Cancer Screening: Present Recommendations, the Development of Multi-Cancer Early Development Tests, and the Prospect of Universal Cancer Screening

Assessing Barriers to Cancer Screening and Early Detection in Older Adults in Saudi Arabia: A Mixed-Methods Approach to Oncology Nursing Practice Implications

BACKGROUND

Access to cancer screening services is crucial for early detection and improved survival rates, yet older adults in Saudi Arabia face significant barriers. Recent data from the Saudi Health Ministry indicate that cancer incidence in this demographic is rising, underscoring the urgent need for enhanced screening efforts. This study explores the factors influencing cancer screening behaviors among older adults in Riyadh, using a mixed-methods approach to identify and address these barriers effectively.

METHODS

The study integrated quantitative data from 100 participants aged 60 and above who attended King Saud University-affiliated healthcare centers, and qualitative insights from 20 semi-structured interviews. The Barriers to Cancer Screening Scale (BCSS) quantitatively assessed barriers, while the thematic analysis of interview data helped identify key themes.

RESULTS

Findings revealed significant barriers, categorized into three primary themes: accessibility challenges, psychological barriers, and social influences. These include logistical difficulties related to transportation and service availability, fears and anxieties regarding cancer diagnoses, and a lack of family support and cultural stigma, all of which impact participants' willingness to engage in screening.

CONCLUSION

The study underscores the multifaceted barriers faced by older adults in accessing cancer screening in Saudi Arabia. Tailored interventions that address logistical, psychological, and social factors are essential to enhance screening uptake and ensure equitable access to preventive services. These findings contribute to the ongoing discussions on public health strategies and underscore the necessity for community and healthcare provider engagement to improve cancer screening rates in this population.

N-NOSE Proves Effective for Early Cancer Detection: Real-World Data from Third-Party Medical Institutions

Cancer remains a leading cause of mortality in Japan, yet participation in conventional screening programs is low due to invasiveness, cost, and accessibility. Non-invasive, affordable, and accurate methods for early cancer detection in asymptomatic individuals are urgently needed. This opinion manuscript evaluates nematode cancer testing, a novel urine-based screening test using the nematode Caenorhabditis elegans, for its potential to improve early cancer detection rates, based on real-world data published in a Japanese journal. Nematode cancer testing leverages the nematode's ability to detect cancer-associated volatile compounds in urine, offering a highly sensitive, non-invasive screening approach. We analyzed data from a nationwide survey of PET-equipped medical institutions in Japan, comparing cancer discovery rates between nematode cancer testing-triggered and standard PET screenings. In nematode cancer testing-triggered PET screenings, the cancer discovery rate was 2.96%, significantly higher than the 1.31% observed in standard PET screenings. The apparent positive predictive value (PPV) of nematode cancer testing was calculated at 2.09%; when adjusted for PET/CT sensitivity, the actual PPV increased to approximately 11.7%. This reflects a screening efficiency 14.6 times higher than the general population's cancer incidence. These findings indicate that nematode cancer testing successfully detects cancer in high-risk individuals and may encourage participation in further diagnostic evaluations. The recently published nationwide survey of PET-equipped medical institutions in Japan highlights the good performance of nematode cancer testing in cancer detection. As an opinion-type manuscript based on real-world data from Japan, this paper shows that nematode cancer testing has substantial potential as a cost-effective, minimally invasive primary cancer screening tool for asymptomatic populations. By enhancing early detection rates and screening efficiency, it addresses the limitations of traditional screening methods. Implementing nematode cancer testing could lead to improved cancer outcomes, particularly in populations with low participation in standard screening programs and resource-limited settings.

Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer

Colon cancer is a prevalent and potentially fatal disease that demands early and accurate diagnosis for effective treatment. Traditional diagnostic approaches for colon cancer often face limitations in accuracy and efficiency, leading to challenges in early detection and treatment. In response to these challenges, this paper introduces an innovative method that leverages artificial intelligence, specifically convolutional neural network (CNN) and Fishier Mantis Optimizer, for the automated detection of colon cancer. The utilization of deep learning techniques, specifically CNN, enables the extraction of intricate features from medical imaging data, providing a robust and efficient diagnostic model. Additionally, the Fishier Mantis Optimizer, a bio-inspired optimization algorithm inspired by the hunting behavior of the mantis shrimp, is employed to fine-tune the parameters of the CNN, enhancing its convergence speed and performance. This hybrid approach aims to address the limitations of traditional diagnostic methods by leveraging the strengths of both deep learning and nature-inspired optimization to enhance the accuracy and effectiveness of colon cancer diagnosis. The proposed method was evaluated on a comprehensive dataset comprising colon cancer images, and the results demonstrate its superiority over traditional diagnostic approaches. The CNN-Fishier Mantis Optimizer model exhibited high sensitivity, specificity, and overall accuracy in distinguishing between cancer and non-cancer colon tissues. The integration of bio-inspired optimization algorithms with deep learning techniques not only contributes to the advancement of computer-aided diagnostic tools for colon cancer but also holds promise for enhancing the early detection and diagnosis of this disease, thereby facilitating timely intervention and improved patient prognosis. Various CNN designs, such as GoogLeNet and ResNet-50, were employed to capture features associated with colon diseases. However, inaccuracies were introduced in both feature extraction and data classification due to the abundance of features. To address this issue, feature reduction techniques were implemented using Fishier Mantis Optimizer algorithms, outperforming alternative methods such as Genetic Algorithms and simulated annealing. Encouraging results were obtained in the evaluation of diverse metrics, including sensitivity, specificity, accuracy, and F1-Score, which were found to be 94.87%, 96.19%, 97.65%, and 96.76%, respectively.