结合剂量组学与机器学习预测儿童癌症幸存者严重心脏疾病：法国儿童癌症幸存者研究数据表1

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Combining dosiomics and machine learning methods for predicting severe cardiac diseases in childhood cancer survivors: the French Childhood Cancer Survivor Study.pdf
• DOI：10.3389/fonc.2024.1241221.s001
• 发布日期：2024-12-02
• 文件数：1 个
• 文件总体积：4.82 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Combining_dosiomics_and_machine_learning_methods_for_predicting_severe_cardiac_diseases_in_childhood_cancer_survivors_the_French_Childhood_Cancer_Survivor_Study_pdf/27937695

命令行下载

curl -L -o "Data Sheet 1_Combining dosiomics and machine learning methods for predicting severe cardiac diseases in childhood cancer survivors: the French Childho.pdf" "https://ndownloader.figshare.com/files/50905044"

文件清单

• Data Sheet 1_Combining dosiomics and machine learning methods for predicting severe cardiac diseases in childhood cancer survivors: the French Childho.pdf（4.82 MB）

文件总体积 4.82 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

BackgroundCardiac disease (CD) is a primary long-term diagnosed pathology among childhood cancer survivors. Dosiomics (radiomics extracted from the dose distribution) have received attention in the past few years to assess better the induced risk of radiotherapy (RT) than standard dosimetric features such as dose-volume indicators. Hence, using the spatial information contained in the dosiomics features with machine learning methods may improve the prediction of CD.
MethodsWe considered the 7670 5-year survivors of the French Childhood Cancer Survivors Study (FCCSS). Dose-volume and dosiomics features are extracted from the radiation dose distribution of 3943 patients treated with RT. Survival analysis is performed considering several groups of features and several models [Cox Proportional Hazard with Lasso penalty, Cox with Bootstrap Lasso selection, Random Survival Forests (RSF)]. We establish the performance of dosiomics compared to baseline models by estimating C-index and Integrated Brier Score (IBS) metrics with 5-fold stratified cross-validation and compare their time-dependent error curves.
ResultsAn RSF model adjusted on the first-order dosiomics predictors extracted from the whole heart performed best regarding the C-index (0.792 ± 0.049), and an RSF model adjusted on the first-order dosiomics predictors extracted from the heart’s subparts performed best regarding the IBS (0.069 ± 0.05). However, the difference is not statistically significant with the standard models (C-index of Cox PH adjusted on dose-volume indicators: 0.791 ± 0.044; IBS of Cox PH adjusted on the mean dose to the heart: 0.074 ± 0.056).
ConclusionIn this study, dosiomics models have slightly better performance metrics but they do not outperform the standard models significantly. Quantiles of the dose distribution may contain enough information to estimate the risk of late radio-induced high-grade CD in childhood cancer survivors.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2024.1241221.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Combining_dosiomics_and_machine_learning_methods_for_predicting_severe_cardiac_diseases_in_childhood_cancer_survivors_the_French_Childhood_Cancer_Survivor_Study_pdf/27937695）。