Generating 2.5D pathology for enhanced viewing and AI diagnosis.

Ekaterina Redekop, Mara Pleasure, Zichen Wang, Anthony Sisk, Yang Zong, Kimberly Flores, William Speier, Corey W Arnold

Abstract read

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

8 authors.

Ekaterina RedekopDepartment of Bioengineering, University of California, Los Angeles, USA.

Mara PleasureMedical Informatics, University of California, Los Angeles, USA.

Zichen WangDepartment of Bioengineering, University of California, Los Angeles, USA.

Anthony SiskDepartment of Pathology, University of California, Los Angeles, USA.

Yang ZongDepartment of Pathology, University of California, Los Angeles, USA.

Kimberly FloresDepartment of Pathology, University of California, Los Angeles, USA.

William SpeierDepartment of Bioengineering, University of California, Los Angeles, USA.

Corey W ArnoldDepartment of Bioengineering, University of California, Los Angeles, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Histological analysis of biopsy samples by pathologists can require the evaluation of complex three-dimensional (3D) tissue structures. This process involves studying the same tissue region across slides, which requires laborious zooming and panning for localization. Additionally, standard deep learning frameworks typically focus on cross-sections cut from biopsy specimens, limiting their ability to capture 3D tissue spatial information. We present a novel framework that constructs 2.5D biopsy cores via the extraction and co-alignment of serial tissue sections using a novel morphology-preserving alignment framework. These 2.5D cores can then be used for enhanced viewing by pathologists and as input to video transformer models that can capture depth-wide spatial dependencies. We used our framework to construct 2.5D cores for 10,210 prostate biopsies, 156 breast biopsies, and 1869 renal biopsies. To evaluate the utility of the cores for downstream tasks, we performed additional studies in prostate cancer by: (1) training a deep learning-based cancer grading model and (2) conducting a reader study with pathologists.

Indexed as

2.5D biopsyDeep learningDigital pathologyDigital viewerProstate cancerVision transformer

Identifiers

PMID40822324

DOI10.1016/j.jpi.2025.100463

PMCPMC12355132

What OpenQuestion holds

Textmetadata

LicenceCC BY

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Registered trials

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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.