Evidence map›Paper›PMID 31519361›Full record

ArticleJournal of pediatric surgery2019

Replicating and identifying large cell neuroblastoma using high-dose intra-tumoral chemotherapy and automated digital analysis.

Jordan S Taylor, Lingdao Sha, Naohiko Ikegaki, Jasmine Zeki, Ryan Deaton, Jamie Harris, Jeannine Coburn, Burcin Yavuz, Amit Sethi, Hiroyuki Shimada and 3 more

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

Article in Journal of pediatric surgery, 2019. 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
0.2field-weighted citation impact, top 46% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

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

13 authors at 5 institutions in 1 country.

Jordan S TaylorDepartment of Surgery, Stanford University, Stanford, CA.
Lingdao ShaDepartment of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL.
Naohiko IkegakiDepartment of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, IL.
Jasmine ZekiDepartment of Surgery, Stanford University, Stanford, CA.
Ryan DeatonDepartment of Pathology, University of Illinois at Chicago, Chicago, IL.
Jamie HarrisDepartment of Surgery, Rush University Medical Center, Chicago, IL.
Jeannine CoburnDepartment of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA; Department of Biomedical Engineering, Tufts University, Medford, MA.
Burcin YavuzDepartment of Biomedical Engineering, Tufts University, Medford, MA.
Amit SethiDepartment of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, IL.
Hiroyuki ShimadaDepartment of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, University of Southern California, Los Angeles, CA.
David L KaplanDepartment of Biomedical Engineering, Tufts University, Medford, MA.
Peter GannDepartment of Pathology, University of Illinois at Chicago, Chicago, IL.
Bill ChiuDepartment of Surgery, Stanford University, Stanford, CA. Electronic address: bhsc@stanford.edu.
University of Illinois Chicago · USStanford University · USTufts University · USChildren's Hospital of Los Angeles · USRush University Medical Center · US

Funding

Focal Sustained Release Chemotherapy-Loaded Biomaterials at Tumor SitesR01NS094218 · NINDS · TUFTS UNIVERSITY MEDFORD · PI CHIU, BILL, KAPLAN, DAVID L. · 2017 to 2021
$1.7M
NINDS NIH HHS R01 NS094218
6 · The paper itself

Abstract

purposeLarge cell neuroblastomas (LCN) are frequently seen in recurrent, high-risk neuroblastoma but are rare in primary tumors. LCN, characterized by large nuclei with prominent nucleoli, predict a poor prognosis. We hypothesize that LCN can be created with high-dose intra-tumoral chemotherapy and identified by a digital analysis system.

methodsOrthotopic mouse xenografts were created using human neuroblastoma and treated with high-dose chemotherapy delivered locally via sustained-release silk platforms, inducing tumor remission. After recurrence, LCN populations were identified on H&E sections manually. Clusters of typical LCN and non-LCN cells were divided equally into training and test sets for digital analysis. Marker-controlled watershed segmentation was used to identify nuclei and characterize their features. Logistic regression was developed to distinguish LCN from non-LCN.

resultsImage analysis identified 15,000 nuclei and characterized 70 nuclear features. A 19-feature model provided AUC >0.90 and 100% accuracy when >30% nuclei/cluster were predicted as LCN. Overall accuracy was 87%.

conclusionsWe recreated LCN using high-dose chemotherapy and developed an automated method for defining LCN histologically. Features in the model provide insight into LCN nuclear phenotypic changes that may be related to increased activity. This model could be adapted to identify LCN in human tumors and correlated with clinical outcomes.

Indexed as

Antineoplastic AgentsNeuroblastomaAnimalsCell NucleusHumansImage Interpretation, Computer-AssistedInjections, IntralesionalMiceNeoplasms, ExperimentalXenograft Model Antitumor AssaysAntineoplastic AgentsIntra-tumoral chemotherapyKELLYLarge cell neuroblastomaNeuroblastomaOrthotopic xenograftSKNAS

Identifiers

PMID31519361
PMCPMC6925654
OpenAlexW2970240688

What OpenQuestion holds

Textmetadata
LicenceTDM
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.