基于临床和SNP数据的宫颈癌多变量预测预警模型数据表

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_A multi-variable predictive warning model for cervical cancer using clinical and SNPs data.PDF
• DOI：10.3389/fmed.2024.1294230.s001
• 发布日期：2024-02-22
• 文件数：1 个
• 文件总体积：31.51 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_A_multi-variable_predictive_warning_model_for_cervical_cancer_using_clinical_and_SNPs_data_PDF/25265689

命令行下载

curl -L -o "Data_Sheet_1_A multi-variable predictive warning model for cervical cancer using clinical and SNPs data.PDF" "https://ndownloader.figshare.com/files/44637532"

文件清单

• Data_Sheet_1_A multi-variable predictive warning model for cervical cancer using clinical and SNPs data.PDF（31.51 KB）

文件总体积 31.51 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

IntroductionCervical cancer is the fourth most common cancer among female worldwide. Early detection and intervention are essential. This study aims to construct an early predictive warning model for cervical cancer and precancerous lesions utilizing clinical data and simple nucleotide polymorphisms (SNPs).
MethodsClinical data and germline SNPs were collected from 472 participants. Univariate logistic regression, least absolute shrinkage selection operator (LASSO), and stepwise regression were performed to screen variables. Logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), extreme gradient boosting(XGBoost) and neural network(NN) were applied to establish models. The receiver operating characteristic (ROC) curve was used to compare the models’ efficiencies. The performance of models was validated using decision curve analysis (DCA).
ResultsThe LR model, which included 6 SNPs and 2 clinical variables as independent risk factors for cervical carcinogenesis, was ultimately chosen as the most optimal model. The DCA showed that the LR model had a good clinical application.
DiscussionThe predictive model effectively foresees cervical cancer risk using clinical and SNP data, aiding in planning timely interventions. It provides a transparent tool for refining clinical decisions in cervical cancer management.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2024.1294230.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_A_multi-variable_predictive_warning_model_for_cervical_cancer_using_clinical_and_SNPs_data_PDF/25265689）。