Evidence map›Paper›PMID 39762736›Full record

ArticleBMC medical imaging2025

Application value of CT three-dimensional reconstruction technology in the identification of benign and malignant lung nodules and the characteristics of nodule distribution.

Guanghai Ji, Fei Liu, Zhiqiang Chen, Jie Peng, Hao Deng, Sheng Xiao, Yun Li

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Article in BMC medical imaging, 2025. 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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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Guanghai Ji *Department of Radiology, The First Affiliated Hospital of Yangtze University, No. 40 Jinlong Road, Shashi District, Jingzhou, Hubei, 434000, China.
Fei Liu *Department of Radiology, The First Affiliated Hospital of Yangtze University, No. 40 Jinlong Road, Shashi District, Jingzhou, Hubei, 434000, China.
Zhiqiang ChenDepartment of Radiology, The First Hospital Affiliated of Hainan Medical University, Haikou, Hainan, 570102, China.
Jie PengDepartment of Radiology, The First Affiliated Hospital of Yangtze University, No. 40 Jinlong Road, Shashi District, Jingzhou, Hubei, 434000, China.
Hao DengDepartment of Urology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, 434000, China.
Sheng XiaoDepartment of Radiology, The First Affiliated Hospital of Yangtze University, No. 40 Jinlong Road, Shashi District, Jingzhou, Hubei, 434000, China. Xiaosheng8188xs@163.com.
Yun LiDepartment of Radiology, The First Affiliated Hospital of Yangtze University, No. 40 Jinlong Road, Shashi District, Jingzhou, Hubei, 434000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe study aimed to evaluate the application value of computed tomography (CT) three-dimensional (3D) reconstruction technology in identifying benign and malignant lung nodules and characterizing the distribution of the nodules.

methodsCT 3D reconstruction was performed for lung nodules. Pathological results were used as the gold standard to compare the detection rates of various lung nodule signs between conventional chest CT scanning and CT 3D reconstruction techniques. Additionally, the differences in mean diffusion coefficient values and partial anisotropy index values between male and female patients were analyzed.

resultsPathologic confirmation identified 30 patients with benign lesions and 45 patients with malignant lesions. CT 3D reconstruction demonstrated higher diagnostic accuracy for lung nodule imaging signs compared to conventional CT scanning (P < 0.05). The mean diffusion coefficient values and partial anisotropy index values were lower in female patients compared to male patients in the lung nodule lesion area, lung perinodular edema area, and normal lung tissue (P < 0.05). Conventional CT scanning showed a benign accuracy rate of 63.33% and a malignant accuracy rate of 60.00%, whereas CT 3D imaging achieved a benign and malignant accuracy rate of 86.67% for both. The accuracy rates for CT 3D imaging were significantly higher than those for conventional CT scanning (P < 0.05).

conclusionCT 3D imaging technology demonstrates high diagnostic accuracy in differentiating benign from malignant lung nodules.

Indexed as

Imaging, Three-DimensionalLung NeoplasmsSolitary Pulmonary NoduleTomography, X-Ray ComputedAdultAgedFemaleHumansMaleMiddle AgedRadiographic Image Interpretation, Computer-AssistedComputed tomographyDiagnostic imagingLung nodulesLung tumorsThoracic tumors

Identifiers

PMID39762736
PMCPMC11702159

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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.