Evidence map›Paper›PMID 41889495›Full record

ArticleFrontiers in medicine2026

Integration of dual-energy CT parameters and radiomics features for non-invasive prediction of

Nan Jiang, Yan Zhang, Gang-Feng Li, Xiao-Yan Qu, Wen-Xiu Wang, Rong Hou, Hong-Juan Ma, Yang Yang, Ying Yu, Guang-Bin Cui

Abstract read
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In one paragraph

Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

10 authors.

Nan Jiang *Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
Yan Zhang *Department of Pathology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
Gang-Feng LiDepartment of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
Xiao-Yan QuDepartment of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
Wen-Xiu WangDepartment of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
Rong HouDepartment of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
Hong-Juan MaFunctional and Molecular Imaging Key Lab of Shaanxi Province, Xi'an, Shaanxi, China.
Yang YangDepartment of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
Ying YuDepartment of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
Guang-Bin CuiDepartment of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The non-invasive characterization of the tumor microenvironment (TME) is essential for stratifying non-small cell lung cancer (NSCLC) patients who may benefit from immunotherapy. This study investigates a novel approach by integrating dual-energy CT (DECT) parameters with radiomics to quantitatively assess stromal fibrosis (via Methods: In this prospective study, 70 treatment-naive NSCLC patients were enrolled. Preoperative DECT scans were used to extract both DECT parameters and radiomics features. Corresponding surgical specimens were analyzed to determine the area percentage of Results: For predicting high Conclusion: The integration of DECT and radiomics presents a feasible, non-invasive strategy to assess specific TME components in NSCLC, underscoring the complementary value of different imaging data types towards developing biomarkers for personalized oncology.

Indexed as

CD8 + T lymphocytedual energy CTnon-small cell lung cancerradiomicstumor microenvironmentα-SMA

Identifiers

PMID41889495
PMCPMC13013019

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Registered trials

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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.