ArticleDiagnostic and interventional radiology (Ankara, Turkey)2023
Machine learning analysis of adrenal lesions: preliminary study evaluating texture analysis in the differentiation of adrenal lesions
Article in Diagnostic and interventional radiology (Ankara, Turkey), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.
- Pooled it
- xsCT-based radiomics for adrenal incidentaloma characterization.La Radiologia medica · 2026Article
- The application of artificial intelligence in adrenal imaging: current state of knowledge, challenges, and future directions.Endocrine connections · 2026Article
- Radiomic Characterization of Adrenal Incidentalomas on NECT: Retrospective Exploratory Study and Systematic Review.Journal of imaging · 2026Article
- Machine Learning Approach for Differentiation of Pheochromocytoma from Adrenocortical Cancer and Non-Functioning Adrenal Adenomas.Life (Basel, Switzerland) · 2026Article
- An interpretable machine learning framework using multi-phase computed tomography for differentiation of adrenal lipid-poor adenomas and pheochromocytomas.Frontiers in artificial intelligence · 2026Article
- CT-based texture analysis for preoperative prediction of key histopathological features in laryngeal cancer.Polish journal of radiology · 2026Article
- Artificial intelligence and radiomics applications in adrenal lesions: a systematic review.Therapeutic advances in urologyReview
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aimed to determine the accuracy of texture analysis in differentiating adrenal lesions on unenhanced computed tomography (CT) images.
methodsIn this single-center retrospective study, 166 adrenal lesions in 140 patients (64 women, 76 men; mean age 56.58 ± 13.65 years) were evaluated between January 2015 and December 2019. The lesions consisted of 54 lipid-rich adrenal adenomas, 37 lipid-poor adrenal adenomas (LPAs), 56 adrenal metastases (ADM), and 19 adrenal pheochromocytomas (APhs). Each adrenal lesion was segmented by manually contouring the borders of the lesion on unenhanced CT images. A texture analysis of the CT images was performed using Local Image Feature Extraction software. First-order and second-order texture parameters were assessed, and 45 features were extracted from each lesion. One-Way analysis of variance with Bonferroni correction and the Mann-Whitney U test was performed to determine the relationships between the texture features and adrenal lesions. Receiver operating characteristic curves were performed for lesion discrimination based on the texture features. Logistic regression analysis was used to generate logistic models, including only the texture parameters with a high-class separation capacity (i.e., P < 0.050). SPSS software was used for all statistical analyses.
resultsFirst-order and second-order texture parameters were identified as significant factors capable of differentiating among the four lesion types (
conclusionTexture features may help in the characterization of adrenal lesions on unenhanced CT images.
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