宫颈癌放疗后骨盆不全骨折风险因素数据集
多中心宫颈癌放疗后骨盆不全骨折风险因素数据集,包含208名患者临床表格数据。

基本信息
资源简介
宫颈癌放疗后骨盆不全骨折风险因素数据集
【数据集背景】
本数据集源站标题为《Treatment characteristics of each institution.》。源站首次发布:2026-03-27。
【数据内容】
源站原始描述:Objective This multicenter study aimed to identify the risk factors for pelvic insufficiency fractures (PIFs) in women who received definitive radiotherapy (RT) as the initial treatment for cervical cancer and to examine the differences in the incidence of PIFs across institutions. Patients and Methods The medical records of 208 women were reviewed. These women received definitive RT as an initial treatment for cervical cancer at four institutions between January 2016 and December 2018. Results…
【数据结构与技术规格】
文件数 1 个,合计 9.73 KB。Table 4.xls:application/vnd.ms-excel,9.73 KB。数据集 DOI:10.1371/journal.pone.0344384.t004。
【主题与分类】
源站学科分类:Medicine、Cell Biology、Neuroscience、Biotechnology、Mathematical Sciences not elsewhere classified、Developmental Biology。主题标签:multicenter study aimed、29 &# 8211、0 %, despite、within two years、individualized risk assessment、93 years ).、standardized treatment conditions、institutional quality control、independent risk factors、received definitive radiotherapy。
【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/_p_Treatment_characteristics_of_each_institution_p_/31873508
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:Shirakawa, Shinsuke; Nagao, Shoji; Yoshio, Kotaro; Mitsuhashi, Toshiharu; Imafuku, Noriaki; Yamamoto, Dan; et al. (2026). Treatment characteristics of each institution.. PLOS ONE. Dataset
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:<p>Treatment characteristics of each institution.</p>
- DOI:10.1371/journal.pone.0344384.t004
- 发布日期:2026-03-27
- 文件数:1 个
- 文件总体积:9.73 KB
- 源站页面:https://plos.figshare.com/articles/dataset/_p_Treatment_characteristics_of_each_institution_p_/31873508
命令行下载
curl -L -o "Table 4.xls" "https://ndownloader.figshare.com/files/63195179"
文件清单
Table 4.xls(9.73 KB)
文件总体积 9.73 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
Objective
This multicenter study aimed to identify the risk factors for pelvic insufficiency fractures (PIFs) in women who received definitive radiotherapy (RT) as the initial treatment for cervical cancer and to examine the differences in the incidence of PIFs across institutions.
Patients and Methods
The medical records of 208 women were reviewed. These women received definitive RT as an initial treatment for cervical cancer at four institutions between January 2016 and December 2018.
Results
The median age was 61.5 years (range: 29–93 years). Overall, 59 patients (28.4%) developed PIF, with 48 (81.4%) of them developing it within two years after completion of RT. Multivariate analysis identified postmenopausal status and treating institution as independent risk factors. The incidence of PIF varied significantly among institutions, ranging from 18.9% to 50.0%, despite no significant differences in patient demographics or RT protocols.
Conclusion
Substantial inter-institutional variation in PIF incidence was observed, even under standardized treatment conditions. These findings underscore the need for individualized risk assessment and institutional quality control in the long-term management of cervical cancer survivors.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0344384.t004(source: https://plos.figshare.com/articles/dataset/_p_Treatment_characteristics_of_each_institution_p_/31873508)。
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。




