融合CT深度学习、CT放射组学和外周血免疫特征诊断有症状肺癌患者的数据集
融合CT影像特征与血液免疫指标,用于有症状患者肺癌诊断的多模态数据集。

基本信息
资源简介
融合CT深度学习、CT放射组学和外周血免疫特征诊断有症状肺癌患者的数据集
【数据集背景】
本数据集源站标题为《Fusing Data from CT Deep Learning, CT Radiomics and Peripheral Blood Immune profiles to Diagnose Lung Cancer in Symptomatic Patients》。项目资助方:CRUK grant [C1519/A27375]。源站首次发布:2026-02-10。
【数据内容】
源站原始描述:Background: Lung cancer is the leading cause of cancer-related deaths. Diagnosis at late stages is common due to the largely non-specific nature of presenting symptoms contributing to high mortality. There is a lack of specific, minimally invasive low-cost tests to screen patients ahead of the diagnostic biopsy. Patients and Methods: 344 symptomatic patients from the lung clinic of Lister hospital suspected of lung cancer were recruited. Predictive covariates were successfully generated on 170 p…
【数据结构与技术规格】
数据集 DOI:10.18742/28070390.v1。
【主题与分类】
源站学科分类:Cancer diagnosis、Liquid biopsies。主题标签:Lung Cancer、Early Diagnosis、CT scan measures、Immune system profiling。
【适用方向】
多模态数据适合跨模态对齐、融合建模与联合表征学习方向的研究。
【获取与许可】
源站页面:https://kcl.figshare.com/articles/dataset/Fusing_Data_from_CT_Deep_Learning_CT_Radiomics_and_Peripheral_Blood_Immune_profiles_to_Diagnose_Lung_Cancer_in_Symptomatic_Patients/28070390
使用许可:King''s Data Access Agreement(https://www.kcl.ac.uk/researchsupport/assets/DataAccessAgreement-Description.pdf)。
规范引用:Barber, Paul; Mustapha, Rami; Ganeshan, Balaji; Ellis, Sam; Dolcetti, Luigi; Schnabel, Julia A.; et al. (2026). Fusing Data from CT Deep Learning, CT Radiomics and Peripheral Blood Immune profiles to Diagnose Lung Cancer in Symptomatic Patients. King''s College London. 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:Fusing Data from CT Deep Learning, CT Radiomics and Peripheral Blood Immune profiles to Diagnose Lung Cancer in Symptomatic Patients
- DOI:10.18742/28070390.v1
- 发布日期:2026-02-10
- 源站页面:https://kcl.figshare.com/articles/dataset/Fusing_Data_from_CT_Deep_Learning_CT_Radiomics_and_Peripheral_Blood_Immune_profiles_to_Diagnose_Lung_Cancer_in_Symptomatic_Patients/28070390
按页面 Download 按钮获取。
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: Lung cancer is the leading cause of cancer-related deaths. Diagnosis at late stages is common due to the largely non-specific nature of presenting symptoms contributing to high mortality. There is a lack of specific, minimally invasive low-cost tests to screen patients ahead of the diagnostic biopsy.
Patients and Methods: 344 symptomatic patients from the lung clinic of Lister hospital suspected of lung cancer were recruited. Predictive covariates were successfully generated on 170 patients from Computed Tomography (CT) scans using CT Texture Analysis (CTTA) and Deep Learning Autoencoders (DLA) as well as from peripheral blood data for immunity using high depth flow-cytometry and for exosome protein components.
Study: “Improving the Early Detection of Lung Cancer by Combining Exosomal Analysis of Hypoxia with Standard of Care Imaging (LungExoDETECT)” (https://clinicaltrials.gov/ct2/show/NCT04629079)
All procedures were performed in compliance with relevant laws and institutional guidelines and have been approved by the UK NHS HRA, and informed consent obtained from the subjects (REC reference: 19/EE/0357 20th Feb 2020).
许可
King’'s Data Access Agreement
引用
学术使用请引用 DOI 10.18742/28070390.v1(source: https://kcl.figshare.com/articles/dataset/Fusing_Data_from_CT_Deep_Learning_CT_Radiomics_and_Peripheral_Blood_Immune_profiles_to_Diagnose_Lung_Cancer_in_Symptomatic_Patients/28070390)。
数据缺失?
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⚡️ 需要数据支持或标注服务?立即联系我们获取专业方案。




