted mortality rates (AAMR) were calculated per 100,000 people. We assessed the AAMR by demographic variables, including race, geographic density, sex, age, and US census region. Temporal trends were evaluated using Joinpoint regression software, and average annual percent change (APC) was calculated. Results: From 1999 through 2020, lung cancer led to 3,380,830 deaths. The AAMR decreased by 55.1 to 31.8, with an associated average APC of −2.6%. In 1999, men had an AAMR almost twice as high as women, but these differences became less pronounced over time. Rural populations experienced the highest AAMR and the slowest rate of decrease compared with urban populations, who experienced the lowest AAMR and fastest decrease. Non-Hispanic Black individuals experienced the highest AAMR, with an annual decrease of −3.0%. The West experienced the fastest decrease at −3.1% annually, whereas the Midwest experienced the slowest decrease at −2.0% annually. Conclusions: Although the mortality rate of lung cancer has been decreasing since 1999, not all demographic groups have experienced the same rates of decrease, and disparities in outcomes are still prevalent. Vulnerable subgroups need targeted interventions, such as the incorporation of patient navigators, improved screening chest CT scan access and follow-up, and telehealth expansion, which will improve the likelihood of earlier-stage diagnoses and the potential for curative treatments.