Evidence map›Paper›PMID 42649855›Full record

ArticleCancers2026

Slide Selection Introduces Sampling-Induced Prediction Uncertainty in Digital Pathology AI: Evidence from Multi-Slide Breast Cancer Cohorts.

Onur C Koyun, Yongxin Guo, Hao Lu, Muhammet Fatih Demir, Abbas Alili, Nabil Rahoui, Diana Cardona, Metin N Gurcan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancers, 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

8 authors.

Onur C KoyunCenter for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA.ORCID 0000-0002-4755-3935
Yongxin GuoCenter for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA.
Hao LuCenter for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA.ORCID 0000-0002-4547-7780
Muhammet Fatih DemirCenter for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA.ORCID 0009-0004-3666-7525
Abbas AliliCenter for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA.ORCID 0000-0002-7849-6881
Nabil RahouiDepartment of Pathology, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA.ORCID 0009-0008-2111-5380
Diana CardonaDepartment of Pathology, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA.
Metin N GurcanCenter for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA.ORCID 0000-0002-2421-8229

Funding

Efficient and cost-effective breast cancer risk stratification using whole slide histopathology imagesR21CA273665 · NCI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI GURCAN, METIN NAFI · 2023 to 2024
$394k
NCI NIH HHS R21 CA273665
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

21-gene recurrence-score surrogatebreast cancercomputational pathologyHER2multiple-instance learningsampling-induced prediction uncertaintytumor heterogeneitywhole-slide imaging

Identifiers

PMID42649855
PMCPMC13510440

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.