原发性醛固酮增多症特殊亚型（肾上腺CT阴性）预测模型数据集

数据集获取（Figshare）

• 源站标题：Table 8_Development and validation of prediction models for special subtype of primary aldosteronism: patients with negative adrenal CT imaging.xlsx
• DOI：10.3389/fendo.2025.1563748.s006
• 发布日期：2025-07-11
• 文件数：1 个
• 文件总体积：13.79 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_8_Development_and_validation_of_prediction_models_for_special_subtype_of_primary_aldosteronism_patients_with_negative_adrenal_CT_imaging_xlsx/29540432

命令行下载

curl -L -o "Table 8_Development and validation of prediction models for special subtype of primary aldosteronism: patients with negative adrenal CT imaging.xlsx" "https://ndownloader.figshare.com/files/56172971"

文件清单

• Table 8_Development and validation of prediction models for special subtype of primary aldosteronism: patients with negative adrenal CT imaging.xlsx（13.79 KB）

文件总体积 13.79 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

ObjectiveCurrent subtype diagnosis of primary aldosteronism relies on adrenal venous sampling and imaging, each with inherent limitations. Lesional adrenal glands with negative CT Imaging is a distinct subtype of primary aldosteronism that has been less frequently studied. The aim of this study was to develop and validate a machine learning and AI model for distinguishing adrenals with transversely negative lesions from normal adrenals Primary Aldosteronism.
Materials and methodsWe conducted a single-center retrospective study, assessing transverse adrenal scans of 170 PA patients. A specialized iterative method was employed for radiomic feature selection. Subsequently, six conventional machine learning methodologies were utilized to construct the radiomics models. This original data was subsequently applied in the construction of a radiomic model, which was combined with clinical data for the final model construction.
Results107 radiomic features were extracted from the adrenal scans and 10 features were selected for ML and AI modeling. In the clinical data, values for serum potassium, aldosterone excretion, uric acid, and IVSd were utilized in the model construction. The integration of clinical data further enhanced the model’s performance, with an AUC reaching 0.868 in the derived cohort, and an AUC of 0.853 in the temporal validation cohort.
ConclusionThe study indicates that clinical-radiomic scores can independently serve as diagnostic biomarkers for the specialized PA subtype categorization. We give the proposal for the precise categorization concept in establishing a clinical-radiomic model for PA subtype diagnosis. The model demonstrates substantial potential for both clinical and translational research.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fendo.2025.1563748.s006（source: https://frontiersin.figshare.com/articles/dataset/Table_8_Development_and_validation_of_prediction_models_for_special_subtype_of_primary_aldosteronism_patients_with_negative_adrenal_CT_imaging_xlsx/29540432）。