Design, Development, and Evaluation of an Advanced Cancer Surveillance System Publisher Pubmed



Soleimani M ; Ghazisaeidi M ; Ayyoubzadeh SM ; Jalilvand A
Source: BMC Cancer Published:2025

Abstract

Background: Cancer remains a leading global cause of mortality, demanding robust surveillance systems to inform public health strategies. Current cancer surveillance systems, particularly in low-resource settings, often lack on-demand analytics, spatial visualization, and predictive modeling, limiting their utility in addressing disparities and guiding targeted interventions. This study aimed to design, develop, and evaluate a GIS-integrated cancer surveillance systems tailored to the epidemiological and geographical context of Iran. Methods: Employing a three-phase approach, the study began with a systematic review of cancer surveillance indicators, followed by the design and development of the system using a modular architecture supported by Django and Vue.js frameworks. The system integrates multi-level data standardization, GIS-based spatial analysis, and predictive analytics for on-demand insights. Usability evaluation was conducted using Nielsen’s Heuristic Assessment, incorporating feedback from medical informatics specialists, pathologists, and health managers. Results: The Cancer Surveillance System incorporated critical data elements validated with CVR (> 0.51) and Cronbach’s alpha (0.849). Phase two developed a GIS-integrated, scalable system handling 20 million records, enabling on-demand monitoring, spatial analysis, and risk factor evaluation. Predictive modeling tools forecast cancer trends over 5-, 10-, and 20-year horizons, adhering to WHO standards. Usability evaluation resolved 85% of identified issues, enhancing functionality, user satisfaction, and scalability for precision cancer surveillance. Conclusion: This study presents a scalable and adaptable CSS framework that bridges traditional surveillance limitations and modern analytical demands. Its integration of advanced technologies provides a model for global adaptation, supporting equitable resource distribution and evidence-based cancer control strategies. © 2025 Elsevier B.V., All rights reserved.