血浆类固醇分析与机器学习相结合,用于鉴别诊断肾上腺偶发瘤患者无功能腺瘤引起的轻度皮质醇自主分泌。

IF 3.7 3区 医学 Q2 ENDOCRINOLOGY & METABOLISM
Danni Mu MD , Xia Qian BD , Yichen Ma BD , Xi Wang PhD , Yumeng Gao BD , Xiaoli Ma PhD , Shaowei Xie BD , Lian Hou MD , Qi Zhang MD , Fang Zhao BD , Liangyu Xia MD , Liling Lin PhD , Ling Qiu MD , Jie Wu PhD , Songlin Yu MD , Xinqi Cheng PhD
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引用次数: 0

摘要

背景:目的:评估血浆类固醇分析与机器学习(ML)相结合在肾上腺偶发瘤患者中区分轻度自主皮质醇分泌(MACS)和非功能性腺瘤(NFA)的诊断价值:筛选了实验室信息系统中2021年1月至2023年12月的血浆类固醇谱数据。应用极梯度提升(XGBoost)技术,利用血浆中的 24 种类固醇和/或受试者的临床特征建立诊断模型。结果显示,76 例 MACS 患者和 86 例 MACS 患者的血浆中均含有类固醇:76例MACS患者和86例NFA患者被纳入开发和内部验证队列,外部验证队列包括27例MACS和21例NFA病例。在评估的五个 ML 模型中,XGBoost 使用 24 种类固醇激素显示出卓越的性能,AUC 为 0.77。SHAP 方法确定了在区分 MACS 和 NFA 方面表现最佳的五种类固醇激素,即脱氢表雄酮(DHEA)、11-脱氧皮质醇、11β-羟基睾酮、睾酮和脱氢表雄酮硫酸盐(DHEAS)。将临床特征纳入模型后,AUC 增加到 0.88,灵敏度为 0.77,特异性为 0.82。此外,通过 SHAP 获得的结果显示,较低水平的睾酮、DHEA、LDL-c、体重指数和促肾上腺皮质激素以及较高水平的 11-脱氧皮质醇显著有助于在模型中识别澳门巴黎人娱乐官网:我们阐明了如何利用基于 ML 的类固醇分析来区分肾上腺偶发瘤患者中的 MACS 和 NFA。这种方法有望通过一次采血区分这两种实体。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Plasma Steroid Profiling Combined With Machine Learning for the Differential Diagnosis in Mild Autonomous Cortisol Secretion From Nonfunctioning Adenoma in Patients With Adrenal Incidentalomas

Objective

To assess the diagnostic value of combining plasma steroid profiling with machine learning (ML) in differentiating between mild autonomous cortisol secretion (MACS) and nonfunctioning adenoma (NFA) in patients with adrenal incidentalomas.

Methods

The plasma steroid profiles data in the laboratory information system were screened from January 2021 to December 2023. EXtreme Gradient Boosting was applied to establish diagnostic models using plasma 24-steroid panels and/or clinical characteristics of the subjects. The SHapley Additive exPlanation (SHAP) method was used for explaining the model.

Results

Seventy-six patients with MACS and 86 patients with NFA were included in the development and internal validation cohort while the external validation cohort consisted of 27 MACS and 21 NFA cases. Among 5 ML models evaluated, eXtreme Gradient Boosting demonstrated superior performance with an area under the curve of 0.77 using 24 steroid hormones. The SHAP method identified 5 steroids that exhibited optimal performance in distinguishing MACS from NFA, namely dehydroepiandrosterone, 11-deoxycortisol, 11β-hydroxytestosterone, testosterone, and dehydroepiandrosteronesulfate. Upon incorporating clinical features into the model, the area under the curve increased to 0.88, with a sensitivity of 0.77 and specificity of 0.82. Furthermore, the results obtained through SHAP revealed that lower levels of testosterone, dehydroepiandrosterone, low-density lipoprotein cholesterol, body mass index, and adrenocorticotropic hormone along with higher level of 11-deoxycortisol significantly contributed to the identification of MACS in the model.

Conclusions

We have elucidated the utilization of ML-based steroid profiling to discriminate between MACS and NFA in patients with adrenal incidentalomas. This approach holds promise for distinguishing these 2 entities through a single blood collection.

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来源期刊
Endocrine Practice
Endocrine Practice ENDOCRINOLOGY & METABOLISM-
CiteScore
7.60
自引率
2.40%
发文量
546
审稿时长
41 days
期刊介绍: Endocrine Practice (ISSN: 1530-891X), a peer-reviewed journal published twelve times a year, is the official journal of the American Association of Clinical Endocrinologists (AACE). The primary mission of Endocrine Practice is to enhance the health care of patients with endocrine diseases through continuing education of practicing endocrinologists.
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