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<p>Artificial intelligence has advanced cancer pathology, but many systems still depend on hand-crafted features, are hard to explain and rely on fragmented workflows. We introduce SPARK (System of Pathology Agents for Research and Knowledge), a foundational agentic artificial intelligence approach that uses language as a universal interface to autonomously generate biologically driven concepts for tumor analysis. SPARK turns biological ideas into analytical tools and works directly with complex pathology data without extra model training. We evaluated SPARK across 18 patient cohorts spanning five cancer types (lung adenocarcinoma, lung squamous cell carcinoma, colorectal cancer, breast cancer and oropharyngeal squamous cell carcinoma) and more than 5,400 patients with available histopathology images and clinical/follow-up information, in both prognostic and predictive settings and on a well characterized spatial biology breast cancer dataset (patient <i>n</i> = 625). We found that SPARK produced clinically and biologically relevant concepts correlated with prognosis, known pathological variables and predictive biomarkers, including patterns of tumor progression and temporal change inferred from static images. A dedicated module allows for human interaction with SPARK. Further prospective validation is needed to evaluate the clinical utility of the tools created by SPARK. All code, parameters and results are openly released to help researchers and clinicians improve diagnostic precision and deepen tumor biology insights.</p>

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An agentic framework for autonomous scientific discovery in cancer pathology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月29日

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  • Florian Trost,
  • Bide Zhang,
  • Ines Aring,
  • Marcus Bauer,
  • Lennert Glamann,
  • Michael Wessolly,
  • Kyra Johnson,
  • Heike Göbel,
  • Tristan Lerbs,
  • Taban Sangenne,
  • Peter Herrmann,
  • Fabian Mairinger,
  • Christopher Kopp,
  • Sebastian Michels,
  • Anna Rasokat,
  • Matthias Heldwein,
  • Steffen Wagner,
  • Birgid Schömig-Markiefka,
  • Jürgen Wolf,
  • Sylvia Hartmann,
  • Claudia Wickenhauser,
  • Andrey Bychkov,
  • Jens Peter Klussmann,
  • Alexander Quaas,
  • Reinhard Buettner,
  • Yuri Tolkach

摘要

Artificial intelligence has advanced cancer pathology, but many systems still depend on hand-crafted features, are hard to explain and rely on fragmented workflows. We introduce SPARK (System of Pathology Agents for Research and Knowledge), a foundational agentic artificial intelligence approach that uses language as a universal interface to autonomously generate biologically driven concepts for tumor analysis. SPARK turns biological ideas into analytical tools and works directly with complex pathology data without extra model training. We evaluated SPARK across 18 patient cohorts spanning five cancer types (lung adenocarcinoma, lung squamous cell carcinoma, colorectal cancer, breast cancer and oropharyngeal squamous cell carcinoma) and more than 5,400 patients with available histopathology images and clinical/follow-up information, in both prognostic and predictive settings and on a well characterized spatial biology breast cancer dataset (patient n = 625). We found that SPARK produced clinically and biologically relevant concepts correlated with prognosis, known pathological variables and predictive biomarkers, including patterns of tumor progression and temporal change inferred from static images. A dedicated module allows for human interaction with SPARK. Further prospective validation is needed to evaluate the clinical utility of the tools created by SPARK. All code, parameters and results are openly released to help researchers and clinicians improve diagnostic precision and deepen tumor biology insights.