ArticleFrontiers in endocrinology2022
Computed Tomography-Based Machine Learning Differentiates Adrenal Pheochromocytoma From Lipid-Poor Adenoma.
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 20 papers.
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Who cites it
20 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
- Precision imaging and evolving therapies in paragangliomas and pheochromocytomas: from molecular diagnostics to imaging-guided management.Insights into 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
- Endorsement of artificial intelligence guidelines across leading endocrinology journals: a cross-sectional analysis.Frontiers in endocrinology · 2026Article
- A novel approach for classifying patients with adrenal tumors based on decision support systems and artificial intelligence.Hormones (Athens, Greece) · 2025Article
- Combined nomogram for differentiating adrenal pheochromocytoma from large-diameter lipid-poor adenoma using multiphase CT radiomics and clinico-radiological features.BMC medical imaging · 2025Article
- Review
- Clinical‑imaging‑radiomic nomogram based on unenhanced CT effectively predicts adrenal metastases in patients with lung cancer with small hyperattenuating adrenal incidentalomas.Oncology letters · 2024Article
- Patient classification and attribute assessment based on machine learning techniques in the qualification process for surgical treatment of adrenal tumours.Scientific reports · 2024Article
- Artificial intelligence in endocrinology: a comprehensive review.Journal of endocrinological investigation · 2024Review
- Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics.BMC medical imaging · 2023Article
- Article
- Artificial intelligence in diabetes mellitus and endocrine diseases - what can we expect?Nature reviews. Endocrinology · 2023Article
- Development and validation of machine-learning models for the difficulty of retroperitoneal laparoscopic adrenalectomy based on radiomics.Frontiers in endocrinology · 2023Article
- Application of radiomics in adrenal incidentaloma: a literature review.Discover oncology · 2022Review
- Dual-energy spectral detector computed tomography differential diagnosis of adrenal adenoma and pheochromocytoma: Changes in the energy level curve, a phenomenon caused by lipid components?Frontiers in endocrinology · 2022Article
- A nomogram for evaluation and analysis of difficulty in retroperitoneal laparoscopic adrenalectomy: A single-center study with prospective validation using LASSO-logistic regression.Frontiers in endocrinology · 2022Article
- A longitudinal study of dual-energy X-ray absorptiometry status in menopausal women with adrenal tumors.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
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Abstract
Objectives: To assess the accuracy of computed tomography (CT)-based machine learning models for differentiating subclinical pheochromocytoma (sPHEO) from lipid-poor adenoma (LPA) in patients with adrenal incidentalomas. Patients and Methods: The study included 188 tumors in the 183 patients with LPA and 92 tumors in 86 patients with sPHEO. Pre-enhanced CT imaging features of the tumors were evaluated. Machine learning prediction models and scoring systems for differentiating sPHEO from LPA were built using logistic regression (LR), support vector machine (SVM) and random forest (RF) approaches. Results: The LR model performed better than other models. The LR model (M1) including three CT features: CT Conclusions: Our results indicated the potential of using a non-invasive imaging method such as CT-based machine learning models and scoring systems for predicting histology of adrenal incidentalomas. This approach may assist the diagnosis and personalized care of patients with adrenal tumors.
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