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Cited by 34Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification TechniquesAI in cancer detection

Distinguishing metastases from benign adrenal masses: what can CT texture analysis do?

Shi, Bing, Zhang, Gu-Mu-Yang, Xu, Min, Jin, Zheng-Yu, Sun, Hao·2019·Acta Radiologica
DOI 10.1177/0284185119830292
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Abstract

Background Computed tomography texture analysis (CTTA) has gained an increasing role in oncology and has successfully demonstrated to reflect biological associations with glucose metabolism, hypoxia, angiogenesis, and even genetic variation. Purpose To determine whether quantitative CTTA can be used to differentiate metastases from benign adrenal masses on single energy CT images. Material and Methods A total of 225 patients with 265 histologically confirmed adrenal masses (101 metastases, 98 pheochromocytomas, and 66 lipid-poor adenomas) were included in this retrospective study. CTTA was performed and six texture parameters (including mean, SD of pixel distribution histogram, mean of positive pixels, entropy, kurtosis, skewness) across six spatial scaling factor (SSF) were recorded on both unenhanced and contrast-enhanced CT images. Receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was calculated using the significant texture parameters for the objective. Diagnostic performance was evaluated using the cut-off values of texture parameters by ROC analysis. The optimal discriminative texture parameters were used to produce support vector machine (SVM) classifiers. Diagnostic accuracy and 10-fold cross-validation was performed. Results Compared to benign adrenal masses, metastases had significantly lower mean gray-level intensity, SD, entropy, mean of positive pixels and kurtosis on unenhanced images ( P < 0.0083). On contrast-enhanced CT images, except for skewness and kurtosis, the other four texture texture-quantifiers were lower in the metastatic compared to the other group ( P < 0.0083). A model incorporating mean, SD, entropy and mean value of positive pixels produced an AUC of 0.85 ± 0.03 with an SVM accuracy of 77% to identify metastatic from benign adrenal masses. Conclusion Differentiation of metastatic and benign adrenal masses can be achieved accurately with CTTA on contrast-enhanced CT images.

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Crossref · 10.1177/0284185119830292

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  • In a larger patient cohort (n = 225), CT textural feature analysis had an accuracy of 77% for distinguishing metastases from benign adrenal lesions.

    “In a recent larger patient cohort (n = 225) the same Chinese group has reported an accuracy of 77% for distinguishing metastases from benign adrenal lesions using this CT textural feature analysis technique (379).”

    From Adrenal incidentaloma (2020) · cites this paper as [379]

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Authors
Shi, B., Zhang, G.-M.-Y., Xu, M., Jin, Z.-Y., & Sun, H.
Affiliations
  • Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
  • Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, PR China
  • CT Scientific Collaboration, Siemens Healthcare Limited, Shanghai, PR China
Corresponding author
Sun, Hao
ORCID
Venue
Acta Radiologica
Publisher
SAGE Publications
Published
2019-02-24
Volume
60
Issue
11
Pages
1553-1561
ISSN
  • 0284-1851 (ISSN-L)
  • 1600-0455
DOI
10.1177/0284185119830292
OpenAlex
W2918070434
Cited by
34
References
25
Citations by year
2026: 2 · 2025: 4 · 2024: 2 · 2023: 7 · 2022: 8 · 2021: 6 · 2020: 3 · 2019: 2
FWCI
3.14
Type
journal article
Language
en
Open access
closed
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
  • Acta Radiologica (publishedVersion)
  • PubMed (publishedVersion)
Funders
Health Industry Special Scientific Research Project of China (201402019)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords
adrenal metastasis, CT texture analysis, pheochromocytoma, adrenal adenoma, contrast-enhanced CT, texture features, support vector machine classification, differential diagnosis
MeSH terms
Adolescent, Adrenal Gland Neoplasms, Adrenal Glands, Adult, Aged, Aged, 80 and over, Diagnosis, Differential, Female, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Tomography, X-Ray Computed, Reproducibility of Results, Young Adult

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