Evidence map›Paper›PMID 42056496›Full record

ArticleNature medicine2026

An agentic framework for autonomous scientific discovery in cancer pathology.

Florian Trost, Bide Zhang, Ines Aring, Marcus Bauer, Lennert Glamann, Michael Wessolly, Kyra Johnson, Heike Göbel, Tristan Lerbs, Taban Sangenne and 16 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. AI agents in cancer imaging: Concepts, advances, and clinical perspectives.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

26 authors.

Florian Trost *Institute of Pathology, University Hospital Cologne, Medical Faculty, University of Cologne, Cologne, Germany.
Bide Zhang *Institute of Pathology, University Hospital Cologne, Medical Faculty, University of Cologne, Cologne, Germany.ORCID http://orcid.org/0000-0002-6571-0033
Ines AringInstitute of Pathology, University Hospital Cologne, Medical Faculty, University of Cologne, Cologne, Germany.ORCID http://orcid.org/0009-0003-8193-0153
Marcus BauerInstitute of Pathology, University Hospital Halle, Halle, Germany.
Lennert GlamannInstitute of Pathology, University Hospital Halle, Halle, Germany.
Michael WessollyInstitute of Pathology, University Hospital Essen, Essen, Germany.
Kyra JohnsonInstitute of Pathology, University Hospital Cologne, Medical Faculty, University of Cologne, Cologne, Germany.
Heike GöbelInstitute of Pathology, University Hospital Cologne, Medical Faculty, University of Cologne, Cologne, Germany.ORCID http://orcid.org/0000-0002-0171-7025
Tristan LerbsInstitute of Pathology, University Hospital Cologne, Medical Faculty, University of Cologne, Cologne, Germany.
Taban SangenneInstitute of Pathology, University Hospital Cologne, Medical Faculty, University of Cologne, Cologne, Germany.ORCID http://orcid.org/0009-0009-2348-3483
Peter HerrmannInstitute of Pathology, University Hospital Essen, Essen, Germany.
Fabian MairingerInstitute of Pathology, University Hospital Essen, Essen, Germany.
Christopher KoppDepartment of Otorhinolaryngology, Head and Neck Surgery, Faculty of Medicine, University of Cologne, Cologne, Germany.
Sebastian MichelsClinic I of Internal Medicine, Center for Integrated Oncology, University Hospital Cologne, Cologne, Germany.
Anna RasokatClinic I of Internal Medicine, Center for Integrated Oncology, University Hospital Cologne, Cologne, Germany.
Matthias HeldweinDepartment of Cardiothoracic Surgery, University Hospital Cologne, Cologne, Germany.ORCID http://orcid.org/0000-0002-2084-795X
Steffen WagnerDepartment of Otorhinolaryngology, Head and Neck Surgery, University of Giessen, Giessen, Germany.
Birgid Schömig-MarkiefkaInstitute of Pathology, University Hospital Cologne, Medical Faculty, University of Cologne, Cologne, Germany.
Jürgen WolfClinic I of Internal Medicine, Center for Integrated Oncology, University Hospital Cologne, Cologne, Germany.
Sylvia HartmannInstitute of Pathology, University Hospital Essen, Essen, Germany.ORCID http://orcid.org/0000-0003-3424-1091
Claudia WickenhauserInstitute of Pathology, University Hospital Halle, Halle, Germany.
Andrey BychkovKameda Medical Center, Kamogawa, and Nagasaki University, Nagasaki, Japan.ORCID http://orcid.org/0000-0002-4203-5696
Jens Peter KlussmannDepartment of Otorhinolaryngology, Head and Neck Surgery, Faculty of Medicine, University of Cologne, Cologne, Germany.ORCID http://orcid.org/0000-0002-8223-7954
Alexander QuaasInstitute of Pathology, University Hospital Cologne, Medical Faculty, University of Cologne, Cologne, Germany.
Reinhard BuettnerInstitute of Pathology, University Hospital Cologne, Medical Faculty, University of Cologne, Cologne, Germany.ORCID http://orcid.org/0000-0001-8806-4786
Yuri TolkachInstitute of Pathology, University Hospital Cologne, Medical Faculty, University of Cologne, Cologne, Germany. iurii.tolkach@uk-koeln.de.ORCID http://orcid.org/0000-0001-5239-2841

Funding

Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) FED-PATH
6 · The paper itself

Abstract

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.

Indexed as

Artificial IntelligenceNeoplasmsBiomarkers, TumorBreast NeoplasmsColorectal NeoplasmsFemaleHumansLung NeoplasmsPrognosisBiomarkers, Tumor

Identifiers

PMID42056496
PMCPMC13278948

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.