# Prototype-driven fusion of pathology and spatial transcriptomics for interpretable survival prediction

Source: [Prototype-driven fusion of pathology and spatial transcriptomics for interpretable survival prediction](<https://www.sota2.com/research/paper/6992a4ac9ebc499c97406d1e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-17T05:00:04.915Z

Authors: Lihe Liu, Xiaoxi Pan, Yinyin Yuan, Lulu Shang

Published: 2026-02-12T21:59:07.000Z

## Abstract

Whole slide images (WSIs) enable weakly supervised prognostic modeling via multiple instance learning (MIL). Spatial transcriptomics (ST) preserves in situ gene expression, providing a spatial molecular context that complements morphology. As paired WSI-ST cohorts scale to population level, leveraging their complementary spatial signals for prognosis becomes crucial; however, principled cross-modal fusion strategies remain limited for this paradigm. To this end, we introduce PathoSpatial, an interpretable end-to-end framework integrating co-registered WSIs and ST to learn spatially informed prognostic representations. PathoSpatial uses task-guided prototype learning within a multi-level experts architecture, adaptively orchestrating unsupervised within-modality discovery with supervised cross-modal aggregation. By design, PathoSpatial substantially strengthens interpretability while maintaining discriminative ability. We evaluate PathoSpatial on a triple-negative breast cancer cohort with paired ST and WSIs. PathoSpatial delivers strong and consistent performance across five survival endpoints, achieving superior or comparable performance to leading unimodal and multimodal methods. PathoSpatial inherently enables post-hoc prototype interpretation and molecular risk decomposition, providing quantitative, biologically grounded explanations, highlighting candidate prognostic factors. We present PathoSpatial as a proof-of-concept for scalable and interpretable multimodal learning for spatial omics-pathology fusion.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Survival Prediction on TNBC cohort (test)](<https://www.sota2.com/research/sota/survival-prediction-on-tnbc-cohort-test.md>): Survival Prediction; TNBC cohort (test); DRFS C-index: 0.637.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2602.12441>)

[PDF](<https://arxiv.org/pdf/2602.12441.pdf>)