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From prediction to interpretation in computational pathology.

Jialu Yao¹, Zhi Huang²

¹Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.

|June 25, 2026

Summary

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PathPrism offers interpretable spatial tissue analysis from histopathology slides. This computational pathology framework aids biomarker discovery and clinical prediction, moving beyond black-box models.

Area of Science:

Computational pathology
Histopathology
Biomedical imaging analysis

Background:

Computational pathology has largely relied on 'black-box' predictive models.
Interpretable models are needed for deeper biological insights and clinical translation.

Purpose of the Study:

To introduce PathPrism, a novel computational framework for analyzing histopathology slides.
To demonstrate PathPrism's utility in biomarker discovery, clinical prediction, and hypothesis generation.
To showcase a shift towards interpretable methods in computational pathology.

Main Methods:

Development of PathPrism, a framework generating interpretable spatial representations of tissue organization.
Application of PathPrism to routine histopathology slides.

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Main Results:

PathPrism enables the extraction of biologically meaningful information from tissue structures.
The framework supports identification of potential biomarkers and improves clinical prediction accuracy.
Spatial representations facilitate hypothesis generation for further research.

Conclusions:

PathPrism represents a significant advancement in computational pathology, emphasizing interpretability.
Interpretable spatial analysis of histopathology slides can enhance biomarker discovery and clinical decision-making.
The framework supports a transition towards more transparent and biologically relevant computational pathology tools.