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
- PMID: 36264313
- DOI: 10.1007/s00330-022-09182-8
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
Abstract
Objectives: To investigate the effectiveness of CT-based radiomics nomograms in differentiating adrenal lipid-poor benign lesions and metastases in a cancer population.
Methods: This 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.
Results: Lesion 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.
Conclusion: Unenhanced 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.
Keywords: Adrenal glands; Neoplasm metastasis; Nomograms; Radiomics; Tomography.
© 2022. The Author(s), under exclusive licence to European Society of Radiology.
Similar articles
-
CT-based radiomics nomogram for differentiation of adrenal hyperplasia from lipid-poor adenoma: an exploratory study.BMC Med Imaging. 2023 Jan 7;23(1):4. doi: 10.1186/s12880-022-00951-x. BMC Med Imaging. 2023. PMID: 36611159 Free PMC article.
-
Radiomics approach based on biphasic CT images well differentiate "early stage" of adrenal metastases from lipid-poor adenomas: A STARD compliant article.Medicine (Baltimore). 2022 Sep 23;101(38):e30856. doi: 10.1097/MD.0000000000030856. Medicine (Baltimore). 2022. PMID: 36197274 Free PMC article.
-
Distinguishing Parotid Polymorphic Adenoma and Warthin Tumor Based on the CT Radiomics Nomogram: A Multicenter Study.Acad Radiol. 2023 Apr;30(4):717-726. doi: 10.1016/j.acra.2022.06.017. Epub 2022 Aug 8. Acad Radiol. 2023. PMID: 35953356 Clinical Trial.
-
Computed Tomography-Based Radiomics Model to Predict Central Cervical Lymph Node Metastases in Papillary Thyroid Carcinoma: A Multicenter Study.Front Endocrinol (Lausanne). 2021 Oct 21;12:741698. doi: 10.3389/fendo.2021.741698. eCollection 2021. Front Endocrinol (Lausanne). 2021. PMID: 34745008 Free PMC article.
-
Radiomic nomograms in CT diagnosis of gall bladder carcinoma: a narrative review.Discov Oncol. 2024 Dec 27;15(1):844. doi: 10.1007/s12672-024-01720-8. Discov Oncol. 2024. PMID: 39730762 Free PMC article. Review.
Cited by
-
Update of the guidelines on the management of adrenal incidentaloma from the adrenal group of the Spanish society of endocrinology and nutrition (SEEN).Endocrine. 2025 Sep 4. doi: 10.1007/s12020-025-04408-3. Online ahead of print. Endocrine. 2025. PMID: 40906030 Review. No abstract available.
-
Application of a Radiomics Machine Learning Model for Differentiating Aldosterone-Producing Adenoma from Non-Functioning Adrenal Adenoma.Bioengineering (Basel). 2023 Dec 14;10(12):1423. doi: 10.3390/bioengineering10121423. Bioengineering (Basel). 2023. PMID: 38136014 Free PMC article.
-
Differentiation of multiple adrenal adenoma subtypes based on a radiomics and clinico-radiological model: a dual-center study.BMC Med Imaging. 2025 Feb 10;25(1):45. doi: 10.1186/s12880-025-01556-w. BMC Med Imaging. 2025. PMID: 39930366 Free PMC article.
-
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 Med Imaging. 2025 Jul 1;25(1):219. doi: 10.1186/s12880-025-01798-8. BMC Med Imaging. 2025. PMID: 40597714 Free PMC article.
-
Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia.Front Med (Lausanne). 2024 May 20;11:1409477. doi: 10.3389/fmed.2024.1409477. eCollection 2024. Front Med (Lausanne). 2024. PMID: 38831994 Free PMC article.
References
MeSH terms
Substances
Grants and funding
LinkOut - more resources
Full Text Sources
Medical