胶质瘤患者肿瘤切除术后生存预测的新监督机器学习分析实践数据集

数据集获取（Figshare）

• 源站标题：Datasheet1_Practice of the new supervised machine learning predictive analytics for glioma patient survival after tumor resection: Experiences in a high-volume Chinese center.zip
• DOI：10.3389/fsurg.2022.975022.s001
• 发布日期：2023-02-17
• 文件数：1 个
• 文件总体积：593.63 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Datasheet1_Practice_of_the_new_supervised_machine_learning_predictive_analytics_for_glioma_patient_survival_after_tumor_resection_Experiences_in_a_high-volume_Chinese_center_zip/22115684

命令行下载

curl -L -o "Datasheet1_Practice of the new supervised machine learning predictive analytics for glioma patient survival after tumor resection: Experiences in a hi.zip" "https://ndownloader.figshare.com/files/39301100"

文件清单

• Datasheet1_Practice of the new supervised machine learning predictive analytics for glioma patient survival after tumor resection: Experiences in a hi.zip（593.63 KB）

文件总体积 593.63 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

ObjectiveThis study aims to assess the effectiveness of the Gradient Boosting (GB) algorithm on glioma prognosis prediction and to explore new predictive models for glioma patient survival after tumor resection.
MethodsA cohort of 776 glioma cases (WHO grades II–IV) between 2010 and 2017 was obtained. Clinical characteristics and biomarker information were reviewed. Subsequently, we constructed the conventional Cox survival model and three different supervised machine learning models, including support vector machine (SVM), random survival forest (RSF), Tree GB, and Component GB. Then, the model performance was compared with each other. At last, we also assessed the feature importance of models.
ResultsThe concordance indexes of the conventional survival model, SVM, RSF, Tree GB, and Component GB were 0.755, 0.787, 0.830, 0.837, and 0.840, respectively. All areas under the cumulative receiver operating characteristic curve of both GB models were above 0.800 at different survival times. Their calibration curves showed good calibration of survival prediction. Meanwhile, the analysis of feature importance revealed Karnofsky performance status, age, tumor subtype, extent of resection, and so on as crucial predictive factors.
ConclusionGradient Boosting models performed better in predicting glioma patient survival after tumor resection than other models.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fsurg.2022.975022.s001（source: https://frontiersin.figshare.com/articles/dataset/Datasheet1_Practice_of_the_new_supervised_machine_learning_predictive_analytics_for_glioma_patient_survival_after_tumor_resection_Experiences_in_a_high-volume_Chinese_center_zip/22115684）。