NDLI: Differentiation of multiple adrenal adenoma subtypes based on a radiomics and clinico-radiological model: a dual-center study

Content Provider	Springer Nature : BioMed Central
Author	Zhang, Xinzhang Si, Yapeng Shi, Xin Zhang, Yiwen Yang, Liuyang Yang, Junfeng Zhang, Ye Leng, Jinjun Hu, Pingping Liu, Hao Chen, Jiaqi Li, Wenliang Song, Wei Zhu, Jianping Yang, Maolin Li, Wei Wang, Junfeng
Abstract	Background The prevalence and detection rates of adrenal incidentalomas have been on the rise globally, with more than 90% of these lesions pathologically classified as adrenocortical adenomas. Among these, approximately 30% of patients present with hormone-secreting adenomas, leading to the deterioration of their health, with some requiring surgical resection. The available methods for adrenal function evaluation are invasive and costly. Moreover, their accuracy is influenced by numerous factors. Therefore, it is imperative to develop non-invasive and simplified preoperative diagnostic approach. Methods A retrospective study was performed on 169 patients from two tertiary medical centers. Subsequently, radiomics features were extracted after tumor margins were delineated layer-by-layer using a semi-automatic contouring approach. Feature selection was achieved in two cycles, with the first round utilizing a support vector machine (SVM) and the second round using a LASSO-based recursive feature elimination algorithm. Finally, logistic regression models were constructed using the clinico-radiological, radiomics, and a combination of both. Results After a comprehensive evaluation of the predictive indicators, the logistic regression classifier model based on the combined clinico-radiological and radiomic features had an AUC of (0.945, 0.927, 0.856) for aldosterone-producing adenoma (APA), (0.963, 0.889, 0.887) for cortisol-producing adenoma (CPA), and (0.940, 0.765, 0.816) for non-functioning adrenal adenoma (NAA) in the training set, validation set, and external test set, respectively. This model exhibited superior predictive performance in differentiating between the three adrenal adenoma subtypes. Conclusions A logistic regression model was constructed using radiomics and clinico-radiological features derived from multi-phase enhanced CT images and conducted external validation. The combined model showed good overall performance, highlighting the feasibility of applying the model for preoperative differentiation and prediction of various types of ACA.
Related Links	https://bmcmedimaging.biomedcentral.com/counter/pdf/10.1186/s12880-025-01556-w.pdf
Ending Page	15
Page Count	15
Starting Page	1
File Format	HTM / HTML
ISSN	14712342
DOI	10.1186/s12880-025-01556-w
Journal	BMC Medical Imaging
Issue Number	1
Volume Number	25
Language	English
Publisher	BioMed Central
Publisher Date	2025-02-10
Access Restriction	Open
Subject Keyword	Imaging Radiology Adrenocortical adenomas Radiomics Computed tomography Prediction model
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	2.8/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

CT-based radiomics nomogram for differentiation of adrenal hyperplasia from lipid-poor adenoma: an exploratory study

Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics

Development and validation of a model predicting adrenal lipid-poor adenoma based on the minimum attenuation value from non-contrast CT: a dual-center retrospective study

A Preliminary Study for Distinguish Hormone-Secreting Functional Adrenocortical Adenoma Subtypes Using Multiparametric CT Radiomics-Based Machine Learning Model and Nomogram

Prediction of Adrenal Adenomas With Hypercortisolism by Using Adrenal Computed Tomography: Emphasis on Contralateral Adrenal Thinning.

Can Radiomics Provide Additional Diagnostic Value for Identifying Adrenal Lipid-Poor Adenomas From Non-Adenomas on Unenhanced CT?

Differentiation of adrenal adenomas from nonadenomas using CT histogram analysis method: a prospective study.

Computed tomography enterography-based radiomics nomograms to predict inflammatory activity for ileocolonic Crohn’s disease: a preliminary single-center retrospective study

Prediction of acute pancreatitis severity based on early CT radiomics

Differentiation of multiple adrenal adenoma subtypes based on a radiomics and clinico-radiological model: a dual-center study

Similar Documents

CT-based radiomics nomogram for differentiation of adrenal hyperplasia from lipid-poor adenoma: an exploratory study

Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics

Development and validation of a model predicting adrenal lipid-poor adenoma based on the minimum attenuation value from non-contrast CT: a dual-center retrospective study

A Preliminary Study for Distinguish Hormone-Secreting Functional Adrenocortical Adenoma Subtypes Using Multiparametric CT Radiomics-Based Machine Learning Model and Nomogram

Prediction of Adrenal Adenomas With Hypercortisolism by Using Adrenal Computed Tomography: Emphasis on Contralateral Adrenal Thinning.

Can Radiomics Provide Additional Diagnostic Value for Identifying Adrenal Lipid-Poor Adenomas From Non-Adenomas on Unenhanced CT?

Differentiation of adrenal adenomas from nonadenomas using CT histogram analysis method: a prospective study.

Computed tomography enterography-based radiomics nomograms to predict inflammatory activity for ileocolonic Crohn’s disease: a preliminary single-center retrospective study

Prediction of acute pancreatitis severity based on early CT radiomics

Differentiation of multiple adrenal adenoma subtypes based on a radiomics and clinico-radiological model: a dual-center study