ArticleNPJ digital medicine2026
Decoding the ERS-CAF immunoregulatory axis via multimodal AI and its pan-cancer prognostic and therapeutic predictive value.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Cancer-Associated Fibroblast-Targeted Nanomedicine in Solid Tumor Therapy: From Mechanisms of Therapeutic Resistance to Precision Stromal Modulation.International journal of nanomedicine · 2026Review
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Authors and funding
10 authors.
Funding
Abstract
Endoplasmic reticulum stress-related cancer-associated fibroblasts (ERS-CAF) remodel the tumor microenvironment and drive immune exclusion and therapy resistance in chordoma, yet routine and non-invasive readouts of this biology are lacking. We hypothesized that standard pre-operative MRI and H&E whole-slide images (WSI) encode image-based surrogates of ERS-CAF-driven immunoregulation that can be learned and generalized across cancers. Three bulk-transcriptomic reference scores were defined for surrogate supervision, capturing ERS-program activity, ERS-CAF-immuneligand-receptor crosstalk and microenvironmental heterogeneity. In 126 chordoma cases, a stage-wise multimodal framework integrating calibrated WSI attention, gated radiopathomic fusion and domain alignment showed strong concordance with molecular profiles, independent prognostic value and biologically specific localization to fibrotic immune-excluded regions. These associations were generalized in zero-shot analyses to the TCGA pan-cancer. An MRI-only distilled model preserved most predictive performance with substantial gains in efficiency, supporting scalable non-invasive clinical application.
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Registered trials
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