异时性第二原发肺癌患者生存率预测与验证数据集

数据集获取（Figshare）

• 源站标题：Prediction and validation of survival rate of metachronous second primary lung cancer patients using machine learning classifiers
• DOI：10.6084/m9.figshare.22359925.v1
• 发布日期：2023-03-30
• 文件数：2 个
• 文件总体积：131.06 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Prediction_and_validation_of_survival_rate_of_metachronous_second_primary_lung_cancer_patients_using_machine_learning_classifiers/22359925

命令行下载

curl -L -o "sup fig.odt" "https://ndownloader.figshare.com/files/39796660"
curl -L -o "supplementary.odt" "https://ndownloader.figshare.com/files/39796663"

文件清单

• sup fig.odt（108.42 KB）
• supplementary.odt（22.65 KB）

文件总体积 131.06 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

Machine learning (ML) has been applied recently to develop prognostic classification models that can be used in individual cancer patients to forecast outcomes. Here, four different ML algorithms were built to predict survival rate of lung cancer patients using 1600 metadata records. Of note, the generated models were validated using test set and external validation data set consisting of 400 patient records each together with 10-fold cross-validation technique. The extratree classifier algorithm was employed to identify the influential descriptors for patients survival after incidence of metachronous second primary lung cancer. The models were assessed using five different performance metrices. The results from our study highlight that logistic regression model with all features and important features achieved an accuracy of 94% and 96%, respectively, for stratifying the survival status of lung cancer patients. On the other hand, logistic regression also outperformed external validation with an accuracy of 85%. Indeed, the results from our study will provide meaningful insights for the treatment and management of large community of lung cancer patients.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.22359925.v1（source: https://tandf.figshare.com/articles/dataset/Prediction_and_validation_of_survival_rate_of_metachronous_second_primary_lung_cancer_patients_using_machine_learning_classifiers/22359925）。