Related Experiment Video
Updated: Oct 21, 2025

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Cancer in Saudi Arabia (CSA): Web-Based Application to Study Cancer Data Among Saudis Using Waterfall Model
Suliman A Alsagaby1, Mafawez T Alharbi2
1Department of Medical Laboratories Sciences, College of Applied Medical Sciences, Majmaah University, Majmaah, 11952, Saudi Arabia.
Background:
Information technology (IT) has emerged as a promising enabler to address the issue of big data in health care. Despite the urgent need for an IT-based tool to tackle this issue, one is not available to specifically study the massive data related to cancer among Saudis.
Objective:
To develop a web-based application, which we named "Cancer in Saudi Arabia (CSA)" to provide an interactive, quick, and easy method to reach, extract, compare, and visualize cancer data from Saudi Cancer Incidence Reports (SCIRs).
Methods:
We used waterfall model to develop CSA. Next, we used CSA to study the data of non-Hodgkin lymphoma (NHL) in Saudis reported in the SCIRs (1999-2015).
Results:
CSA-based analysis showed that NHL incidence rate increased with age and the disease was more common among males compared with females. In addition, NHL was most predominant in the regions of Riyadh and Eastern, while it was the least prevalent in Jazan Region. Interestingly, the largest proportion of NHL patients was diagnosed in the late stage, and malignant lymphoma, large B-cell diffuse, OS (DLBCL) were the most frequent subtypes of NHL.
Conclusion:
As a user-friendly application, we believe that CSA will be a useful tool for studying cancer data in Saudis and will make the data published in SCIRs more reachable and usable. Our findings of NHL provided an almost comprehensive view of the epidemiology of the disease in Saudis for 17 years.
More Related Videos
11:18Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research
Published on: January 22, 2011
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
Related Concept Videos
Cancer Survival Analysis
Statistical Software for Data Analysis and Clinical Trials
Mouse Models of Cancer Study
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Kaplan-Meier Approach