四川大学华西医院 a. 放射科；b. 内分泌代谢科；c. 临床磁共振研究中心；d. 信息中心，四川 成都 610041
Prediction of Adrenal Venous Sampling Outcome in Patients with PA Using
Radiomics and Automated Machine Learning
XIE Weia, CHEN Taob, LUO Guotinga,c, WANG Hanxiaoa, SHU Yanga, LIU Juana, ZHENG Taod, SUN Huaiqianga,c
a. Department of Radiology; b. Department of Endocrinology and Metabolism; c. Huaxi MR Research Center; d. IT Center,
West China Hospital Sichuan University, Chengdu Sichuan 610041, China
Abstract：Objective To build a preoperative prediction model for the subtype classification of primary aldosteronism (PA) based
on enhanced high-resolution CT and automated machine learning techniques. Methods A retrospective study was conducted
on 312 patients with PA diagnosed by subtypes of adrenal venous sampling (AVS). Among them, 207 were diagnosed with
unilateral dominance (AVS right∶AVS left=93∶114), and 105 were diagnosed with bilateral dominance. Initial CT images were
retrospectively included and radiomics features were extracted from bilateral adrenal based on thin layer venous phase images. The
quotient radiomics features were defined as the left-right ratio of bilateral adrenals radiomics features, and then input feature vectors
into automatic machine learning for model training. Results According to the automatic model screening, the random forest classifier
achieved good overall performance in predicting AVS results, with an accuracy of 0.7500, a recall rate of 0.7466, and an area under
operating receiver characteristic curve of 0.8792. Conclusion This system has shown certain potential in predicting AVS outcomes in
PA patients. Therefore, the machine learning model can assist in predicting the subtype diagnosis of PA in routine clinical practice.
谢薇a，陈涛b，罗国婷a,c，王寒箫a，舒炀a，刘娟a，郑涛d，孙怀强a,c. 基于影像组学和自动机器学习的PA患者
肾上腺静脉取血结果的研究[J]. 中国医疗设备, 2024, 39(2): 45-51.
XIE Weia, CHEN Taob, LUO Guotinga,c, WANG Hanxiaoa, SHU Yanga, LIU Juana, ZHENG Taod, SUN Huaiqianga,c. Prediction of Adrenal Venous Sampling Outcome in Patients with PA Using
Radiomics and Automated Machine Learning. China Medical Devices, 2024, 39(2): 45-51.
