Evidence map›Paper›PMID 32913224›Full record

ArticleScientific reports2020

Molecular imaging and deep learning analysis of uMUC1 expression in response to chemotherapy in an orthotopic model of ovarian cancer.

Hongwei Zhao, Hasaan Hayat, Xiaohong Ma, Daguang Fan, Ping Wang, Anna Moore

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.6field-weighted citation impact, top 34% 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

6 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

6 authors at 1 institution in 2 countries.

Hongwei ZhaoPrecision Health Program, Michigan State University, East Lansing, MI, 48823, USA.
Hasaan HayatPrecision Health Program, Michigan State University, East Lansing, MI, 48823, USA.
Xiaohong MaPrecision Health Program, Michigan State University, East Lansing, MI, 48823, USA.
Daguang FanPrecision Health Program, Michigan State University, East Lansing, MI, 48823, USA.
Ping WangPrecision Health Program, Michigan State University, East Lansing, MI, 48823, USA. wangpin4@msu.edu.
Anna MoorePrecision Health Program, Michigan State University, East Lansing, MI, 48823, USA. moorea57@msu.edu.
Michigan State University · US

Funding

Predictive Cancer Diagnostics and Therapy ResponseR01CA135650 · NCI · MICHIGAN STATE UNIVERSITY · PI MEDAROVA, ZDRAVKA O, MOORE, ANNA · 2008 to 2018
$4.6M
NCI NIH HHS R01 CA135650NIH HHS R01CA135650
6 · The paper itself

Abstract

Artificial Intelligence (AI) algorithms including deep learning have recently demonstrated remarkable progress in image-recognition tasks. Here, we utilized AI for monitoring the expression of underglycosylated mucin 1 (uMUC1) tumor antigen, a biomarker for ovarian cancer progression and response to therapy, using contrast-enhanced in vivo imaging. This was done using a dual-modal (magnetic resonance and near infrared optical imaging) uMUC1-specific probe (termed MN-EPPT) consisted of iron-oxide magnetic nanoparticles (MN) conjugated to a uMUC1-specific peptide (EPPT) and labeled with a near-infrared fluorescent dye, Cy5.5. In vitro studies performed in uMUC1-expressing human ovarian cancer cell line SKOV3/Luc and control uMUC1

Indexed as

Artificial IntelligenceDeep LearningAnimalsAntineoplastic AgentsApoptosisCell ProliferationDocetaxelFemaleHumansMiceMice, NudeMolecular ImagingMucin-1Ovarian NeoplasmsTumor Cells, CulturedXenograft Model Antitumor AssaysAntineoplastic AgentsDocetaxelMUC1 protein, humanMucin-1

Identifiers

PMID32913224
PMCPMC7484755
OpenAlexW3084190006

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.