弥漫性大B细胞淋巴瘤复发或难治风险预测数据集（Table 2）

数据集获取（Figshare）

• 源站标题：Table 2_Predicting the risk of relapsed or refractory in patients with diffuse large B-cell lymphoma via deep learning.docx
• DOI：10.3389/fonc.2025.1480645.s002
• 发布日期：2025-03-03
• 文件数：1 个
• 文件总体积：191.91 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Predicting_the_risk_of_relapsed_or_refractory_in_patients_with_diffuse_large_B-cell_lymphoma_via_deep_learning_docx/28521494

命令行下载

curl -L -o "Table 2_Predicting the risk of relapsed or refractory in patients with diffuse large B-cell lymphoma via deep learning.docx" "https://ndownloader.figshare.com/files/52726982"

文件清单

• Table 2_Predicting the risk of relapsed or refractory in patients with diffuse large B-cell lymphoma via deep learning.docx（191.91 KB）

文件总体积 191.91 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

IntroductionDiffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma (NHL) in humans, and it is a highly heterogeneous malignancy with a 40% to 50% risk of relapsed or refractory (R/R), leading to a poor prognosis. So early prediction of R/R risk is of great significance for adjusting treatments and improving the prognosis of patients.
MethodsWe collected clinical information and H&E images of 227 patients diagnosed with DLBCL in Xuzhou Medical University Affiliated Hospital from 2015 to 2018. Patients were then divided into R/R group and non-relapsed & non-refractory group based on clinical diagnosis, and the two groups were randomly assigned to the training set, validation set and test set in a ratio of 7:1:2. We developed a model to predict the R/R risk of patients based on clinical features utilizing the random forest algorithm. Additionally, a prediction model based on histopathological images was constructed using CLAM, a weakly supervised learning method after extracting image features with convolutional networks. To improve the prediction performance, we further integrated image features and clinical information for fusion modeling.
ResultsThe average area under the ROC curve value of the fusion model was 0.71±0.07 in the validation dataset and 0.70±0.04 in the test dataset. This study proposed a novel method for predicting the R/R risk of DLBCL based on H&E images and clinical features.
DiscussionFor patients predicted to have high risk, follow-up monitoring can be intensified, and treatment plans can be adjusted promptly.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2025.1480645.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Predicting_the_risk_of_relapsed_or_refractory_in_patients_with_diffuse_large_B-cell_lymphoma_via_deep_learning_docx/28521494）。