ArticleEuropean radiology2023
Two nomograms based on radiomics models using triphasic CT for differentiation of adrenal lipid-poor benign lesions and metastases in a cancer population: an exploratory study.
Article in European radiology, 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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8 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Guideline
- Automatic segmentation-based radiomics and deep learning combined with clinical parameters for precise differentiation of lipid-poor adrenal adenomas and metastases.Abdominal radiology (New York) · 2026Article
- Survival outcomes and prognostic predictors after adrenalectomy for renal cell carcinoma adrenal metastasis.Discover oncology · 2025Article
- Evaluation of a fusion model combining deep learning models based on enhanced CT images with radiological and clinical features in distinguishing lipid-poor adrenal adenoma from metastatic lesions.BMC medical imaging · 2025Article
- Differentiation of multiple adrenal adenoma subtypes based on a radiomics and clinico-radiological model: a dual-center study.BMC medical imaging · 2025Article
- Non-invasively identifying candidates of active surveillance for prostate cancer using magnetic resonance imaging radiomics.Visual computing for industry, biomedicine, and art · 2024Article
- Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia.Frontiers in medicine · 2024Article
- Application of a Radiomics Machine Learning Model for Differentiating Aldosterone-Producing Adenoma from Non-Functioning Adrenal Adenoma.Bioengineering (Basel, Switzerland) · 2023Article
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7 authors at 4 institutions in 1 country.
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Abstract
objectivesTo investigate the effectiveness of CT-based radiomics nomograms in differentiating adrenal lipid-poor benign lesions and metastases in a cancer population.
methodsThis retrospective study enrolled 178 patients with cancer history from three medical centres categorised as those with adrenal lipid-poor benign lesions or metastases. Patients were divided into training, validation, and external testing cohorts. Radiomics features were extracted from triphasic CT images (unenhanced, arterial, and venous) to establish three single-phase models and one triphasic radiomics model using logistic regression. Unenhanced and triphasic nomograms were established by incorporating significant clinico-radiological factors and radscores. The models were evaluated by the receiver operating characteristic curve, Delong's test, calibration curve, and decision curve.
resultsLesion side, diameter, and enhancement ratio resulted as independent factors and were selected into nomograms. The areas under the curves (AUCs) of unenhanced and triphasic radiomics models in validation (0.878, 0.914, p = 0.381) and external testing cohorts (0.900, 0.893, p = 0.882) were similar and higher than arterial and venous models (validation: 0.842, 0.765; testing: 0.814, 0.806). Unenhanced and triphasic nomograms yielded similar AUCs in validation (0.903, 0.906, p = 0.955) and testing cohorts (0.928, 0.946, p = 0.528). The calibration curves showed good agreement and decision curves indicated satisfactory clinical benefits.
conclusionUnenhanced and triphasic CT-based radiomics nomograms resulted as a useful tool to differentiate adrenal lipid-poor benign lesions from metastases in a cancer population. They exhibited similar predictive efficacies, indicating that enhanced examinations could be avoided in special populations. KEY POINTS: • All four radiomics models and two nomograms using triphasic CT images exhibited favourable performances in three cohorts to characterise the cancer population's adrenal benign lesions and metastases. • Unenhanced and triphasic radiomics models had similar predictive performances, outperforming arterial and venous models. • Unenhanced and triphasic nomograms also exhibited similar efficacies and good clinical benefits, indicating that contrast-enhanced examinations could be avoided when identifying adrenal benign lesions and metastases.
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