Evidence map›Paper›PMID 41473324›Full record

ArticlebioRxiv : the preprint server for biology2025

Web engine for tumor pathology image retrievals on massive scales.

Zisha Zhong, Jinlin Huang, Xinjing Li, Lijuan Song, Lanqi Gong, Beibei Ru, Rohit Paul, Jason Levine, Yu Zhang, Kenneth Aldape and 1 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

11 authors.

Zisha ZhongCancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health; Bethesda, Maryland 20892, U.S.A.
Jinlin HuangDepartment of Pathology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China, Guangzhou, Guangdong, P.R. China.
Xinjing LiDepartment of Pathology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, P.R. China.
Lijuan SongDepartment of Pathology, Sun Yat-sen University Cancer Center Gansu Hospital, Lanzhou, Gansu, P.R. China.
Lanqi GongCancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health; Bethesda, Maryland 20892, U.S.A.
Beibei RuCancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health; Bethesda, Maryland 20892, U.S.A.
Rohit PaulOffice of the Director, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, U.S.A.
Jason LevineOffice of the Director, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, U.S.A.
Yu ZhangSun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, P.R. China.
Kenneth AldapeLaboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health; Bethesda, Maryland 20892, U.S.A.
Peng JiangCancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health; Bethesda, Maryland 20892, U.S.A.ORCID 0000-0002-7828-5486

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hematoxylin and Eosin staining (H&E) is widely used in clinical practice, but efficient and versatile image retrieval tools are lacking. We developed the H&E Retrieval Engine (HERE, https://hereapp.ccr.cancer.gov) to analyze patient cases based on image similarities to database records. Using H&E image regions as input, HERE searches 21.2 terabytes of whole-slide images from multiple tumor histopathology cohorts through a 12.1-gigabyte memory index, and returns top images containing regions similar to the query. HERE scans high-resolution images in the database using accurate artificial intelligence encoding and ultra-efficient hierarchical skip indexing. HERE demonstrated performance superior to existing image retrieval tools based on blinded pathologist scoring using benchmark queries that represent key image features of human tumors. By pairing spatial transcriptomics with H&E images, HERE also enables retrieving image features from gene transcriptomics input and identifies molecular pathways associated with tumor histologies.

Identifiers

PMID41473324
PMCPMC12747273

What Socratic 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 Socratic graph.