早期肝细胞癌患者生存预测列线图数据表
包含2225例早期肝细胞癌患者的临床表格数据,用于构建和验证生存预测列线图。

基本信息
资源简介
早期肝细胞癌患者生存预测列线图数据表
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集,收录 1 个文件、合计 182.98 KB,主题涵盖 early hepatocellular carcinoma、nomogram、survival analysis、prediction、clinic utility,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《Data_Sheet_1_A Simple-to-Use Nomogram for Predicting the Survival of Early Hepatocellular Carcinoma Patients.docx》。配套论文《Comparative study of survival of patients with hepatocellular carcinoma predicted by different staging systems using multivariate analysis》发表于 European Journal of Surgical Oncology (EJSO)(2005),DOI:10.1016/j.ejso.2005.04.016。源站首次发布:2019-07-10。
【数据内容】
源站原始描述:Objective: This study aimed to develop and validate a simple-to-use nomogram for early hepatocellular carcinoma (HCC) patients undergoing a preoperative consultation and doctors conducting a postoperative evaluation. Methods: A total of 2,225 HCC patients confirmed with stage I or II were selected from the Surveillance, Epidemiology, and End Results database between January 2010 and December 2015. The patients were randomly divided into two groups: a training group (n = 1,557) and a validation g…
【数据结构与技术规格】
文件数 1 个,合计 182.98 KB。Data_Sheet_1_A Simple-to-Use Nomogram for Predicting the Survival of Early Hepatocellular Carcinoma Patients.docx:application/vnd.openxmlformats-officedocument.wordprocessingml.document,182.98 KB。数据集 DOI:10.3389/fonc.2019.00584.s001。
【主题与分类】
源站学科分类:Cancer、Haematological Tumours、Radiation Therapy、Solid Tumours、Oncology and Carcinogenesis not elsewhere classified、Molecular Targets。主题标签:early hepatocellular carcinoma、nomogram、survival analysis、prediction、clinic utility。
【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://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_A_Simple-to-Use_Nomogram_for_Predicting_the_Survival_of_Early_Hepatocellular_Carcinoma_Patients_docx/8848901
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:Chen, Si-Hai; Wan, Qin-Si; Zhou, Di; Wang, Ting; Hu, Jia; He, Yu-Ting; et al. (2019). Data_Sheet_1_A Simple-to-Use Nomogram for Predicting the Survival of Early Hepatocellular Carcinoma Patients.docx. Frontiers. Dataset
【使用注意】
· 源站最后修改于 2023-06-01,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:Data_Sheet_1_A Simple-to-Use Nomogram for Predicting the Survival of Early Hepatocellular Carcinoma Patients.docx
- DOI:10.3389/fonc.2019.00584.s001
- 发布日期:2019-07-10
- 文件数:1 个
- 文件总体积:182.98 KB
- 源站页面:https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_A_Simple-to-Use_Nomogram_for_Predicting_the_Survival_of_Early_Hepatocellular_Carcinoma_Patients_docx/8848901
命令行下载
curl -L -o "Data_Sheet_1_A Simple-to-Use Nomogram for Predicting the Survival of Early Hepatocellular Carcinoma Patients.docx" "https://ndownloader.figshare.com/files/16195652"
文件清单
Data_Sheet_1_A Simple-to-Use Nomogram for Predicting the Survival of Early Hepatocellular Carcinoma Patients.docx(182.98 KB)
文件总体积 182.98 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 study aimed to develop and validate a simple-to-use nomogram for early hepatocellular carcinoma (HCC) patients undergoing a preoperative consultation and doctors conducting a postoperative evaluation.
Methods: A total of 2,225 HCC patients confirmed with stage I or II were selected from the Surveillance, Epidemiology, and End Results database between January 2010 and December 2015. The patients were randomly divided into two groups: a training group (n = 1,557) and a validation group (n = 668). Univariate and multivariate hazards regression analyses were used to identify independent prognostic factors. The Akaike information criterion (AIC) was used to select variables for the nomogram. The performance of the nomogram was validated concerning its ability of discrimination and calibration and its clinical utility.
Results: Age, alpha-fetoprotein (AFP), race, the degree of differentiation, and therapy method were significantly associated with the prognosis of early HCC patients. Based on the AIC results, five variables (age, race, AFP, degree of differentiation, and therapy method) were incorporated into the nomogram. The concordance indexes of the simple nomogram in the training and validation groups were 0.707 (95% CI: 0.683–0.731) and 0.733 (95% CI: 0.699–0.767), respectively. The areas under the receiver operating characteristic (ROC) curve of the nomogram in the training and validation groups were 0.744 and 0.764, respectively, for predicting 3-year survival, and 0.786 and 0.794, respectively, for predicting 5-year survival. Calibration plots showed good consistency between the predictions of the nomogram and the actual observations in both the training and validation groups. Decision curve analysis (DCA) showed that the simple nomogram was clinically useful, and the overall survival significantly differed between low- and high-risk groups divided by the median score of the nomogram in the training group (P Conclusion: A simple-to-use nomogram based on a large population-based study is developed and validated, which is a conventional tool for doctors to facilitate the individual consultation of preoperative patients and the postoperative perso
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fonc.2019.00584.s001(source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_A_Simple-to-Use_Nomogram_for_Predicting_the_Survival_of_Early_Hepatocellular_Carcinoma_Patients_docx/8848901)。
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。
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