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

Yineng Zheng,Xin Liu,Haitao Yang,Yi Zhong,Fajin Lv

doi:10.3389/fonc.2020.570502

Abstract

Purpose: To explore the application value of multiparametric computed tomography (CT) radiomics in non-invasive differentiation between aldosterone-producing and cortisol-producing functional adrenocortical adenomas.Methods: This retrospective review analyzed 83 patients including 41 patients with aldosterone-producing adenoma and 42 patients with cortisol-producing adenoma. The quantitative radiomics features were extracted from the complete unenhanced, arterial, and venous phase CT images. A comparative study of several frequently used machine learning models (linear discriminant analysis, logistic regression, random forest, and support vector machine) combined with different feature selection methods was implemented in order to determine which was most advantageous for differential diagnosis using radiomics features. Then, the integrated model using the combination of radiomic signature and clinic–radiological features was built, and the associated calibration curve was also presented. The diagnostic performance of these models was estimated and compared using the area under the receiver operating characteristic (ROC) curve (AUC).Result: In the radiomics-based machine learning model, logistic regression model with LASSO (least absolute shrinkage and selection operator) outperformed the other models, which yielded a sensitivity of 0.935, a specificity of 0.823, and an accuracy of 0.887 [AUC = 0.882, 95% confidence interval (CI) = 0.819–0.945]. Moreover, the nomogram representing the integrated model achieved good discrimination performances, which yielded a sensitivity of 0.915, a specificity of 0.928, and an accuracy of 0.922 (AUC = 0.902, 95% CI = 0.822–0.982), and it was better than that of the radiomics model alone.Conclusion: This study found that the combination of multiparametric radiomics signature and clinic–radiological features can non-invasively differentiate the subtypes of hormone-secreting functional adrenocortical adenomas, which may have good potential for facilitating the diagnosis and treatment in clinical practice.

Highlights

Adrenocortical adenomas (ACAs) are the most common benign adrenal cortical tumors representing 50–80% of all adrenal tumors [1] that may be functional or nonfunctional depend on whether producing hormones
The diagnosis of aldosteroneproducing adenoma (APA) and cortisol-producing adenoma (CPA) was established by these criteria: (i) common clinical characteristics and laboratory findings including an elevated aldosterone/renin ratio together with positive confirmatory tests in APA and an elevated serum cortisol, failure to suppress cortisol with dexamethasone, and normal aldosterone levels in CPA, respectively; (ii) presence of an adrenal mass confirmed via CT before surgery; (iii) a confirmed pathological diagnosis of the adrenal mass as an adrenal adenoma after surgery; and (iv) a postoperative cure or considerable improvement
In the APA group, the tumor showed smaller size and lower mean CT attenuation compared to CPA group, while the ipsilateral or contralateral adrenocortical atrophy was more commonly seen in CPA group (Table 2)

Summary

Introduction

Adrenocortical adenomas (ACAs) are the most common benign adrenal cortical tumors representing 50–80% of all adrenal tumors [1] that may be functional (hormone-secreting) or nonfunctional depend on whether producing hormones. Two major subtypes are aldosteroneproducing adenoma (APA) and cortisol-producing adenoma (CPA), leading to respective complications including primary aldosteronism (Conn syndrome) and hypercortisolism (Cushing syndrome), and each requires different treatment strategies including surgery or medications. The diagnosis of the functional ACAs is dependent on the clinical manifestations, laboratory tests, imaging, and pathologic examinations forming the basis to conclude. About 10–20% of ACAs are bilateral or multiple [7, 8] In such condition, it is very important, and quite difficult, to distinguish the responsible foci to avoid unnecessary excision or overresection for performing precision treatment

Objectives

Methods

Results

Discussion

Conclusion