建立预测早期胃癌内镜黏膜下剥离术后淋巴结转移列线图数据集

数据集获取（Figshare）

• 源站标题：DataSheet_1_Establishment of a nomogram for predicting lymph node metastasis in patients with early gastric cancer after endoscopic submucosal dissection.pdf
• DOI：10.3389/fonc.2022.898640.s001
• 发布日期：2022-10-28
• 文件数：1 个
• 文件总体积：130.83 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Establishment_of_a_nomogram_for_predicting_lymph_node_metastasis_in_patients_with_early_gastric_cancer_after_endoscopic_submucosal_dissection_pdf/21426765

命令行下载

curl -L -o "DataSheet_1_Establishment of a nomogram for predicting lymph node metastasis in patients with early gastric cancer after endoscopic submucosal dissect.pdf" "https://ndownloader.figshare.com/files/38018559"

文件清单

• DataSheet_1_Establishment of a nomogram for predicting lymph node metastasis in patients with early gastric cancer after endoscopic submucosal dissect.pdf（130.83 KB）

文件总体积 130.83 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

BackgroundEndoscopic submucosal dissection (ESD) has been accepted as the standard treatment for the appropriate indication of early gastric cancer (EGC). Determining the risk of lymph node metastasis (LNM) is critical for the following treatment selection after ESD. This study aimed to develop a predictive model to quantify the probability of LNM in EGC to help minimize the invasive procedures.
MethodsA total of 952 patients with EGC who underwent radical gastrectomy were retrospectively reviewed. LASSO regression was used to help screen the potential risk factors. Multivariate logistic regression was used to establish a predictive nomogram, which was subjected to discrimination and calibration evaluation, bootstrapping internal validation, and decision curve analysis.
ResultsResults of multivariate analyses revealed that gender, fecal occult blood test, CEA, CA19-9, histologic differentiation grade, lymphovascular invasion, depth of infiltration, and Ki67 labeling index were independent prognostic factors for LNM. The nomogram had good discriminatory performance, with a concordance index of 0.816 (95% CI 0.781–0.853). The validation dataset yielded a corrected concordance index of 0.805 (95% CI 0.770–0.842). High agreements between ideal curves and calibration curves were observed.
ConclusionsThe nomogram is clinically useful for predicting LNM after ESD in EGC, which is beneficial to identifying patients who are at low risk for LNM and would benefit from avoiding an unnecessary gastrectomy.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2022.898640.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Establishment_of_a_nomogram_for_predicting_lymph_node_metastasis_in_patients_with_early_gastric_cancer_after_endoscopic_submucosal_dissection_pdf/21426765）。