Evaluation of a fusion model combining deep learning models based on enhanced CT images with radiological and clinical features in distinguishing lipid-poor adrenal adenoma from metastatic lesions

Machine learning models based on contrast-enhanced CT combined with clinical and imaging features exhibit outstanding diagnostic performance in differentiating lipid-poor adrenal adenomas from metastases.

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Publication Details

Journal
BMC Medical Imaging
Published
2025-07-01
DOI
https://doi.org/10.1186/s12880-025-01798-8
Citations
1
Primary Topic
Adrenal and Paraganglionic Tumors
Type
article
Field-Weighted Citation Impact
2.01
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article

Evaluation of a fusion model combining deep learning models based on enhanced CT images with radiological and clinical features in distinguishing lipid-poor adrenal adenoma from metastatic lesions

Long Jin, S. Wang, Yi-Ding Ji, Shengnan Yin et al.
1 citations
BMC Medical Imaging
Adrenal and Paraganglionic Tumors
2.01
article

Evaluation of a fusion model combining deep learning models based on enhanced CT images with radiological and clinical features in distinguishing lipid-poor adrenal adenoma from metastatic lesions

Long Jin, S. Wang, Yi-Ding Ji, Shengnan Yin, Ning Ding, Zi-You Wang
article en
1 citations

Abstract

Machine learning models based on contrast-enhanced CT combined with clinical and imaging features exhibit outstanding diagnostic performance in differentiating lipid-poor adrenal adenomas from metastases.

BMC Medical ImagingVol. 25(1)
Soochow University (CN)
No poverty
Openalex Percentile: Top 14%
Adrenal and Paraganglionic Tumors
2.01
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