数据表1：基于CT成像的深度图神经网络用于早期肺癌生存分析——多中心研究

数据集获取（Figshare）

• 源站标题：DataSheet_1_Imaging-Based Deep Graph Neural Networks for Survival Analysis in Early Stage Lung Cancer Using CT: A Multicenter Study.docx
• DOI：10.3389/fonc.2022.868186.s001
• 发布日期：2022-07-13
• 文件数：1 个
• 文件总体积：679.54 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Imaging-Based_Deep_Graph_Neural_Networks_for_Survival_Analysis_in_Early_Stage_Lung_Cancer_Using_CT_A_Multicenter_Study_docx/20300940

命令行下载

curl -L -o "DataSheet_1_Imaging-Based Deep Graph Neural Networks for Survival Analysis in Early Stage Lung Cancer Using CT: A Multicenter Study.docx" "https://ndownloader.figshare.com/files/36259386"

文件清单

• DataSheet_1_Imaging-Based Deep Graph Neural Networks for Survival Analysis in Early Stage Lung Cancer Using CT: A Multicenter Study.docx（679.54 KB）

文件总体积 679.54 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

BackgroundLung cancer is the leading cause of cancer-related mortality, and accurate prediction of patient survival can aid treatment planning and potentially improve outcomes. In this study, we proposed an automated system capable of lung segmentation and survival prediction using graph convolution neural network (GCN) with CT data in non-small cell lung cancer (NSCLC) patients.
MethodsIn this retrospective study, we segmented 10 parts of the lung CT images and built individual lung graphs as inputs to train a GCN model to predict 5-year overall survival. A Cox proportional-hazard model, a set of machine learning (ML) models, a convolutional neural network based on tumor (Tumor-CNN), and the current TNM staging system were used as comparison.
FindingsA total of 1,705 patients (main cohort) and 125 patients (external validation cohort) with lung cancer (stages I and II) were included. The GCN model was significantly predictive of 5-year overall survival with an AUC of 0.732 (p The proposed GCN model outperformed all ML, Tumor-CNN, and TNM staging models. This study demonstrated the value of utilizing medical imaging graph structure data, resulting in a robust and effective model for the prediction of survival in early-stage lung cancer.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2022.868186.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Imaging-Based_Deep_Graph_Neural_Networks_for_Survival_Analysis_in_Early_Stage_Lung_Cancer_Using_CT_A_Multicenter_Study_docx/20300940）。