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Zhu H, Wu M, Feng B, Zhang H, Hu C, Zhang T, Han Z. 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. BMC Med Imaging 2024; 24:210. [PMID: 39134939 PMCID: PMC11318272 DOI: 10.1186/s12880-024-01392-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 08/05/2024] [Indexed: 08/15/2024] Open
Abstract
OBJECTIVE The early differentiation of adrenal lipid-poor adenomas from non-adenomas is a crucial step in reducing excessive examinations and treatments. This study seeks to construct an eXtreme Gradient Boosting (XGBoost) predictive model utilizing the minimum attenuation values (minAVs) from non-contrast CT (NCCT) scans to identify lipid-poor adenomas. MATERIALS AND METHODS Retrospective analysis encompassed clinical data, minAVs, CT histogram (CTh), mean attenuation values (meanAVs), and lesion diameter from patients with pathologically or clinically confirmed adrenal lipid-poor adenomas across two medical institutions, juxtaposed with non-adenomas. Variable selection transpired in Institution A (training set), with XGBoost models established based on minAVs and CTh separately. Institution B (validation set) corroborated the diagnostic efficacy of the two models. Receiver operator characteristic (ROC) curve analysis, calibration curves, and Brier scores assessed the diagnostic performance and calibration of the models, with the Delong test gauging differences in the area under the curve (AUC) between models. SHapley Additive exPlanations (SHAP) values elucidated and visualized the models. RESULTS The training set comprised 136 adrenal lipid-poor adenomas and 126 non-adenomas, while the validation set included 46 and 40 instances, respectively. In the training set, there were substantial inter-group differences in minAVs, CTh, meanAVs, diameter, and body mass index (BMI) (p < 0.05 for all). The AUC for the minAV and CTh models were 0.912 (95% confidence interval [CI]: 0.866-0.957) and 0.916 (95% CI: 0.873-0.958), respectively. Both models exhibited good calibration, with Brier scores of 0.141 and 0.136. In the validation set, the AUCs were 0.871 (95% CI: 0.792-0.951) and 0.878 (95% CI: 0.794-0.962), with Brier scores of 0.156 and 0.165, respectively. The Delong test revealed no statistically significant differences in AUC between the models (p > 0.05 for both). SHAP value analysis for the minAV model suggested that minAVs had the highest absolute weight (AW) and negative contribution. CONCLUSION The XGBoost predictive model based on minAVs demonstrates effective discrimination between adrenal lipid-poor adenomas and non-adenomas. The minAV variable is easily obtainable, and its diagnostic performance is comparable to that of the CTh model. This provides a basis for patient diagnosis and treatment plan selection.
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Affiliation(s)
- Hanlin Zhu
- Department of Radiology, Hangzhou Ninth People's Hospital (Hangzhou Red Cross Hospital Qiantang Campus), Hangzhou, 310012, China
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, 310006, China
| | - Mengwei Wu
- Department of Radiology, The Quzhou Affiliated Hospital of Wenzhou Medical University, (Quzhou People's Hospital), Quzhou, 324003, China
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, 310006, China
| | - Bo Feng
- Department of Radiology, Hangzhou Ninth People's Hospital (Hangzhou Red Cross Hospital Qiantang Campus), Hangzhou, 310012, China
| | - Haifeng Zhang
- Department of Radiology, Hangzhou Ninth People's Hospital (Hangzhou Red Cross Hospital Qiantang Campus), Hangzhou, 310012, China
| | - Chunfeng Hu
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, 310006, China
| | - Tong Zhang
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, 310006, China
| | - Zhijiang Han
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, 310006, China.
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Barat M, Cottereau AS, Gaujoux S, Tenenbaum F, Sibony M, Bertherat J, Libé R, Gaillard M, Jouinot A, Assié G, Hoeffel C, Soyer P, Dohan A. Adrenal Mass Characterization in the Era of Quantitative Imaging: State of the Art. Cancers (Basel) 2022; 14:cancers14030569. [PMID: 35158836 PMCID: PMC8833697 DOI: 10.3390/cancers14030569] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/14/2022] [Accepted: 01/18/2022] [Indexed: 12/30/2022] Open
Abstract
Simple Summary Non-invasive characterization of adrenal lesions requires a rigorous approach. Although CT is the cornerstone of adrenal lesion characterization, a multimodality multiparametric imaging approach helps improve confidence in adrenal lesion characterization. Abstract Detection and characterization of adrenal lesions have evolved during the past two decades. Although the role of imaging in adrenal lesions associated with hormonal secretion is usually straightforward, characterization of non-functioning adrenal lesions may be challenging to confidently identify those that need to be resected. Although many adrenal lesions can be readily diagnosed when they display typical imaging features, the diagnosis may be challenging for atypical lesions. Computed tomography (CT) remains the cornerstone of adrenal imaging, but other morphological or functional modalities can be used in combination to reach a diagnosis and avoid useless biopsy or surgery. Early- and delayed-phase contrast-enhanced CT images are essential for diagnosing lipid-poor adenoma. Ongoing studies are evaluating the capabilities of dual-energy CT to provide valid virtual non-contrast attenuation and iodine density measurements from contrast-enhanced examinations. Adrenal lesions with attenuation values between 10 and 30 Hounsfield units (HU) on unenhanced CT can be characterized by MRI when iodinated contrast material injection cannot be performed. 18F-FDG PET/CT helps differentiate between atypical benign and malignant adrenal lesions, with the adrenal-to-liver maximum standardized uptake value ratio being the most discriminative variable. Recent studies evaluating the capabilities of radiomics and artificial intelligence have shown encouraging results.
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Affiliation(s)
- Maxime Barat
- Department of Radiology, Cochin Teaching Hospital, AP-HP, Université de Paris, 75014 Paris, France; (M.B.); (P.S.)
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
| | - Anne-Ségolène Cottereau
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Nuclear Medicine, Cochin Hospital, AP-HP, 75014 Paris, France;
| | - Sébastien Gaujoux
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Pancreatic and Endocrine Surgery, Pitié-Salpetrière Hospital, AP-HP, 75013 Paris, France
| | - Florence Tenenbaum
- Department of Nuclear Medicine, Cochin Hospital, AP-HP, 75014 Paris, France;
| | - Mathilde Sibony
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Pathology, Cochin Hospital, AP-HP, 75014 Paris, France
| | - Jérôme Bertherat
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Endocrinology, Cochin Hospital, AP-HP, 75014 Paris, France
| | - Rossella Libé
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Endocrinology, Cochin Hospital, AP-HP, 75014 Paris, France
| | - Martin Gaillard
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Digestive, Hepatobiliary and Endocrine Surgery, Cochin Hospital, AP-HP, 75014 Paris, France
| | - Anne Jouinot
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Endocrinology, Cochin Hospital, AP-HP, 75014 Paris, France
| | - Guillaume Assié
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Endocrinology, Cochin Hospital, AP-HP, 75014 Paris, France
| | | | - Philippe Soyer
- Department of Radiology, Cochin Teaching Hospital, AP-HP, Université de Paris, 75014 Paris, France; (M.B.); (P.S.)
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
| | - Anthony Dohan
- Department of Radiology, Cochin Teaching Hospital, AP-HP, Université de Paris, 75014 Paris, France; (M.B.); (P.S.)
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Correspondence:
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