MSAI-Path: Predicting Microsatellite Instability From Routine Histology Slides Without Reinventing the Wheel
Bibliographic record
Abstract
Microsatellite instability (MSI) is an important biomarker in colorectal cancer, influencing both patient prognosis and treatment decisions. Current approaches for MSI prediction from hematoxylin and eosin--stained whole-slide images (WSI) rely on end-to-end deep learning ("black-box") models with limited interpretability, often relying on heatmaps for visualization. However, experienced pathologists can intuitively identify MSI through specific histologic features and have developed manual classification systems such as MS-Path for Lynch syndrome screening. We present a novel hybrid approach that combines computational and pathologist expertise to create an explainable and verifiable method for MSI prediction in colorectal cancer, applicable to resection and biopsy WSI. Our proposed method uses nuclei and tissue segmentation models to automatically quantify MSI-associated histologic features outlined in the Bethesda guidelines, including intraepithelial lymphocytes, grade of differentiation, mucinous components, and tertiary lymphoid structures. After validation on annotated data sets, these features are integrated with clinical data and used in logistic regression and random forest models to predict MSI status. We validated our approach using 3256 WSI from 2267 patients across 7 cohorts from 5 centers. The method achieved an area under the curve of up to 0.88 across all resection cohorts, and 0.90 on biopsies, performing on par with published black-box deep learning models. Importantly, the learned variable importances strongly correlated with manual scoring systems and aligned with manual pathologist assessments. We observed significant intrapatient heterogeneity in predicted scores, emphasizing the importance of whole-case analysis. Our approach also shows potential as a screening tool that could exclude 41% of patients from gold-standard MSI testing while maintaining 95% sensitivity. This study demonstrates that classifiers based on clinical and validated histologic information can predict MSI status as effectively as black-box models while providing complete interpretability. Our method offers an alternative pathway for understandable, explainable, and trustworthy biomarker prediction in computational pathology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".