肺腺癌软脑膜转移预测模型（molGPA）表格数据

数据集获取（Figshare）

• 源站标题：Table_4_Predictors of Lung Adenocarcinoma With Leptomeningeal Metastases: A 2022 Targeted-Therapy-Assisted molGPA Model.docx
• DOI：10.3389/fonc.2022.903851.s006
• 发布日期：2022-06-20
• 文件数：1 个
• 文件总体积：13.03 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_4_Predictors_of_Lung_Adenocarcinoma_With_Leptomeningeal_Metastases_A_2022_Targeted-Therapy-Assisted_molGPA_Model_docx/20103335

命令行下载

curl -L -o "Table_4_Predictors of Lung Adenocarcinoma With Leptomeningeal Metastases: A 2022 Targeted-Therapy-Assisted molGPA Model.docx" "https://ndownloader.figshare.com/files/35959511"

文件清单

• Table_4_Predictors of Lung Adenocarcinoma With Leptomeningeal Metastases: A 2022 Targeted-Therapy-Assisted molGPA Model.docx（13.03 KB）

文件总体积 13.03 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

ObjectiveTo explore prognostic indicators of lung adenocarcinoma with leptomeningeal metastases (LM) and provide an updated graded prognostic assessment model integrated with molecular alterations (molGPA).
MethodsA cohort of 162 patients was enrolled from 202 patients with lung adenocarcinoma and LM. By randomly splitting data into the training (80%) and validation (20%) sets, the Cox regression and random survival forest methods were used on the training set to identify statistically significant variables and construct a prognostic model. The C-index of the model was calculated and compared with that of previous molGPA models.
ResultsThe Cox regression and random forest models both identified four variables, which included KPS, LANO neurological assessment, TKI therapy line, and controlled primary tumor, as statistically significant predictors. A novel targeted-therapy-assisted molGPA model (2022) using the above four prognostic factors was developed to predict LM of lung adenocarcinoma. The C-indices of this prognostic model in the training and validation sets were higher than those of the lung-molGPA (2017) and molGPA (2019) models.
ConclusionsThe 2022 molGPA model, a substantial update of previous molGPA models with better prediction performance, may be useful in clinical decision making and stratification of future clinical trials.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2022.903851.s006（source: https://frontiersin.figshare.com/articles/dataset/Table_4_Predictors_of_Lung_Adenocarcinoma_With_Leptomeningeal_Metastases_A_2022_Targeted-Therapy-Assisted_molGPA_Model_docx/20103335）。