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Next-Generation AI-Driven Unified Cancer Data Ecosystem and Framework for Clinical Service and Research in Malaysia

Project No.:  

AIM-C01-2025

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Project Leader: 

Professor Dr  Sarinder Kaur

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Project Collaborators:

  1. ​Dr Mohamad Hazim bin Md Hanif

  2. Professor Dr  Nur Aishah Mohd Taib

  3. Associate Professor Dr  Azura Mansor

  4. Associate Professor Dr  Pang Yong Kek​

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Graduate Research Assistant:

Barkavi Cheven

AIM-C01-2025_Professor Dr Sarinder Kaur_PI.jpg

Professor Dr  Sarinder Kaur 

Institute of Biological Sciences

Faculty of Science

Cancer registries collect information about people diagnosed with cancer, such as the type of cancer, treatments received, and outcomes. These registries are essential for understanding cancer trends, improving care, and supporting research. However, many countries, especially low- and middle-income countries, lack high-quality cancer registries. Existing systems often rely on outdated technology, require extensive manual data entry, and are slow and expensive to operate.

 

This study describes NextGen-v1, a modern, cloud-based cancer registry designed to address these challenges. The system was tested using data from more than 50,000 cancer cases, combining real hospital records with simulated data to safely assess performance at scale.

NextGen-v1 uses the latest international cancer classification system, ICD-11, which allows cancers to be recorded in a more accurate and consistent way. The platform can automatically suggest the correct cancer codes based on clinical information, greatly reducing manual workload. In this study, almost all cancer cases were successfully coded, and most codes were filled in automatically, saving time and reducing errors. Basic patient details such as age and sex were nearly always recorded, and key cancer information such as stage and treatment met international quality standards, particularly for newer records entered directly into the system.

The platform also makes it easier for researchers to access data while protecting patient privacy. Research requests are submitted and reviewed through an automated online process, allowing most approvals to be completed within a few days rather than weeks. Researchers can securely analyze data within the system without downloading sensitive information.


 

NextGen-v1 performed reliably, with almost no downtime and fast response times. Importantly, the cost of running the platform was much lower than that of many traditional cancer registries.

Overall, this study shows that modern cloud-based technology can support accurate, secure, and affordable cancer registries. While further expansion and long-term evaluation are needed, NextGen-v1 offers a practical solution for improving cancer data collection and research, especially in countries with limited healthcare resources.

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