Clinical Scorecard: Causally Adaptive Foundation Model with Uncertainty Awareness for Enhanced Diagnosis of Colorectal Cancer Pathology

At a Glance

Key Highlights

• UAD-FM model addresses domain shifts, unreliable uncertainty estimation, and spurious correlations limiting clinical reliability in CRC pathology models.
• Incorporates epistemic and aleatoric uncertainty quantification, causal representation learning, and entropy-based test-time adaptation for robust inference.
• Produces interpretable uncertainty maps enabling human-AI collaboration and defers uncertain cases to pathologists for safe clinical deployment.

Guideline-Based Recommendations

Diagnosis

• Use histopathological examination of hematoxylin and eosin (H&E) stained whole-slide images as the diagnostic gold standard for CRC.
• Employ computational pathology models with uncertainty quantification to support diagnosis and grading.
• Incorporate causal representation learning to reduce spurious correlations and improve robustness across centers.

Management

• Integrate foundation models like UAD-FM that adapt to domain shifts via test-time adaptation without requiring source data access.
• Use calibrated prediction confidence to defer uncertain cases to human experts, ensuring trustworthy clinical decisions.

Monitoring & Follow-up

• Monitor model calibration and uncertainty estimates continuously to maintain reliability across heterogeneous datasets.
• Assess model performance across multi-institutional cohorts to detect domain shifts and adapt accordingly.

Risks

• Be aware of risks from domain shift due to staining variability, scanner differences, and patient population heterogeneity.
• Recognize limitations of models lacking uncertainty quantification which may lead to erroneous clinical decisions.
• Consider spurious correlations from non-causal morphological features that can compromise model robustness.

Patient & Prescribing Data

Patients undergoing colorectal cancer histopathological diagnosis across diverse clinical centers.

While not directly prescribing treatment, UAD-FM supports accurate diagnosis and grading, facilitating appropriate clinical management decisions.

Clinical Best Practices

• Adopt computational pathology models that provide both high accuracy and reliable uncertainty estimates for CRC diagnosis.
• Implement causal test-time adaptation strategies to maintain model generalizability in multi-center settings.
• Use interpretable uncertainty maps to enhance human-AI collaboration and improve diagnostic confidence.
• Defer uncertain model predictions to expert pathologists to ensure patient safety and diagnostic accuracy.

References

• Global cancer statistics 2020
• TCGA-COAD/READ datasets
• Entropy minimization for test-time adaptation
• Causal representation learning in pathology