Evidence map›Paper›PMID 42368023›Full record

ArticlebioRxiv : the preprint server for biology2026

PhenoBIC: operator-free single-cell spatial phenotyping in multiplex imaging data using deep learning of cell staining patterns.

Abishek Sankaranarayanan, Chenkai Zhao, Madeline Gabriela Hernandez, Elizabeth A Clemens, Kimberly S Smythe, Anum S Kazerouni, Lisa L Carr, Christopher I Li, Savannah C Partridge, Shaveta Vinayak and 1 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. 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.

Abishek SankaranarayananDepartment of Chemical Engineering, University of Washington, Seattle, WA 98195, USA.ORCID 0000-0001-9456-1180
Chenkai ZhaoDepartment of Biochemistry, University of Washington, Seattle, WA 98195, USA.
Madeline Gabriela HernandezDepartment of Chemical Engineering, University of Washington, Seattle, WA 98195, USA.
Elizabeth A ClemensDepartment of Chemical Engineering, University of Washington, Seattle, WA 98195, USA.
Kimberly S SmytheTranslational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
Anum S KazerouniDepartment of Radiology, University of Washington, Seattle, WA 98195, USA.
Lisa L CarrDepartment of Chemical Engineering, University of Washington, Seattle, WA 98195, USA.ORCID 0000-0002-2679-836X
Christopher I LiDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington.
Savannah C PartridgeDepartment of Radiology, University of Washington, Seattle, WA 98195, USA.
Shaveta VinayakDivision of Hematology/Oncology, University of Washington, Seattle, WA 98195, USA.
Shachi MittalDepartment of Chemical Engineering, University of Washington, Seattle, WA 98195, USA.ORCID 0000-0002-5837-3411

Funding

Translational Bioimaging Core Shared ResourceP30CA015704 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Eric Collisson · 1985 to 2026
$296.4M
Quantitative characterization of tumor heterogeneity using habitat imaging for the prediction of patient outcome in triple negative breast cancerK99CA293004 · NCI · UNIVERSITY OF WASHINGTON · PI KAZEROUNI, ANUM SYED · 2024 to 2025
$203k
NCI NIH HHS K99 CA293004NCI NIH HHS P30 CA015704
6 · The paper itself

Abstract

Multiplex imaging is a valuable tool for spatially examining tissue microenvironments at the single-cell level to uncover biological and clinical insights. However, most multiplex image analysis workflows currently require manual intervention for cell phenotyping, which slows progress, demands human effort, and yields operator-dependent outputs. Here, we developed PhenoBIC, a pre-trained deep learning model for image classification of the multiplexed biomarker signals in a cell (

Identifiers

PMID42368023
PMCPMC13308180

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.