Evidence map›Paper›PMID 36184987›Full record

ArticleTechnology in cancer research & treatment

Compute Tomography Radiomics Analysis on Whole Pancreas Between Healthy Individual and Pancreatic Ductal Adenocarcinoma Patients: Uncertainty Analysis and Predictive Modeling.

Shuo Wang, Chi Lin, Alexander Kolomaya, Garett P Ostdiek-Wille, Jeffrey Wong, Xiaoyue Cheng, Yu Lei, Chang Liu

Open access · goldAbstract read
In one paragraph

Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 11% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.

  1. Pooled it
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  5. Article
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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

8 authors at 3 institutions in 1 country.

Shuo WangDepartment of Radiation Oncology, 12284University of Nebraska Medical Center, Omaha, NE, USA.ORCID 0000-0002-3814-606X
Chi LinDepartment of Radiation Oncology, 12284University of Nebraska Medical Center, Omaha, NE, USA.
Alexander KolomayaCollege of Medicine, 12284University of Nebraska Medical Center, Omaha, NE, USA.
Garett P Ostdiek-WilleCollege of Medicine, 12284University of Nebraska Medical Center, Omaha, NE, USA.
Jeffrey WongDepartment of Radiation Oncology, 12284University of Nebraska Medical Center, Omaha, NE, USA.
Xiaoyue ChengDepartment of Mathematics, 14720University of Nebraska Omaha, Omaha, NE, USA.
Yu LeiDepartment of Radiation Oncology, 115467Barrow Neurological Institute, Phoenix, AZ, USA.
Chang LiuLX Consulting, LLC, Novi, MI, USA.
University of Nebraska Medical Center · USBarrow Neurological Institute · USUniversity of Nebraska at Omaha · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Radiomics is a rapidly growing field that quantitatively extracts image features in a high-throughput manner from medical imaging. In this study, we analyzed the radiomics features of the whole pancreas between healthy individuals and pancreatic cancer patients, and we established a predictive model that can distinguish cancer patients from healthy individuals based on these radiomics features.

Indexed as

Carcinoma, Pancreatic DuctalPancreatic NeoplasmsHumansPancreasRetrospective StudiesTomography, X-Ray ComputedUncertaintycomputed tomographypancreatic cancerradiomicsrandom forestuncertainty analysis

Identifiers

PMID36184987
PMCPMC9530578
OpenAlexW4300688132

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.