Evidence map›Paper›PMID 42302781›Full record

ArticleCell2026

Cellular architecture and neighborhood-informed virtual spatial tumor profiling from histopathology.

Yuchen Li, Zhe Li, Ryan Quinton, Yuanfeng Ji, Xiaoming Zhang, Jinxi Xiang, Xiyue Wang, Sen Yang, Feyisope Eweje, Yijiang Chen and 14 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. 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

24 authors.

Yuchen LiDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Zhe 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.
Yuanfeng JiDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Xiaoming ZhangDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Jinxi XiangDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Xiyue WangDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Sen YangDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Feyisope EwejeDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Yijiang ChenDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Xiangde LuoDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Yuanyuan LiCell Sciences Imaging Facility, Stanford University School of Medicine, Stanford, CA, USA.
Jonathan MulhollandCell Sciences Imaging Facility, Stanford University School of Medicine, Stanford, CA, USA.
Siwei ChenProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 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.
Sierra WillensDepartment of Medicine (Oncology), Stanford University School of Medicine, Stanford, CA, USA.
Steven H LinDepartment of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Jeffrey J NirschlDepartment of Pathology, 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.
Maximilian DiehnDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, 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

The tumor microenvironment (TME) critically shapes disease progression and therapeutic resistance. However, a comprehensive understanding of its spatial architecture remains elusive, and clinical translation is challenging. Here, we present cellular architecture and neighborhood-informed virtual AI-driven spatial profiling (CANVAS), an artificial intelligence platform that infers tumor ecological habitats from hematoxylin and eosin (H&E) histopathology. Built on an atlas of over 18 million cells profiled by 41-plex spatial proteomics across 457 patients with non-small cell lung cancer, CANVAS establishes 10 reproducible cellular neighborhoods (CNs) capturing conserved spatial organization of the TME. Through multimodal alignment and foundation-model-based morphological encoding, CANVAS predicts CN-anchored habitat structures from H&E slides and enables clinical evaluation in over 5,000 patients spanning 9 cancer types. Across patient cohorts, CANVAS supports prognostic modeling, spatial ecotype stratification, and immunotherapy outcome prediction. These results establish CANVAS as a clinically scalable platform for spatial profiling, bridging single-cell analysis to population-level insight and enabling precision oncology.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsNeoplasmsTumor MicroenvironmentArtificial IntelligenceHumansImmunotherapyProteomicscellular neighborhoodshistology-based AIimmunotherapy predictionspatial proteomicstumor microenvironment

Identifiers

PMID42302781
PMCPMC13345706

What OpenQuestion holds

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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.