Evidence map›Paper›PMID 39083142›Full record

ArticleClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2025

The potential value of dual-energy CT radiomics in evaluating CD8

Ruobing Li, Xue Bing, Xinyou Su, Chunling Zhang, Haitao Sun, Zhengjun Dai, Aimei Ouyang

Abstract read
PubMed Publisher
In one paragraph

Article in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

7 authors.

Ruobing LiDepartment of Radiology, Central Hospital Affiliated to Shandong First Medical University, 105 JieFang Road, Jinan, 250013, China.
Xue BingDepartment of Radiology, Central Hospital Affiliated to Shandong First Medical University, 105 JieFang Road, Jinan, 250013, China.
Xinyou SuDepartment of Oncology, Central Hospital Affiliated to Shandong First Medical University, Jinan, 250013, China.
Chunling ZhangDepartment of Radiology, Central Hospital Affiliated to Shandong First Medical University, 105 JieFang Road, Jinan, 250013, China.
Haitao SunDepartment of Radiology, Central Hospital Affiliated to Shandong First Medical University, 105 JieFang Road, Jinan, 250013, China.
Zhengjun DaiScientific Research Department, Huiying Medical Technology Co, Ltd, Beijing, 100192, China.
Aimei OuyangDepartment of Radiology, Central Hospital Affiliated to Shandong First Medical University, 105 JieFang Road, Jinan, 250013, China. 13370582510@163.com.ORCID http://orcid.org/0000-0002-2318-8654

Funding

Central Guidance on Local Science and Technology Development Fund Project of Shandong Province YDZX2021012Jinan Clinical Medical Science and Technology Innovation Program 202019036
6 · The paper itself

Abstract

purposeThis study aims to develop radiomics models and a nomogram based on machine learning techniques, preoperative dual-energy computed tomography (DECT) images, clinical and pathological characteristics, to explore the tumor microenvironment (TME) of clear cell renal cell carcinoma (ccRCC).

methodsWe retrospectively recruited of 87 patients diagnosed with ccRCC through pathological confirmation from Center I (training set, n = 69; validation set, n = 18), and collected their DECT images and clinical information. Feature selection was conducted using variance threshold, SelectKBest, and the least absolute shrinkage and selection operator (LASSO). Radiomics models were then established using 14 classifiers to predict TME cells. Subsequently, we selected the most predictive radiomics features to calculate the radiomics score (Radscore). A combined model was constructed through multivariate logistic regression analysis combining the Radscore and relevant clinical characteristics, and presented in the form of a nomogram. Additionally, 17 patients were recruited from Center II as an external validation cohort for the nomogram. The performance of the models was assessed using methods such as the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).

resultsThe validation set AUC values for the radiomics models assessing CD8

conclusionRadiomics models could allow for non-invasive assessment of TME cells from DECT images in ccRCC patients, promising to enhance our understanding and management of the tumor.

Indexed as

Antigens, CDAntigens, Differentiation, MyelomonocyticCarcinoma, Renal CellKidney NeoplasmsReceptors, Cell SurfaceTomography, X-Ray ComputedTumor MicroenvironmentAdultAgedCD163 AntigenFemaleHumansMachine LearningMaleMiddle AgedNomogramsAntigens, CDAntigens, Differentiation, MyelomonocyticCD163 AntigenReceptors, Cell SurfaceCancer-associated fibroblastsClear cell renal cell carcinomaRadiomicsT lymphocyteTumor-associated macrophagesTumor microenvironment

Identifiers

PMID39083142

What OpenQuestion holds

Textmetadata
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.