Evidence map›Paper›PMID 38179181›Full record

ArticleView (Beijing, China)2023

Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives.

Wenpeng Huang, Zihao Tao, Muhsin H Younis, Weibo Cai, Lei Kang

Abstract read
In one paragraph

Article in View (Beijing, China), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

2 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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

5 authors.

Wenpeng HuangDepartment of Nuclear Medicine, Peking University First Hospital, Beijing, China.
Zihao TaoDepartment of Nuclear Medicine, Peking University First Hospital, Beijing, China.
Muhsin H YounisDepartments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Weibo CaiDepartments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Lei KangDepartment of Nuclear Medicine, Peking University First Hospital, Beijing, China.ORCID 0000-0001-8729-4547

Funding

UW COMPREHENSIVE CANCER CENTER SUPPORTP30CA014520 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI Justine Yang Bruce · 1985 to 2026
$142.6M
U.W. RADIOLOGICAL SCIENCES TRAINING PROGRAMT32CA009206 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI Bryan Patrick Bednarz · 1985 to 2026
$12.1M
NCI NIH HHS P30 CA014520NCI NIH HHS T32 CA009206
6 · The paper itself

Abstract

Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual information about pathology, immunophenotype, and tumor microenvironment. It uses automated or semiautomated quantitative analysis of high-dimensional images to improve characterization, diagnosis, and prognosis. Recent years have seen a rapid increase in radiomics applications in nuclear medicine, leading to some promising research results in digestive system oncology, which have been driven by big data analysis and the development of artificial intelligence. Although radiomics advances one step further toward the non-invasive precision medical analysis, it is still a step away from clinical application and faces many challenges. This review article summarizes the available literature on digestive system tumors regarding radiomics in nuclear medicine. First, we describe the workflow and steps involved in radiomics analysis. Subsequently, we discuss the progress in clinical application regarding the utilization of radiomics for distinguishing between various diseases and evaluating their prognosis, and demonstrate how radiomics advances this field. Finally, we offer our viewpoint on how the field can progress by addressing the challenges facing clinical implementation.

Indexed as

artificial intelligencedigestive system tumorsmachine learningmedical imagingnon-invasive predictionnuclear medicinepositron emission tomographyradiomics

Identifiers

PMID38179181
PMCPMC10766416

What OpenQuestion holds

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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.