Evidence map›Paper›PMID 39962757›Full record

ReviewJournal of applied clinical medical physics2025

Review of the application of dual-energy CT combined with radiomics in the diagnosis and analysis of lung cancer.

Rongyu Zhang, Hao Zheng, Jie Lin, Junna Wang

Abstract readReview
In one paragraph

Review in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
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

4 authors.

Rongyu ZhangDepartment of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.
Hao ZhengDepartment of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.
Jie LinDepartment of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.
Junna WangDepartment of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.

Funding

Medical and Health Research Project of Zhejiang Province 2023KY853the Zhejiang Traditional Chinese Medicine Science and Technology Project 2023ZL393
6 · The paper itself

Abstract

Lung cancer is one of the most common malignant tumors in the world. Early detection and precise treatment are of great significance to clinical decision-making and patient prognosis. As an emerging imaging technology, dual-energy computed tomography (DECT) has increasingly prominent advantages in multi-parameter and quantitative analysis in assessing the benign and malignant, classification, and prognosis of lung cancer. Radiomics uses an automated high-throughput method to extract a large number of quantitative features from medical images, quantify tumor heterogeneity, monitor tumor development and prognosis, and provide new ideas for the diagnosis and identification of lung cancer. This article will review the application progress of DECT post-processing technology combined with radiomics in lung cancer diagnosis, identification, biomarker and gene prediction, and prognosis assessment.

Indexed as

Image Processing, Computer-AssistedLung NeoplasmsRadiography, Dual-Energy Scanned ProjectionTomography, X-Ray ComputedHumansPrognosisRadiomicsdeep learningdual‐energy CTlung cancerradiomics

Identifiers

PMID39962757
PMCPMC11969089

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.