老年低级别胶质瘤基于LASSO-Cox回归的预后变量筛选与模型构建数据表：一项基于人群的队列研究

数据集获取（Figshare）

• 源站标题：DataSheet1_Variable screening and model construction for prognosis of elderly patients with lower-grade gliomas based on LASSO-Cox regression: a population-based cohort study.pdf
• DOI：10.3389/fimmu.2024.1447879.s001
• 发布日期：2024-09-11
• 文件数：1 个
• 文件总体积：2.31 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet1_Variable_screening_and_model_construction_for_prognosis_of_elderly_patients_with_lower-grade_gliomas_based_on_LASSO-Cox_regression_a_population-based_cohort_study_pdf/26982370

命令行下载

curl -L -o "DataSheet1_Variable screening and model construction for prognosis of elderly patients with lower-grade gliomas based on LASSO-Cox regression: a popul.pdf" "https://ndownloader.figshare.com/files/49113943"

文件清单

• DataSheet1_Variable screening and model construction for prognosis of elderly patients with lower-grade gliomas based on LASSO-Cox regression: a popul.pdf（2.31 MB）

文件总体积 2.31 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

BackgroundThis study aimed to identify prognostic factors for survival and develop a prognostic nomogram to predict the survival probability of elderly patients with lower-grade gliomas (LGGs).
MethodsElderly patients with histologically confirmed LGG were recruited from the Surveillance, Epidemiology, and End Results (SEER) database. These individuals were randomly allocated to the training and validation cohorts at a 2:1 ratio. First, Kaplan−Meier survival analysis and subgroup analysis were performed. Second, variable screening of all 13 variables and a comparison of predictive models based on full Cox regression and LASSO-Cox regression analyses were performed, and the key variables in the optimal model were selected to construct prognostic nomograms for OS and CSS. Finally, a risk stratification system and a web-based dynamic nomogram were constructed.
ResultsA total of 2307 elderly patients included 1220 males and 1087 females, with a median age of 72 years and a mean age of 73.30 ± 6.22 years. Among them, 520 patients (22.5%) had Grade 2 gliomas, and 1787 (77.5%) had Grade 3 gliomas. Multivariate Cox regression analysis revealed four independent prognostic factors (age, WHO grade, surgery, and chemotherapy) that were used to construct the full Cox model. In addition, LASSO-Cox regression analysis revealed five prognostic factors (age, WHO grade, surgery, radiotherapy, and chemotherapy), and a LASSO model was constructed. A comparison of the two models revealed that the LASSO model with five variables had better predictive performance than the full Cox model with four variables. Ultimately, five key variables based on LASSO-Cox regression were utilized to develop prognostic nomograms for predicting the 1-, 2-, and 5-year OS and CSS rates. The nomograms exhibited relatively good predictive ability and clinical utility. Moreover, the risk stratification system based on the nomograms effectively divided patients into low-risk and high-risk subgroups.
ConclusionVariable screening based on LASSO-Cox regression was used to determine the optimal prediction model in this study. Prognostic nomograms could serve as practical tools for predicting survival probabiliti

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fimmu.2024.1447879.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet1_Variable_screening_and_model_construction_for_prognosis_of_elderly_patients_with_lower-grade_gliomas_based_on_LASSO-Cox_regression_a_population-based_cohort_study_pdf/26982370）。