Back to all news
AI Model Predicts Microsatellite Instability and Immunotherapy Response from Histology
Yonsei University researchers introduced MSI-SEER, an AI model for MSI and immunotherapy response prediction from histology images of gastric and colorectal cancers.
Key Details
- 1MSI-SEER uses deep Gaussian process modeling to analyze H&E-stained whole-slide images.
- 2The model integrates uncertainty quantification, providing a Bayesian Confidence Score for each prediction.
- 3MSI-SEER flags uncertain cases for human review to enhance reliability and safety.
- 4Validated on large, racially diverse datasets, it achieved state-of-the-art MSI prediction accuracy.
- 5The model also predicts immune checkpoint inhibitor (ICI) response, integrating tumor MSI status and stroma-to-tumor ratio.
- 6Published in npj Digital Medicine on May 19, 2025.
Why It Matters
This innovation demonstrates the power of AI in pathology to provide clinically actionable predictions and to integrate uncertainty for safer AI-human collaboration in cancer diagnosis and therapy selection. The approach may serve as a model for broader use of AI in precision oncology workflows.
Source
EurekAlert
Related News
•EurekAlert
University of Michigan AI Achieves Near-Instant Brain MRI Diagnosis
University of Michigan's AI model, Prima, can diagnose brain MRI scans in seconds, achieving up to 97.5% accuracy in identifying neurological conditions.
•EurekAlert
BrainIAC AI Model Excels in Brain MRI Analysis and Disease Prediction
BrainIAC, a new foundation model from Mass General Brigham, outperforms traditional AI approaches in analyzing brain MRI for tasks like brain age estimation and cancer prognosis.
•EurekAlert
Janus Meta-Imager Leverages Neural Networks for Asymmetric Optical Imaging
Researchers unveil a passive all-optical device using neural networks for asymmetric image transmission and transformation.