子宫腺肌症患者转铁蛋白饱和度与贫血相关生物标志物特征数据集

数据集获取

源站：https://doi.org/10.5281/zenodo.18637559

标准获取流程：

1. 访问源站链接，注册/登录账号；
2. 进入数据集详情页，按页面指引申请权限或直接下载；
3. 部分平台提供 SDK，例如：
   • OpenDataLab: pip install openxlab 后 openxlab.dataset.get(...)
   • ModelScope: pip install modelscope 后 modelscope download
   • 天池: 登录后页面提供直链下载
     以源站「数据下载/使用文档」页面为准。

数据集大小约 11.5 KB。

数据说明

This dataset contains the anonymized clinical data analyzed in the study titled “Characteristics of transferrin saturation and anemia-related biomarkers in patients with uterine adenomyosis”. Abstract:Background: Ferritin may be elevated as an acute-phase reactant in inflammatory conditions, potentially masking iron deficiency. We aimed to evaluate the frequency of iron deficiency and the diagnostic significance of transferrin saturation (TSAT) in women with uterine adenomyosis. Methods: This retrospective study included non-menopausal women aged 30–55 years who had iron metabolism markers measured at first visit. Anemia was defined as hemoglobin (Hb) <11 g/dL. Iron deficiency was defined using three criteria: ferritin-only (serum ferritin <20 ng/mL), TSAT-only (TSAT <20%), and a combined criterion (ferritin <20 ng/mL and/or TSAT <20%). Functional iron deficiency was defined as TSAT <20%, with ferritin ≥20 ng/mL. We compared the frequencies of anemia and iron deficiency across groups and evaluated correlations between Hb and TSAT. Results: Iron deficiency was more common than anemia in all patient groups. In the adenomyosis group, the frequency of iron deficiency based on the combined criterion was significantly higher than that diagnosed using ferritin alone. The correlation between Hb and log(TSAT) was weak in the adenomyosis group but strong in the myoma and no uterine structural abnormality groups. Conclusions: Measuring TSAT and ferritin levels enhances the detection of iron deficiency in women with adenomyosis and may help identify functional iron deficiency when ferritin interpretability is limited by inflammation.

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。