Evidence map›Paper›PMID 42558213›Full record

ArticleFrontiers in oncology2026

Evaluation of a dual-energy computed tomography parameter -radiomics combined model for solitary pulmonary nodules.

Xiaojing Kan, Bin Nan, Ruidi Ni, Congyan Wang, Yukun Pan, Doudou Liu, Yinghui Ge

Abstract read
In one paragraph

Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xiaojing Kan *Department of Radiology, Key Laboratory of Cardiac Imaging Medicine in Henan Province, Central China Subcenter of National Center for Cardiovascular Diseases, Henan Cardiovascular Disease Center, Fuwai Central-China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, China.
Bin Nan *Department of Radiology, Key Laboratory of Cardiac Imaging Medicine in Henan Province, Central China Subcenter of National Center for Cardiovascular Diseases, Henan Cardiovascular Disease Center, Fuwai Central-China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, China.
Ruidi NiDepartment of Radiology, Key Laboratory of Cardiac Imaging Medicine in Henan Province, Central China Subcenter of National Center for Cardiovascular Diseases, Henan Cardiovascular Disease Center, Fuwai Central-China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, China.
Congyan WangDepartment of Radiology, Key Laboratory of Cardiac Imaging Medicine in Henan Province, Central China Subcenter of National Center for Cardiovascular Diseases, Henan Cardiovascular Disease Center, Fuwai Central-China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, China.
Yukun PanDepartment of Radiology, Key Laboratory of Cardiac Imaging Medicine in Henan Province, Central China Subcenter of National Center for Cardiovascular Diseases, Henan Cardiovascular Disease Center, Fuwai Central-China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, China.
Doudou LiuDepartment of Radiology, Key Laboratory of Cardiac Imaging Medicine in Henan Province, Central China Subcenter of National Center for Cardiovascular Diseases, Henan Cardiovascular Disease Center, Fuwai Central-China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, China.
Yinghui GeDepartment of Radiology, Key Laboratory of Cardiac Imaging Medicine in Henan Province, Central China Subcenter of National Center for Cardiovascular Diseases, Henan Cardiovascular Disease Center, Fuwai Central-China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To construct a dual-energy computed tomography (DECT) parameter-radiomics combined model based on DECT quantitative parameters and radiomic features and to evaluate its diagnostic performance in differentiating benign from malignant solitary pulmonary nodules. Materials and methods: We retrospectively collected data from inpatients who underwent contrast-enhanced thoracic DECT scans at our hospital between June 2018 and November 2024. Quantitative parameters measured and calculated included iodine concentration, effective atomic number, normalized iodine concentration, normalized effective atomic number, spectral Hounsfield unit slope, and extracellular volume fraction. These DECT parameters were used to construct a predictive model. The dataset was randomly divided into training and test sets in a 7:3 ratio. Radiomic features were extracted from mixed-energy images of the arterial and venous phases (A Results: In total, 209 patients were enrolled (166 in the malignant group and 43 in the benign group). In the test set, the diagnostic performance of the combined model (AUC = 0.906) exceeded that of the DECT-only radiomics model (AUC = 0.794). The combined model demonstrated an accuracy of 82.5%, sensitivity of 80.0%, and specificity of 92.3%. Conclusion: The DECT parameter-radiomics combined model effectively differentiated benign from malignant solitary pulmonary nodules and shows promising potential for clinical translation.

Indexed as

benign-malignant differentiationdual-energy CTmachine learningpulmonary noduleradiomics

Identifiers

PMID42558213
PMCPMC13437286

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