Adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced CT can effectively differentiate sPHEO from lipid-poor adrenal adenoma
Open accessAbstract
Objective: To evaluate the feasibility and accuracy of machine learning based texture analysis of unenhanced CT images in differentiating subclinical pheochromocytoma (sPHEO) from lipid-poor adenoma (LPA) in adrenal incidentaloma (AI).Methods: Seventy-nine patients with 80 LPA and 29 patients with 30 sPHEO were included in the study.Texture parameters were derived using imaging software (MaZda).Thirty texture features were selected and LPA was performed for the features selected.The number of positive features was used to predict results.Logistic multiple regression analysis was performed on the 30 texture features, and a predictive equation was created based on the coefficients obtained.Results: LPA yielded a misclassification rate of 19.39% in differentiating sPHEO from LPA.Our predictive model had an accuracy rate of 94.4% (102/108), with a sensitivity of 86.2% (25/29) and a specificity of 97.5% (77/79) for differentiation.When the number of positive features was greater than 8, the accuracy of prediction was 85.2% (92/108), with a sensitivity of 96.6% (28/29) and a specificity of 81% (64/79).Conclusions: Machine learning-based quantitative texture analysis of unenhanced CT may be a reliable quantitative method in differentiating sPHEO from LPA when AI is present.
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A feasibility study assessing machine learning-based quantitative textural analysis of unenhanced CT in 108 patients with indeterminate adrenal lesions had 94% accuracy for differentiating pheochromocytoma from lipid-poor adenoma.
“A feasibility study assessing machine learning-based quantitative textural analysis of unenhanced CT in 108 patients with indeterminate adrenal lesions had a 94% accuracy for differentiating pheochromocytoma from lipid-poor adenoma (384).”
From Adrenal incidentaloma (2020) · cites this paper as [384]
At present, there is insufficient evidence to support clinical translation of textural analysis and machine learning techniques for adrenal lesions.
“At present, there is insufficient evidence to support clinical translation, but these techniques have the potential to improve diagnostic accuracy and more informed decision making in the future.”
From Adrenal incidentaloma (2020) · cites this paper as [384]
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References 29 · Cited by 87
- Authors
- Yi, X., Guan, X., Chen, C., Zhang, Y., Zhang, Z., Li, M., Liu, P., Yu, A., Long, X., Liu, L., Chen, B. T., & Zee, C.
- Affiliations
- Central South University (CN)
- Changsha Medical University (CN)
- City Of Hope National Medical Center (US)
- Corresponding author
- Long, Xueying
- Liu, Longfei
- ORCID
- Venue
- Journal of Cancer
- Publisher
- Ivyspring International Publisher
- Published
- 2018
- Volume
- 9
- Issue
- 19
- Pages
- 3577-3582
- ISSN
- 1837-9664 (ISSN-L)
- PMC
- PMC6171020
- OpenAlex
- W2891623084
- Cited by
- 87
- References
- 29
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 25 · 2023: 15 · 2022: 14 · 2021: 9 · 2020: 8 · 2019: 4
- FWCI
- 3.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- Journal of Cancer (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (81400773)
- Central South University
- China Postdoctoral Science Foundation (2018M632997, xywm2015I35)
- Peking University (xywm2015I35, 2018M632997)
- Postdoctoral Science Foundation of Central South University (185705)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Surgery › Adrenal and Paraganglionic Tumors
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- adrenal incidentaloma, quantitative texture analysis, unenhanced CT, machine learning, differential diagnosis, texture features, prediction model
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