基于机器学习预测糖尿病肾病的肾小管间质病变：一项多中心验证研究

数据集获取（Figshare）

• 源站标题：Machine learning-based prediction of tubulointerstitial lesions in diabetic kidney disease: a multicenter validation study
• DOI：10.6084/m9.figshare.29964981.v1
• 发布日期：2025-08-22
• 文件数：11 个
• 文件总体积：3.35 GB
• 源站页面：https://tandf.figshare.com/articles/dataset/Machine_learning-based_prediction_of_tubulointerstitial_lesions_in_diabetic_kidney_disease_a_multicenter_validation_study/29964981

命令行下载

curl -L -o "irnf_a_2547266_sm0790.docx" "https://ndownloader.figshare.com/files/57352528"
curl -L -o "Figure 1.tif" "https://ndownloader.figshare.com/files/57352531"
curl -L -o "Figure 2.tif" "https://ndownloader.figshare.com/files/57352534"
curl -L -o "Figure 7.tif" "https://ndownloader.figshare.com/files/57352537"
curl -L -o "Figure 8.tif" "https://ndownloader.figshare.com/files/57352540"
curl -L -o "Figure3.tif" "https://ndownloader.figshare.com/files/57352543"
curl -L -o "Figures.docx" "https://ndownloader.figshare.com/files/57352546"
curl -L -o "figure 4.tif" "https://ndownloader.figshare.com/files/57352549"

文件清单

• irnf_a_2547266_sm0790.docx（39.16 KB）
• Figure 1.tif（206.95 MB）
• Figure 2.tif（650.27 MB）
• Figure 7.tif（546.69 MB）
• Figure 8.tif（77.96 MB）
• Figure3.tif（49.56 MB）
• Figures.docx（1.27 GB）
• figure 4.tif（264.32 MB）
• figure 5.tif（94.30 MB）
• figure 6.tif（187.33 MB）
• ~$igures.docx（162 B）

文件总体积 3.35 GB（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

Tubulointerstitial lesions (TILs) play a crucial role in the progression of diabetic kidney disease (DKD). Current clinical prediction methods rely heavily on invasive biopsies and fall short of providing a comprehensive, multidimensional assessment.
This multicenter study, conducted from 2010 to 2024, involved patients with biopsy-confirmed DKD. We employed logistic regression and the Least Absolute Shrinkage and Selection Operator (LASSO) with 10-fold cross-validation to identify key predictive indicators. A machine learning-based nomogram was developed and model performance is evaluated by discrimination, calibration, and decision curve analysis (DCA).
A total of 337 were divided into training (n = 180), internal validation (n = 78), and external validation (n = 79) cohorts. Multivariate analysis confirmed SCr (OR = 1.02, 95% confidence interval [CI]:1.01–1.03), HDL (OR = 0.68, 95% CI: 0.52–0.85), and severe glomerular hyperplasia (OR = 9.79, 95% CI: 1.41–80.89) as independent predictors. The AUC for the training, internal validation, and external validation cohorts were 0.93, 0.86, and 0.94, respectively, indicating high calibration accuracy (p > 0.05). DCA demonstrated a 75% higher net clinical benefit compared to traditional models at risk thresholds exceeding 18.
In order to avoid repeated renal biopsy puncture, by integrating kidney function, metabolic markers, and pathological features, the model provides more accurate diagnosis and treatment suggestions for predicting the risk of TILs and further deterioration of renal function. This facilitates the early identification of high-risk patients and supports targeted interventions, marking a paradigm shift toward precision nephrology in the management of DKD.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.29964981.v1（source: https://tandf.figshare.com/articles/dataset/Machine_learning-based_prediction_of_tubulointerstitial_lesions_in_diabetic_kidney_disease_a_multicenter_validation_study/29964981）。