癌症中心肺结节识别算法开发数据表

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre.PDF
• DOI：10.3389/fmed.2021.748168.s001
• 发布日期：2021-11-04
• 文件数：1 个
• 文件总体积：107.90 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Development_of_a_Structured_Query_Language_and_Natural_Language_Processing_Algorithm_to_Identify_Lung_Nodules_in_a_Cancer_Centre_PDF/16927429

命令行下载

curl -L -o "Data_Sheet_1_Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre.PDF" "https://ndownloader.figshare.com/files/31320511"

文件清单

• Data_Sheet_1_Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre.PDF（107.90 KB）

文件总体积 107.90 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

Importance: The stratification of indeterminate lung nodules is a growing problem, but the burden of lung nodules on healthcare services is not well-described. Manual service evaluation and research cohort curation can be time-consuming and potentially improved by automation.
Objective: To automate lung nodule identification in a tertiary cancer centre.
Methods: This retrospective cohort study used Electronic Healthcare Records to identify CT reports generated between 31st October 2011 and 24th July 2020. A structured query language/natural language processing tool was developed to classify reports according to lung nodule status. Performance was externally validated. Sentences were used to train machine-learning classifiers to predict concerning nodule features in 2,000 patients.
Results: 14,586 patients with lung nodules were identified. The cancer types most commonly associated with lung nodules were lung (39%), neuro-endocrine (38%), skin (35%), colorectal (33%) and sarcoma (33%). Lung nodule patients had a greater proportion of metastatic diagnoses (45 vs. 23%, p Conclusion: We have developed and validated an accurate tool for automated lung nodule identification that is valuable for service evaluation and research data acquisition.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2021.748168.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Development_of_a_Structured_Query_Language_and_Natural_Language_Processing_Algorithm_to_Identify_Lung_Nodules_in_a_Cancer_Centre_PDF/16927429）。