2型糖尿病初级保健中的MASLD随访研究

覆盖MASLD、肝硬化、进展性纤维化、肌少症、超重与肥胖、2型糖尿病、高血压及OSAS等状况。原始研究纳入317名初级保健中的2型糖尿病患者

Linkoeping UniversityLinkoeping University
clinicaltrials
浏览 8
医学影像预测CirrhosisAdvanced FibrosisSarcopeniaObesity & OverweightType 2 DiabetesHypertensionOSAS (Obstructive Sleep Apneas Syndrome)Metabolic Syndrome
MASLD in Type 2 Diabetes in Primary Care - a Follow-up Study

基本信息

模态
医学影像
任务类型
预测
创建/更新时间
2026-10-12

资源简介

2型糖尿病初级保健中的MASLD随访研究

该数据集来自clinicaltrials.gov,为一项前瞻性观察性随访研究(EPSOMIP2),对象是2019至2023年间曾参与EPSONIP研究(NCT03864510)的2型糖尿病参与者。覆盖MASLD、肝硬化、进展性纤维化、肌少症、超重与肥胖、2型糖尿病、高血压及OSAS等状况。原始研究纳入317名初级保健中的2型糖尿病患者。数据可用于研究MASLD在2型糖尿病人群中的疾病进展,识别肝纤维化、肝硬化及肝癌等严重肝脏相关并发症的高风险患者(来源 clinicaltrials.gov)。

【数据内容】

源站描述(机器翻译,仅供参考):条件:MASLD - 代谢功能障碍相关脂肪性肝病、肝硬化、进展性纤维化、肌少症、肥胖与超重、2型糖尿病、高血压、OSAS(阻塞性睡眠呼吸暂停综合征)。代谢功能障碍相关脂肪性肝病(MASLD)是全球最常见的慢性肝病之一,在2型糖尿病患者中尤为常见。虽然许多患者随时间保持稳定,但部分人会发展为进展性肝纤维化、肝硬化、肝细胞癌及其他严重肝脏相关并发症。可靠识别疾病进展风险最高患者的方法仍然有限。EPSOMIP2是一项前瞻性观察性随访研究,对象是2019年至2023年间曾参与EPSONIP研究(NCT03864510)的参与者。在原始研究中,317名来自初级保健的2型糖尿病患者

【主题与分类】

主题标签:Cirrhosis、Advanced Fibrosis、Sarcopenia、Obesity & Overweight、Type 2 Diabetes、Hypertension、OSAS (Obstructive Sleep Apneas Syndrome)、Metabolic Syndrome。本站分类:肿瘤/肝癌。

【获取与许可】

源站页面:https://clinicaltrials.gov/study/NCT07312136

官方服务

专家保障全链路合规授权与标注交付

陕川双基地 × 超十家三甲医院直连

从合规授权、采集治理、专业标注到数据集交付,我们提供全链路闭环服务,让高质量临床数据即拿即用。

⚡️ 需要原始数据或标注支持?联系我们获取定制方案。

帮我联系

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

源站:https://clinicaltrials.gov/study/NCT07312136

标准获取流程:

  1. 访问源站链接,注册/登录账号;
  2. 进入数据集详情页,按页面指引申请权限或直接下载;
  3. 部分平台提供 SDK,例如:
    • OpenDataLab: pip install openxlab 后 openxlab.dataset.get(...)
    • ModelScope: pip install modelscope 后 modelscope download
    • 天池: 登录后页面提供直链下载
      以源站「数据下载/使用文档」页面为准。

数据说明

Conditions: MASLD - Metabolic Dysfunction-Associated Steatotic Liver Disease, Cirrhosis, Advanced Fibrosis, Sarcopenia, Obesity & Overweight, Type 2 Diabetes, Hypertension, OSAS (Obstructive Sleep Apneas Syndrome). Metabolic dysfunction-associated steatotic liver disease (MASLD) is one of the most common chronic liver diseases worldwide and is particularly common among individuals with type 2 diabetes. While many people remain stable over time, some develop progressive liver fibrosis, cirrhosis, hepatocellular carcinoma, and other serious liver-related complications. Reliable methods for identifying patients at greatest risk of disease progression remain limited. EPSOMIP2 is a prospective observational follow-up study of participants who were previously enrolled in the EPSONIP study (NCT03864510) between 2019 and 2023. In the original study, 317 individuals with type 2 diabetes from primary healthcare centers in Östergötland, Sweden, underwent detailed evaluation of liver disease, metabolic health, physical activity, and body composition. Participants who completed the original study are invited to undergo a new assessment approximately 6 years after their initial evaluation. The follow-up examination includes clinical assessment, blood and urine sampling, vibration-controlled transient elastography, magnetic resonance imaging-derived proton density fat fraction (MRI-PDFF), magnetic resonance elastography (MRE), body composition analysis, questionnaires, physical activity measurements, blood pressure assessments, and biobanking. By comparing the follow-up findings with data collected during the original EPSONIP study, researchers aim to characterize the long-term natural history of MASLD in type 2 diabetes. The study will evaluate prevalence, progression, and regression of hepatic steatosis and liver fibrosis, and investigate associations with physical activity, body composition, genetic variants, hypertension, obstructive sleep apnea, and other metabolic risk factors. The study will also investigate the occurrence of clinically significant liver-related outcomes, including decompensated liver disease, hepatocellular carcinoma, liver transplantation, and liver-r

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。

获取专属数据定制方案