Evidence map›Paper›PMID 37873865›Full record

ArticleThe journal of pathology. Clinical research2024

AI-based intra-tumor heterogeneity score of Ki67 expression as a prognostic marker for early-stage ER+/HER2- breast cancer.

Wenqi Lu, Ayat G Lashen, Noorul Wahab, Islam M Miligy, Mostafa Jahanifar, Michael Toss, Simon Graham, Mohsin Bilal, Abhir Bhalerao, Nehal M Atallah and 7 more

Open access · goldAbstract read
In one paragraph

Article in The journal of pathology. Clinical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.9field-weighted citation impact, top 9% of its field
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

8 citing papers in PubMed, 12 citations in OpenAlex.

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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

17 authors at 3 institutions in 2 countries.

Wenqi LuTissue Image Analytics (TIA) Centre, Department of Computer Science, University of Warwick, Coventry, UK.ORCID 0000-0002-7838-0918
Ayat G LashenAcademic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK.
Noorul WahabTissue Image Analytics (TIA) Centre, Department of Computer Science, University of Warwick, Coventry, UK.ORCID 0000-0002-1251-1559
Islam M MiligyAcademic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK.
Mostafa JahanifarTissue Image Analytics (TIA) Centre, Department of Computer Science, University of Warwick, Coventry, UK.
Michael TossAcademic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK.
Simon GrahamTissue Image Analytics (TIA) Centre, Department of Computer Science, University of Warwick, Coventry, UK.
Mohsin BilalTissue Image Analytics (TIA) Centre, Department of Computer Science, University of Warwick, Coventry, UK.
Abhir BhaleraoTissue Image Analytics (TIA) Centre, Department of Computer Science, University of Warwick, Coventry, UK.
Nehal M AtallahAcademic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK.
Shorouk MakhloufAcademic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK.
Asmaa Y IbrahimAcademic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK.
David SneadDepartment of Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
Fayyaz MinhasTissue Image Analytics (TIA) Centre, Department of Computer Science, University of Warwick, Coventry, UK.
Shan E Ahmed RazaTissue Image Analytics (TIA) Centre, Department of Computer Science, University of Warwick, Coventry, UK.
Emad RakhaAcademic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK.
Nasir RajpootTissue Image Analytics (TIA) Centre, Department of Computer Science, University of Warwick, Coventry, UK.
University of Warwick · GBUniversity of Nottingham · GBUniversity Hospitals Coventry and Warwickshire NHS Trust · GB

Funding

Innovate UK 104689
6 · The paper itself

Abstract

Early-stage estrogen receptor positive and human epidermal growth factor receptor negative (ER+/HER2-) luminal breast cancer (BC) is quite heterogeneous and accounts for about 70% of all BCs. Ki67 is a proliferation marker that has a significant prognostic value in luminal BC despite the challenges in its assessment. There is increasing evidence that spatial colocalization, which measures the evenness of different types of cells, is clinically important in several types of cancer. However, reproducible quantification of intra-tumor spatial heterogeneity remains largely unexplored. We propose an automated pipeline for prognostication of luminal BC based on the analysis of spatial distribution of Ki67 expression in tumor cells using a large well-characterized cohort (n = 2,081). The proposed Ki67 colocalization (Ki67CL) score can stratify ER+/HER2- BC patients with high significance in terms of BC-specific survival (p < 0.00001) and distant metastasis-free survival (p = 0.0048). Ki67CL score is shown to be highly significant compared with the standard Ki67 index. In addition, we show that the proposed Ki67CL score can help identify luminal BC patients who can potentially benefit from adjuvant chemotherapy.

Indexed as

Breast NeoplasmsArtificial IntelligenceErb-b2 Receptor Tyrosine KinasesFemaleHumansKi-67 AntigenPrognosisErb-b2 Receptor Tyrosine KinasesKi-67 AntigenAI based prognostic biomarkerscomputational pathologyKi67 expressionluminal breast cancer

Identifiers

PMID37873865
PMCPMC10766021
OpenAlexW4387893610

What OpenQuestion holds

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LicenceCC BY
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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.