Reading tumor ecosystems from routine histology

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Li et al. introduce CANVAS, an AI framework for translating hematoxylin and eosin images into spatial maps of tumor habitats. By predicting stable, biologically anchored labels with foundation-model image analysis, CANVAS extends habitat mapping to archival pathology samples and suggests a path toward more accessible precision oncology.

Tumors are complex ecosystems comprising a multitude of interdependent cell populations. By measuring proteins and transcripts in situ using multiplex spatial proteomics and transcriptomics, researchers have characterized the spatial organization of tumors across a wide range of cancers, revealing recurrent cellular neighborhoods associated with disease progression and clinical outcomes.1,2 Yet, the high cost and technical complexity of these technologies have confined them to a research setting and impeded their adoption in routine clinical practice. Thus, the potential of spatially resolved molecular profiling to inform diagnosis, prognosis, and treatment decisions remains largely untapped.