数据集:多学科方法提高CT引导肺活检诊断材料体积的效果

该数据集来自 Dryad,对应一项关于多学科策略提高 CT 引导经胸针芯肺活检组织体积的研究。研究采用三方面策略:在确认已取得可诊断组织且对患者安全时增加活检穿刺次数以增加材料体积;将活检材料分装多个 cassette 处理;组织学实验室切片时保留所有组织带

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医学影像回归Lung cancertissue biopsy volumeCT-guided lung biopsytumor volumelung biopsybiopsy adequacymultidisciplinary strategy
Data from: Effects of a multidisciplinary approach to improve volume of diagnostic material in CT-guided lung biopsies

基本信息

模态
医学影像
大小
0.00016896 GB
任务类型
回归
许可
Creative Commons Zero v1.0 Universal
创建/更新时间
2026-10-12

资源简介

数据集:多学科方法提高CT引导肺活检诊断材料体积的效果

Data from: Effects of a multidisciplinary approach to improve volume of diagnostic material in CT-guided lung biopsies

该数据集来自 Dryad,对应一项关于多学科策略提高 CT 引导经胸针芯肺活检组织体积的研究。研究采用三方面策略:在确认已取得可诊断组织且对患者安全时增加活检穿刺次数以增加材料体积;将活检材料分装多个 cassette 处理;组织学实验室切片时保留所有组织带。本研究量化了前两项策略的效果,为回顾性分析,比较 2007 年及之后的 CT 引导肺活检。数据可用于探讨活检充分性、组织体积与肺癌诊断及后续检测的关系(来源 DOI 解析,Creative Commons Zero v1.0 Universal 许可)。

【数据集背景】

源站首次发布:2015。

【数据内容】

源站描述(机器翻译,仅供参考):背景:近期发表的文章强调了多学科策略对于最大限度保存和利用肺活检材料进行高级检测的重要性,这可能决定治疗方案。本文量化了为优化和增加 CT 引导经胸针芯肺活检组织体积而实施的多学科策略的效果。该策略分为三个方面:(1)一旦确信已获得可诊断组织且对患者安全,则进行额外的活检穿刺以进一步增加活检材料体积;(2)将活检材料放入多个 cassette 中进行处理;(3)组织学实验室切割蜡块时保留所有组织带。本研究量化了策略 #1 和 #2 的效果。设计:这项回顾性分析比较了 2007 年和 20

【主题与分类】

主题标签:Lung cancer、tissue biopsy volume、CT-guided lung biopsy、tumor volume、lung biopsy、biopsy adequacy、multidisciplinary strategy。本站分类:肿瘤。

【获取与许可】

源站页面:https://datadryad.org/dataset/doi:10.5061/dryad.6bt21

使用许可:Creative Commons Zero v1.0 Universal。

官方服务

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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。

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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://doi.org/10.5061/dryad.6bt21

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。

数据集大小约 169.0 KB。

数据说明

Background: Recent publications have emphasized the importance of a multidisciplinary strategy for maximum conservation and utilization of lung biopsy material for advanced testing, which may determine therapy. This paper quantifies the effect of a multidisciplinary strategy implemented to optimize and increase tissue volume in CT-guided transthoracic needle core lung biopsies. The strategy was three-pronged: (1) once there was confidence diagnostic tissue had been obtained and if safe for the patient, additional biopsy passes were performed to further increase volume of biopsy material, (2) biopsy material was placed in multiple cassettes for processing, and (3) all tissue ribbons were conserved when cutting blocks in the histology laboratory. This study quantifies the effects of strategies #1 and #2. Design: This retrospective analysis comparing CT-guided lung biopsies from 2007 and 2012 (before and after multidisciplinary approach implementation) was performed at a single institution. Patient medical records were reviewed and main variables analyzed include biopsy sample size, radiologist, number of blocks submitted, diagnosis, and complications. The biopsy sample size measured was considered to be directly proportional to tissue volume in the block. Results: Biopsy sample size increased 2.5 fold with the average total biopsy sample size increasing from 1.0 cm (0.9–1.1 cm) in 2007 to 2.5 cm (2.3–2.8 cm) in 2012 (P<0.0001). The improvement was statistically significant for each individual radiologist. During the same time, the rate of pneumothorax requiring chest tube placement decreased from 15% to 7% (P = 0.065). No other major complications were identified. The proportion of tumor within the biopsy material was similar at 28% (23%–33%) and 35% (30%–40%) for 2007 and 2012, respectively. The number of cases with at least two blocks available for testing increased from 10.7% to 96.4% (P<0.0001). Conclusions: The effect of this multidisciplinary strategy to CT-guid

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。

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