结肠腺癌组织病理学图像与基因组数据整合分析数据集

数据集获取（Figshare）

• 源站标题：DataSheet_1_Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma.zip
• DOI：10.3389/fonc.2021.636451.s001
• 发布日期：2021-09-27
• 文件数：1 个
• 文件总体积：14.36 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Integrative_Analysis_of_Histopathological_Images_and_Genomic_Data_in_Colon_Adenocarcinoma_zip/16683898

命令行下载

curl -L -o "DataSheet_1_Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma.zip" "https://ndownloader.figshare.com/files/30891961"

文件清单

• DataSheet_1_Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma.zip（14.36 MB）

文件总体积 14.36 MB（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

BackgroundColon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD.
MethodsWe downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF).
ResultsThere were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group.
ConclusionsThese results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2021.636451.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Integrative_Analysis_of_Histopathological_Images_and_Genomic_Data_in_Colon_Adenocarcinoma_zip/16683898）。