A La Trobe University-developed AI tool accurately predicts relapse risk in stage II bowel cancer using digital pathology images and descriptions.
Key Details
- 1AI tool SÉMIL uses pathology slide images and written descriptions to stratify relapse risk for stage II bowel cancer patients.
- 2Validated on over 1220 patients across three independent cohorts and multiple Australian institutions.
- 3Greatest predictive accuracy when AI assessments align with pathologist evaluations.
- 4Can be integrated into existing clinical digital pathology workflows, supporting but not replacing human clinicians.
- 5Findings published in Gastroenterology (July 2026) with no conflicts of interest reported.
- 6Research involved collaboration among several top Australian institutions and cancer centers.
Why It Matters
Accurate risk stratification can ensure high-risk stage II bowel cancer patients receive timely, potentially life-saving therapy, while sparing low-risk patients from unnecessary treatment. The study demonstrates how AI can augment pathology workflows for more consistent, data-driven clinical decisions in oncology.
Source
EurekAlert
Related News
•EurekAlert
AI Framework Accelerates Aortic Aneurysm Risk Prediction from Imaging
Researchers developed BioPINN-LM, combining physics-informed neural networks and multimodal large language models to deliver fast, interpretable risk assessments for ascending thoracic aortic aneurysms.
•EurekAlert
Study Finds Patient Voices Missing in Generative AI Design for Oncology
A Flinders University-led review found patients and carers are rarely involved in shaping generative AI tools used in oncology.
•EurekAlert
Scripps Researchers Develop AI Foundation Model for ECG-Based Heart Disease Prediction
A new AI foundation model, ECG-CLIP, improves detection and prediction of multiple heart diseases using large-scale ECG and clinician note data.