Evidence map›Paper›PMID 40520331›Full record

ArticleJournal of pathology informatics2025

PD-L1 expression assessment in Angiosarcoma improves with artificial intelligence support.

F H Reith, A Jarosch, J P Albrecht, F Ghoreschi, A Flörcken, A Dörr, S Roohani, F M Schäfer, R Öllinger, S Märdian and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of pathology informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

17 authors.

F H ReithMax Delbrück Center for Molecular Medicine in the Helmholtz Association.
A JaroschCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Department of Pathology, Berlin, Germany.
J P AlbrechtMax Delbrück Center for Molecular Medicine in the Helmholtz Association.
F GhoreschiCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Dermatology, Berlin, Germany.
A FlörckenCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Department of Hematology, Oncology, and Tumor Immunology, Berlin, Germany.
A DörrCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Department of Hematology, Oncology, and Tumor Immunology, Berlin, Germany.
S RoohaniGerman Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany.
F M SchäferCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Berlin, Germany.
R ÖllingerCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Surgery, Experimental Surgery, Berlin, Germany.
S MärdianCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin and Berlin Institute of Health, Center for Musculoskeletal Surgery, Berlin, Germany.
K TielkingCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Department of Pathology, Berlin, Germany.
P BischoffCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Department of Pathology, Berlin, Germany.
N FrühaufCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Department of Hematology, Oncology, and Tumor Immunology, Berlin, Germany.
F BrandesInselspital, Bern University Hospital, University of Bern, Department of Medical Oncology, Bern, Switzerland.
D HorstCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Department of Pathology, Berlin, Germany.
C SersCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Department of Pathology, Berlin, Germany.
D KainmüllerMax Delbrück Center for Molecular Medicine in the Helmholtz Association.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tumoral PD-L1 expression is assessed to weigh immunotherapy options in the treatment of various types of cancer. To determine PD-L1 expression, each tumor cell needs to be assessed to calculate the percentage of PD-L1 positive tumor cells, called tumor proportion score (TPS). Pathologists cannot evaluate each cell individually due to time constraints and thus need to approximate TPS, which has been shown to result in low concordance rates. Decision quality could be improved by an AI-based TPS prediction tool which serves as a "second opinion". Establishing such a tool requires a certain amount of training data, which manifests a bottleneck for rare cancer types such as Angiosarcoma. To address this challenge, we developed and open sourced a pipeline that leverages pre-trained and generalist models to achieve strong TPS prediction performance on limited data. Pathologists were asked to reassess patients for which their TPS strongly disagreed with the AI's prediction. In many of these cases, pathologists updated their TPS score, improving their assessment, thus demonstrating the technical feasibility and practical value of AI-based TPS scoring assistance for rare cancers.

Indexed as

AI-assisted diagnosisAngiosarcomaArtificial intelligenceDigital pathologyImmunotherapyPD-L1 expression

Identifiers

PMID40520331
PMCPMC12166781

What OpenQuestion holds

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LicenceCC BY
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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.