Evidence map›Paper›PMID 42721959›Full record

ArticleCancer cell2026

Ensemble learning of pathology foundation models for precision oncology.

Xiangde Luo, Xiyue Wang, Feyisope Eweje, Xiaoming Zhang, Juan Luis Gomez Marti, Sarah Cascarino, Sen Yang, Yuchen Li, Ryan Quinton, Jinxi Xiang and 17 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer cell, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. 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

27 authors.

Xiangde LuoDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Xiyue WangDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Feyisope EwejeDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Xiaoming ZhangDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Juan Luis Gomez MartiDepartment of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Sarah CascarinoDepartment of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Sen YangDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Yuchen LiDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Ryan QuintonDepartment of Medicine (Oncology), Stanford University School of Medicine, Stanford, CA, USA.
Jinxi XiangDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Yuanfeng JiDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Zhe LiDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Yijiang ChenDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Colin BergstromDepartment of Medicine (Oncology), Stanford University School of Medicine, Stanford, CA, USA.
Ted KimDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Francesca Maria OlguinDepartment of Medicine (Oncology), Stanford University School of Medicine, Stanford, CA, USA.
Kelley YuanDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Matthew AbikenariDepartment of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
Andrew HeiderDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Sierra WillensDepartment of Medicine (Oncology), Stanford University School of Medicine, Stanford, CA, USA.
Sanjeeth RajaramDepartment of Medicine (Oncology), Stanford University School of Medicine, Stanford, CA, USA.
Robert WestDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Joel NealDepartment of Medicine (Oncology), Stanford University School of Medicine, Stanford, CA, USA.
Adam SchoenfeldDepartment of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Maximilian DiehnDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Chad VanderbiltDepartment of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Ruijiang LiDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA; Stanford Institute for Human-Centered Artificial Intelligence, Stanford, CA, USA. Electronic address: rli2@stanford.edu.

Funding

Computational imaging approaches to personalized gastric cancer treatmentR01CA269559 · NCI · STANFORD UNIVERSITY · PI Ruijiang Li · 2023 to 2026
$2.3M
Noninvasive imaging and blood biomarkers for personalized lung cancer immunotherapyR01CA290715 · NCI · STANFORD UNIVERSITY · PI Maximilian Diehn, Ruijiang Li · 2024 to 2026
$1.9M
MRI and blood biomarkers of neoadjuvant therapy response and outcomes in rectal cancerR01CA285456 · NCI · STANFORD UNIVERSITY · PI Ruijiang Li · 2024 to 2026
$1.6M
NCI NIH HHS R01 CA269559NCI NIH HHS R01 CA285456NCI NIH HHS R01 CA290715
6 · The paper itself

Abstract

Histopathology is essential for cancer diagnosis and treatment selection, and pathology foundation models learn visual representations from whole-slide images (WSIs). However, existing foundation models are trained on disparate datasets with varying strategies, leading to inconsistent performance and limited generalizability. Here, we introduce ELF (Ensemble Learning of Foundation models), which integrates five pretrained pathology foundation models into unified slide-level representations. Trained on 53,699 WSIs spanning 20 anatomical sites, ELF leverages ensemble learning to capture complementary information across models. ELF's slide-level architecture is designed for data-efficient downstream evaluation, including settings with limited data such as therapeutic response prediction. We evaluate ELF for disease classification and biomarker detection, as well as anticancer and immunotherapy response prediction across multiple cancer types. ELF achieves higher performance than the evaluated constituent and slide-level foundation models across the tested tasks, supporting further evaluation of ensemble learning for pathology applications in oncology.

Indexed as

anticancer therapy response predictioncomputational pathologyensemble learningimmunotherapy response predictionmolecular biomarker detectionpathology foundation modelsprecision oncology

Identifiers

PMID42721959
PMCPMC13564370

What OpenQuestion holds

Textmetadata
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.