用于个性化粪便结直肠癌筛查的进化算法数据表1

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_An Evolutionary Algorithm to Personalize Stool-Based Colorectal Cancer Screening.pdf
• DOI：10.3389/fphys.2021.718276.s001
• 发布日期：2022-01-26
• 文件数：1 个
• 文件总体积：244.91 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_An_Evolutionary_Algorithm_to_Personalize_Stool-Based_Colorectal_Cancer_Screening_pdf/19069556

命令行下载

curl -L -o "Data_Sheet_1_An Evolutionary Algorithm to Personalize Stool-Based Colorectal Cancer Screening.pdf" "https://ndownloader.figshare.com/files/33894014"

文件清单

• Data_Sheet_1_An Evolutionary Algorithm to Personalize Stool-Based Colorectal Cancer Screening.pdf（244.91 KB）

文件总体积 244.91 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

BackgroundFecal immunochemical testing (FIT) is an established method for colorectal cancer (CRC) screening. Measured FIT-concentrations are associated with both present and future risk of CRC, and may be used for personalized screening. However, evaluation of personalized screening is computationally challenging. In this study, a broadly applicable algorithm is presented to efficiently optimize personalized screening policies that prescribe screening intervals and FIT-cutoffs, based on age and FIT-history.
MethodsWe present a mathematical framework for personalized screening policies and a bi-objective evolutionary algorithm that identifies policies with minimal costs and maximal health benefits. The algorithm is combined with an established microsimulation model (MISCAN-Colon), to accurately estimate the costs and benefits of generated policies, without restrictive Markov assumptions. The performance of the algorithm is demonstrated in three experiments.
ResultsIn Experiment 1, a relatively small benchmark problem, the optimal policies were known. The algorithm approached the maximum feasible benefits with a relative difference of 0.007%. Experiment 2 optimized both intervals and cutoffs, Experiment 3 optimized cutoffs only. Optimal policies in both experiments are unknown. Compared to policies recently evaluated for the USPSTF, personalized screening increased health benefits up to 14 and 4.3%, for Experiments 2 and 3, respectively, without adding costs. Generated policies have several features concordant with current screening recommendations.
DiscussionThe method presented in this paper is flexible and capable of optimizing personalized screening policies evaluated with computationally-intensive but established simulation models. It can be used to inform screening policies for CRC or other diseases. For CRC, more debate is needed on what features a policy needs to exhibit to make it suitable for implementation in practice.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fphys.2021.718276.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_An_Evolutionary_Algorithm_to_Personalize_Stool-Based_Colorectal_Cancer_Screening_pdf/19069556）。