MSIntuit Suite

Optimize MSI testing
Rule-out MSS cases from the diagnostic workflow by screening with AI for digital pathology
As biomarker screening evolves, AI testing bridges the gaps
Immune Checkpoint Inhibitor (ICI)
MSI status is predictive of response to immunotherapy across all solid tumors.
Pain points across indications
Evolving guidelines are increasing the number of biomarkers to test for; expanding the diagnostic workflow burden for pathologists and clinicians.
Biopsied tissue is increasingly insufficient to meet the demands of multi-biomarker testing.
Performances rule-out and NPV
50% rule-out with 0.99 NPV
70% rule-out with 0.98 NPV
Performances rule-out and NPV
62% rule-out with 0.99 NPV
Performances rule-out and NPV
21% rule-out with 0.96 NPV
32% rule-out with 0.97 NPV
| Solution Type | Specific AI Algorithm/App |
|---|---|
| Deployment Environment | Clinical Diagnostics |
| Regulatory Clearance | Colorectal Cancer (dMMR/MSI) – CE-IVD |
| Target | Colorectal Cancer, Endometrial Cancer, Gastrointestinal (GI) Cancer |
| Platform Dependency | Platform Agnostic |
| Deployment Options | Hybrid (Cloud analysis, local viewer) |
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![Figure 1: Fine-tuning improves robustness and performance jointly. Each foundation model is shown before (open circle) and after (filled circle) fine-tuning. The x-axis is the average PathoROB robustness index over three datasets, where higher values indicate greater robustness; the y-axis is the normalized rank sum over the HEST, THUNDER and Patho-Bench benchmarks, rescaled to [0, 1] so that 1 corresponds to the best achievable performance.](https://www.pathologynews.com/wp-content/uploads/2020/07/embedding-figure-800x600-1.png)
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