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What if one of the biggest sources of diagnostic variability in prostate cancer isn’t the pathologist—but the stain we’ve trusted for decades?

In this episode, I speak with Professor Ingid Carlbom, founder of CADESS.AI, about a different way to approach prostate cancer grading—by rethinking staining, segmentation, and AI decision support from the ground up. We explore why 30–40% interobserver variability persists in Gleason grading and how optimized stains combined with explainable AI can significantly reduce that uncertainty.

Ingrid shares her journey from applied mathematics and computer science into pathology, the skepticism she faced in 2008, and why CADESS.AI chose not to “optimize H&E,” but instead developed a Picrosirius red + hematoxylin stain designed specifically for computational pathology. We discuss how grading at the gland and cellular level improves reproducibility, why explainability matters for trust, and what it really takes to build both stain and software as a single diagnostic workflow.

This conversation challenges long-held assumptions—and asks whether improving data quality should come before building smarter algorithms.

Highlights:

• [00:00–01:08] The problem: 30–40% disagreement in prostate cancer grading
• [01:08–03:03] Ingrid’s path from applied math to digital pathology
• [03:03–04:58] Early skepticism toward AI in pathology and fear of replacement
• [04:58–08:56] Why H&E limits segmentation—and how a new stain changes that
• [10:55–15:09] Clinical testing: non-inferiority, AI assistance, and NCCN risk stratification
• [19:47–22:59] Explainable UI: color-coded glands and pathologist override
• [26:16–27:29] Why grading glands (not whole slides) reduces variability
• [38:09–41:47] Regulatory challenges of combined stain + AI devices
• [45:52–48:55] The future of optimized stains in routine pathology
  
  
  

Resources from This Episode

• CADESS.AI – Prostate cancer decision support system
• NCCN prostate cancer risk stratification guidelines

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