Development and Validation of a Clinical-Image Model for Quantitatively Distinguishing Uncertain Lipid-Poor Adrenal Adenomas From Nonadenomas

Wenting Pan; Huangqi Zhang; Shengze Jin; Xin Li; Jiawen Yang; Binhao Zhang; Xue Dong; Ling Ma; Wenbin Ji

doi:10.3389/fonc.2022.902991

Development and Validation of a Clinical-Image Model for Quantitatively Distinguishing Uncertain Lipid-Poor Adrenal Adenomas From Nonadenomas

Front Oncol. 2022 Jul 13:12:902991. doi: 10.3389/fonc.2022.902991. eCollection 2022.

Authors

Wenting Pan¹, Huangqi Zhang¹, Shengze Jin², Xin Li¹, Jiawen Yang¹, Binhao Zhang¹, Xue Dong³, Ling Ma⁴, Wenbin Ji¹

Affiliations

¹ Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China.
² Department of Radiology, Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou, China.
³ Department of Radiology, Taizhou Hospital, Zhejiang University, Taizhou, China.
⁴ He Kang Corporate Management (Shanghai) Co.Ltd, Shanghai, China.

Abstract

Background: There remains a demand for a practical method of identifying lipid-poor adrenal lesions.

Purpose: To explore the predictive value of computed tomography (CT) features combined with demographic characteristics for lipid-poor adrenal adenomas and nonadenomas.

Materials and methods: We retrospectively recruited patients with lipid-poor adrenal lesions between January 2015 and August 2021 from two independent institutions as follows: Institution 1 for the training set and the internal validation set and Institution 2 for the external validation set. Two radiologists reviewed CT images for the three sets. We performed a least absolute shrinkage and selection operator (LASSO) algorithm to select variables; subsequently, multivariate analysis was used to develop a generalized linear model. The probability threshold of the model was set to 0.5 in the external validation set. We calculated the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) for the model and radiologists. The model was validated and tested in the internal validation and external validation sets; moreover, the accuracy between the model and both radiologists were compared using the McNemar test in the external validation set.

Results: In total, 253 patients (median age, 55 years [interquartile range, 47-64 years]; 135 men) with 121 lipid-poor adrenal adenomas and 132 nonadenomas were included in Institution 1, whereas another 55 patients were included in Institution 2. The multivariable analysis showed that age, male, lesion size, necrosis, unenhanced attenuation, and portal venous phase attenuation were independently associated with adrenal adenomas. The clinical-image model showed AUCs of 0.96 (95% confidence interval [CI]: 0.91, 0.98), 0.93 (95% CI: 0.84, 0.97), and 0.86 (95% CI: 0.74, 0.94) in the training set, internal validation set, and external validation set, respectively. In the external validation set, the model showed a significantly and non-significantly higher accuracy than reader 1 (84% vs. 65%, P = 0.031) and reader 2 (84% vs. 69%, P = 0.057), respectively.

Conclusions: Our clinical-image model displayed good utility in differentiating lipid-poor adrenal adenomas. Further, it showed better diagnostic ability than experienced radiologists in the external validation set.

Keywords: adrenal adenoma; clinic; computed tomography; distinguish; model.