The Rising Need for Precision in Cancer Care
Colon cancer remains a significant global health challenge, ranking as the third most common cancer worldwide and a leading cause of cancer-related deaths. In Malaysia and Southeast Asia, rising incidence rates and limited access to advanced diagnostics underscore the urgency for innovative solutions. Traditional methods of assessing cancer risk often rely on microscopic examination of tissue samples by pathologists, but these approaches can overlook subtle biological markers that influence treatment outcomes.
How AI Enhances Tumor Risk Assessment
A Norwegian startup, DoMore Diagnostics, has developed an AI system that analyzes digital images of cancer tissue samples with unprecedented precision. Unlike human pathologists, who may miss nuanced patterns due to visual limitations, this AI tool evaluates thousands of image features simultaneously. By correlating these features with long-term patient outcomes—such as recurrence rates and survival—it generates highly accurate risk predictions.
The technology was developed through a collaboration between Oslo University Hospital, Oxford University, and University College London. Its algorithm is trained on vast datasets of patient images and clinical outcomes, enabling it to identify risk factors linked to metastasis and death. This data-driven approach allows clinicians to move beyond the "one-size-fits-all" chemotherapy model, which often exposes patients to severe side effects without clear benefits.
Reducing Unnecessary Chemotherapy: A Game-Changer for Patients
Chemotherapy is frequently prescribed as a standard follow-up treatment after surgery for colorectal cancer. However, studies indicate that 96–98% of stage two patients and 80% of stage three patients receive these treatments without improved survival rates. The AI tool addresses this issue by quantifying tumor aggressiveness, helping doctors tailor therapies to individual risk profiles. For low-risk patients, this could mean avoiding chemotherapy altogether, reducing exposure to nausea, fatigue, and long-term health complications.
Regional Implications for Southeast Asia
In Malaysia and neighboring countries, healthcare systems face challenges in balancing cost-effective care with personalized treatment. The integration of AI diagnostics could alleviate pressure on pathology departments while improving patient outcomes. Local hospitals adopting this technology may see a shift toward data-informed decision-making, particularly in resource-constrained settings where expert pathologists are scarce.
How the Technology Works
The AI system processes digital slides of tumor tissue, focusing on cellular structures and patterns associated with rapid growth or spread. By comparing these features to historical patient data, it assigns a risk score that guides treatment planning. Notably, the tool does not replace pathologists but complements their expertise, offering a second layer of analysis to enhance diagnostic accuracy.
Clinical Validation and Global Adoption
DoMore Diagnostics’ tool has been validated in European, U.S., Japanese, and Mexican hospitals, demonstrating superior accuracy compared to human evaluations. Independent studies have shown that AI-predicted risk scores correlate strongly with actual patient survival rates. As regulatory approvals expand, the technology could become a standard tool in oncology practices worldwide.
What This Means for Patients and Clinicians
For patients, the AI tool represents a step toward precision medicine—tailoring treatments to individual biology rather than general guidelines. Clinicians gain a data-driven framework to discuss risks and benefits with patients, fostering informed consent. However, widespread adoption will require collaboration between tech developers, healthcare providers, and policymakers to ensure equitable access.
Medical Disclaimer
The information provided in this article is for educational purposes only and does not constitute medical advice. Treatment decisions should always be made in consultation with a qualified healthcare professional, as individual cases vary widely.
