表1：使用深度学习方法预测三阴性乳腺癌患者复发

数据集获取（Figshare）

• 源站标题：Table_1_Predicting Relapse in Patients With Triple Negative Breast Cancer (TNBC) Using a Deep-Learning Approach.DOCX
• DOI：10.3389/fphys.2020.511071.s001
• 发布日期：2020-09-23
• 文件数：1 个
• 文件总体积：414.48 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Predicting_Relapse_in_Patients_With_Triple_Negative_Breast_Cancer_TNBC_Using_a_Deep-Learning_Approach_DOCX/12992834

命令行下载

curl -L -o "Table_1_Predicting Relapse in Patients With Triple Negative Breast Cancer (TNBC) Using a Deep-Learning Approach.DOCX" "https://ndownloader.figshare.com/files/24755459"

文件清单

• Table_1_Predicting Relapse in Patients With Triple Negative Breast Cancer (TNBC) Using a Deep-Learning Approach.DOCX（414.48 KB）

文件总体积 414.48 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

The abundance and/or location of tumor infiltrating lymphocytes (TILs), especially CD8+ T cells, in solid tumors can serve as a prognostic indicator in various types of cancer. However, it is often difficult to select an appropriate threshold value in order to stratify patients into well-defined risk groups. It is also important to select appropriate tumor regions to quantify the abundance of TILs. On the other hand, machine-learning approaches can stratify patients in an unbiased and automatic fashion. Based on immunofluorescence (IF) images of CD8+ T lymphocytes and cancer cells, we develop a machine-learning approach which can predict the risk of relapse for patients with Triple Negative Breast Cancer (TNBC). Tumor-section images from 9 patients with poor outcome and 15 patients with good outcome were used as a training set. Tumor-section images of 29 patients in an independent cohort were used to test the predictive power of our algorithm. In the test cohort, 6 (out of 29) patients who belong to the poor-outcome group were all correctly identified by our algorithm; for the 23 (out of 29) patients who belong to the good-outcome group, 17 were correctly predicted with some evidence that improvement is possible if other measures, such as the grade of tumors, are factored in. Our approach does not involve arbitrarily defined metrics and can be applied to other types of cancer in which the abundance/location of CD8+ T lymphocytes/other types of cells is an indicator of prognosis.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fphys.2020.511071.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Predicting_Relapse_in_Patients_With_Triple_Negative_Breast_Cancer_TNBC_Using_a_Deep-Learning_Approach_DOCX/12992834）。