英国背景下子宫内膜癌女性林奇综合征反射性检测的成本效益分析数据集
针对子宫内膜癌女性林奇综合征反射性检测的成本效益分析数据集,包含决策模型参数、成本和健康结局数据。

基本信息
资源简介
英国背景下子宫内膜癌女性林奇综合征反射性检测的成本效益分析数据集
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集,收录 7 个文件、合计 1.60 MB,主题涵盖 DNA mismatch repair、Lynch syndrome、NHS、Results Reflex testing、Model input parameters,可用于统计建模、特征工程与队列分析,CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Cost-effectiveness analysis of reflex testing for Lynch syndrome in women with endometrial cancer in the UK setting》。配套论文《Cost-effectiveness analysis of reflex testing for Lynch syndrome in women with endometrial cancer in the UK setting》发表于 PLOS ONE(2019),DOI:10.1371/journal.pone.0221419。源站首次发布:2019-08-30。
【数据内容】
源站原始描述:Background Lynch syndrome is a hereditary cancer syndrome caused by constitutional pathogenic variants in the DNA mismatch repair (MMR) system, leading to increased risk of colorectal, endometrial and other cancers. The study aimed to identify the incremental costs and consequences of strategies to identify Lynch syndrome in women with endometrial cancer. Methods A decision-analytic model was developed to evaluate the relative cost-effectiveness of reflex testing strategies for identifying Lynch…
【数据结构与技术规格】
文件数 7 个,合计 1.60 MB。pone.0221419.s001.docx:application/vnd.openxmlformats-officedocument.wordprocessingml.document,58.59 KB。pone.0221419.s002.docx:application/vnd.openxmlformats-officedocument.wordprocessingml.document,219.76 KB。pone.0221419.s003.docx:application/vnd.openxmlformats-officedocument.wordprocessingml.document,308.17 KB。数据集 DOI:10.1371/journal.pone.0221419。
【主题与分类】
源站学科分类:Medicine、Genetics、Biotechnology、Sociology、Mathematical Sciences not elsewhere classified、Science Policy。主题标签:DNA mismatch repair、Lynch syndrome、NHS、Results Reflex testing、Model input parameters、endometrial cancer、MMR immunohistochemistry、strategy、Background Lynch syndrome、MLH 1 methylation testing。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面:https://plos.figshare.com/articles/dataset/Cost-effectiveness_analysis_of_reflex_testing_for_Lynch_syndrome_in_women_with_endometrial_cancer_in_the_UK_setting/9753884
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:Snowsill, Tristan M.; Ryan, Neil A. J.; Crosbie, Emma J.; Frayling, Ian M.; Evans, D. Gareth; Hyde, Chris J. (2019). Cost-effectiveness analysis of reflex testing for Lynch syndrome in women with endometrial cancer in the UK setting. PLOS ONE. Dataset
【使用注意】
· 源站最后修改于 2023-05-30,引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载,适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档,适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务(收技术服务费,非数据销售)
暂未开放当前数据集主要来源为 Kaggle 公开托管,完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取(Figshare)
- 源站标题:Cost-effectiveness analysis of reflex testing for Lynch syndrome in women with endometrial cancer in the UK setting
- DOI:10.1371/journal.pone.0221419
- 发布日期:2019-08-30
- 文件数:7 个
- 文件总体积:1.60 MB
- 源站页面:https://plos.figshare.com/articles/dataset/Cost-effectiveness_analysis_of_reflex_testing_for_Lynch_syndrome_in_women_with_endometrial_cancer_in_the_UK_setting/9753884
命令行下载
curl -L -o "pone.0221419.s001.docx" "https://ndownloader.figshare.com/files/17473301"
curl -L -o "pone.0221419.s002.docx" "https://ndownloader.figshare.com/files/17473304"
curl -L -o "pone.0221419.s003.docx" "https://ndownloader.figshare.com/files/17473307"
curl -L -o "pone.0221419.s004.docx" "https://ndownloader.figshare.com/files/17473310"
curl -L -o "pone.0221419.s005.docx" "https://ndownloader.figshare.com/files/17473313"
curl -L -o "pone.0221419.s006.docx" "https://ndownloader.figshare.com/files/17473316"
curl -L -o "pone.0221419.s007.docx" "https://ndownloader.figshare.com/files/17473319"
文件清单
pone.0221419.s001.docx(58.59 KB)pone.0221419.s002.docx(219.76 KB)pone.0221419.s003.docx(308.17 KB)pone.0221419.s004.docx(159.64 KB)pone.0221419.s005.docx(218.44 KB)pone.0221419.s006.docx(127.93 KB)pone.0221419.s007.docx(505.44 KB)
文件总体积 1.60 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
Background
Lynch syndrome is a hereditary cancer syndrome caused by constitutional pathogenic variants in the DNA mismatch repair (MMR) system, leading to increased risk of colorectal, endometrial and other cancers. The study aimed to identify the incremental costs and consequences of strategies to identify Lynch syndrome in women with endometrial cancer.
Methods
A decision-analytic model was developed to evaluate the relative cost-effectiveness of reflex testing strategies for identifying Lynch syndrome in women with endometrial cancer taking the NHS perspective and a lifetime horizon. Model input parameters were sourced from various published sources. Consequences were measured using quality-adjusted life years (QALYs). A cost-effectiveness threshold of £20 000/QALY was used.
Results
Reflex testing for Lynch syndrome using MMR immunohistochemistry and MLH1 methylation testing was cost-effective versus no testing, costing £14 200 per QALY gained. There was uncertainty due to parameter imprecision, with an estimated 42% chance this strategy is not cost-effective compared with no testing. Age had a significant impact on cost-effectiveness, with testing not predicted to be cost-effective in patients aged 65 years and over.
Conclusions
Testing for Lynch syndrome in younger women with endometrial cancer using MMR immunohistochemistry and MLH1 methylation testing may be cost-effective. Age cut-offs may be controversial and adversely affect implementation.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0221419(source: https://plos.figshare.com/articles/dataset/Cost-effectiveness_analysis_of_reflex_testing_for_Lynch_syndrome_in_women_with_endometrial_cancer_in_the_UK_setting/9753884)。
数据缺失?
依托陕西、四川两大基地,我们与超过十家三甲医院建立直接合作关系,覆盖合规授权、采集治理、专业标注、数据交付的全流程,为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务?立即联系我们获取专业方案。




