Design, development, and evaluation of an advanced cancer surveillance system

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.

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.

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.

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.