用于预测非小细胞肺癌对免疫检查点抑制剂持久反应的多特征模型表格数据

数据集获取（Figshare）

• 源站标题：Table_2_Predicting Durable Responses to Immune Checkpoint Inhibitors in Non-Small-Cell Lung Cancer Using a Multi-Feature Model.xlsx
• DOI：10.3389/fimmu.2022.829634.s004
• 发布日期：2022-04-22
• 文件数：1 个
• 文件总体积：17.05 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Predicting_Durable_Responses_to_Immune_Checkpoint_Inhibitors_in_Non-Small-Cell_Lung_Cancer_Using_a_Multi-Feature_Model_xlsx/19633980

命令行下载

curl -L -o "Table_2_Predicting Durable Responses to Immune Checkpoint Inhibitors in Non-Small-Cell Lung Cancer Using a Multi-Feature Model.xlsx" "https://ndownloader.figshare.com/files/34871199"

文件清单

• Table_2_Predicting Durable Responses to Immune Checkpoint Inhibitors in Non-Small-Cell Lung Cancer Using a Multi-Feature Model.xlsx（17.05 KB）

文件总体积 17.05 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

Due to the complex mechanisms affecting anti-tumor immune response, a single biomarker is insufficient to identify patients who will benefit from immune checkpoint inhibitors (ICIs) treatment. Therefore, a comprehensive predictive model is urgently required to predict the response to ICIs. A total of 162 non-small-cell lung cancer (NSCLC) patients undergoing ICIs treatment from three independent cohorts were enrolled and used as training and test cohorts (training cohort = 69, test cohort1 = 72, test cohort2 = 21). Eight genomic markers were extracted or calculated for each patient. Ten machine learning classifiers, such as the gaussian process classifier, random forest, and support vector machine (SVM), were evaluated. Three genomic biomarkers, namely tumor mutation burden, intratumoral heterogeneity, and loss of heterozygosity in human leukocyte antigen were screened out, and the SVM_poly method was adopted to construct a durable clinical benefit (DCB) prediction model. Compared with a single biomarker, the DCB multi-feature model exhibits better predictive value with the area under the curve values equal to 0.77 and 0.78 for test cohort1 and cohort2, respectively. The patients predicted to have DCB showed improved median progression-free survival (mPFS) and median overall survival (mOS) than those predicted to have non-durable clinical benefit.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fimmu.2022.829634.s004（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Predicting_Durable_Responses_to_Immune_Checkpoint_Inhibitors_in_Non-Small-Cell_Lung_Cancer_Using_a_Multi-Feature_Model_xlsx/19633980）。