ArticleFrontiers in endocrinology2022
Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan.
Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- 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
- A novel approach for classifying patients with adrenal tumors based on decision support systems and artificial intelligence.Hormones (Athens, Greece) · 2025Article
- Patient classification and attribute assessment based on machine learning techniques in the qualification process for surgical treatment of adrenal tumours.Scientific reports · 2024Article
- Treating Primary Aldosteronism-Induced Hypertension: Novel Approaches and Future Outlooks.Endocrine reviews · 2024Article
- Feasibility study of computed tomography texture analysis for evaluation of canine primary adrenal gland tumors.Frontiers in veterinary science · 2023Article
- Artificial intelligence and radiomics applications in adrenal lesions: a systematic review.Therapeutic advances in urologyReview
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
New radioimaging techniques, exploiting the quantitative variables of imaging, permit to identify an hypothetical pathological tissue. We have applied this potential in a series of 72 adrenal incidentalomas (AIs) followed at our center, subdivided in functioning and non-functioning using laboratory findings. Each AI was studied in the preliminary non-contrast phase with a specific software (Mazda), surrounding a region of interest within each lesion. A total of 314 features were extrapolated. Mean and standard deviations of features were obtained and the difference in means between the two groups was statistically analyzed. Receiver Operating Characteristic (ROC) curves were used to identify an optimal cutoff for each variable and a prediction model was constructed
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