Evidence map›Paper›PMID 33927352›Full record

ArticleBritish journal of cancer2021

A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma.

Amin Zadeh Shirazi, Mark D McDonnell, Eric Fornaciari, Narjes Sadat Bagherian, Kaitlin G Scheer, Michael S Samuel, Mahdi Yaghoobi, Rebecca J Ormsby, Santosh Poonnoose, Damon J Tumes and 1 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in British journal of cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
4.0field-weighted citation impact, top 5% 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

26 citing papers in PubMed, 38 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

11 authors at 6 institutions in 3 countries.

Amin Zadeh Shirazi *Centre for Cancer Biology, SA Pathology and University of South Australia, Adelaide, SA, Australia.ORCID http://orcid.org/0000-0002-1906-9900
Mark D McDonnell *Computational Learning Systems Laboratory, UniSA STEM, University of South Australia, Mawson Lakes, SA, Australia.ORCID http://orcid.org/0000-0002-7009-3869
Eric Fornaciari *Department of Mathematics of Computation, University of California, Los Angeles (UCLA), CA, USA.
Narjes Sadat BagherianMashhad University of Medical Sciences, Mashhad, Iran.
Kaitlin G ScheerCentre for Cancer Biology, SA Pathology and University of South Australia, Adelaide, SA, Australia.ORCID http://orcid.org/0000-0002-2103-0378
Michael S SamuelCentre for Cancer Biology, SA Pathology and University of South Australia, Adelaide, SA, Australia.ORCID http://orcid.org/0000-0001-7880-6379
Mahdi YaghoobiElectrical and Computer Engineering Department, Department of Artificial Intelligence, Islamic Azad University, Mashhad Branch, Mashhad, Iran.
Rebecca J OrmsbyFlinders Health and Medical Research Institute, College of Medicine & Public Health, Flinders University, Adelaide, SA, Australia.ORCID http://orcid.org/0000-0002-4256-7481
Santosh PoonnooseFlinders Health and Medical Research Institute, College of Medicine & Public Health, Flinders University, Adelaide, SA, Australia.ORCID http://orcid.org/0000-0002-9486-4904
Damon J TumesCentre for Cancer Biology, SA Pathology and University of South Australia, Adelaide, SA, Australia.ORCID http://orcid.org/0000-0001-5709-857X
Guillermo A Gomez *Centre for Cancer Biology, SA Pathology and University of South Australia, Adelaide, SA, Australia. Guillermo.Gomez@unisa.edu.au.ORCID http://orcid.org/0000-0002-0494-2404
South Australia Pathology · AUFlinders University · AUIslamic Azad University, Mashhad · IRMashhad University of Medical Sciences · IRUniversity of California, Los Angeles · USUniversity of South Australia · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGlioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. However, a spatial characterisation of gene signatures and the cell types expressing these in different tumour locations is still lacking.

methodsWe have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Correlation analysis between segmentation results from tumour images together with matched RNA expression data was performed to identify genetic signatures that are specific to different tumour regions.

resultsWe found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Further in silico cell ontology analysis along with single-cell RNA sequencing data from resected glioblastoma tissue samples showed that these tumour regions had different gene signatures, whose expression was driven by different cell types in the regional tumour microenvironment. Our results further pointed to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival.

conclusionsThis work identified key histopathological features that correlate with patient survival and detected spatially associated genetic signatures that contribute to tumour-stroma interactions and which should be investigated as new targets in glioblastoma. The source codes and datasets used are available in GitHub: https://github.com/amin20/GBM_WSSM .

Indexed as

Gene Regulatory NetworksBrain NeoplasmsDeep LearningGene Expression ProfilingGene Expression Regulation, NeoplasticGlioblastomaHumansNeural Networks, ComputerRadiographic Image Interpretation, Computer-AssistedSingle-Cell AnalysisStem Cell NicheSurvival AnalysisTumor Microenvironment

Identifiers

PMID33927352
PMCPMC8329064
OpenAlexW3157774828

What OpenQuestion holds

Textmetadata
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.