外周肺腺癌淋巴结转移术前预测综合模型数据表

数据集获取（Figshare）

• 源站标题：DataSheet_1_Development and Validation of a Combined Model for Preoperative Prediction of Lymph Node Metastasis in Peripheral Lung Adenocarcinoma.docx
• DOI：10.3389/fonc.2021.675877.s001
• 发布日期：2021-05-24
• 文件数：1 个
• 文件总体积：861.96 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Development_and_Validation_of_a_Combined_Model_for_Preoperative_Prediction_of_Lymph_Node_Metastasis_in_Peripheral_Lung_Adenocarcinoma_docx/14659362

命令行下载

curl -L -o "DataSheet_1_Development and Validation of a Combined Model for Preoperative Prediction of Lymph Node Metastasis in Peripheral Lung Adenocarcinoma.docx" "https://ndownloader.figshare.com/files/28141464"

文件清单

• DataSheet_1_Development and Validation of a Combined Model for Preoperative Prediction of Lymph Node Metastasis in Peripheral Lung Adenocarcinoma.docx（861.96 KB）

文件总体积 861.96 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

BackgroundBased on the “seed and soil” theory proposed by previous studies, we aimed to develop and validate a combined model of machine learning for predicting lymph node metastasis (LNM) in patients with peripheral lung adenocarcinoma (PLADC).
MethodsRadiomics models were developed in a primary cohort of 390 patients (training cohort) with pathologically confirmed PLADC from January 2016 to August 2018. The patients were divided into the LNM (−) and LNM (+) groups. Thereafter, the patients were subdivided according to TNM stages N0, N1, N2, and N3. Radiomic features from unenhanced computed tomography (CT) were extracted. Radiomic signatures of the primary tumor (R1) and adjacent pleura (R2) were built as predictors of LNM. CT morphological features and clinical characteristics were compared between both groups. A combined model incorporating R1, R2, and CT morphological features, and clinical risk factors was developed by multivariate analysis. The combined model’s performance was assessed by receiver operating characteristic (ROC) curve. An internal validation cohort containing 166 consecutive patients from September 2018 to November 2019 was also assessed.
ResultsThirty-one radiomic features of R1 and R2 were significant predictors of LNM (all P CT phenotypes of the primary tumor and adjacent pleura were significantly associated with LNM. A combined model incorporating radiomic signatures, CT morphological features, and clinical risk factors can assess LNM of patients with PLADC accurately and non-invasively.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2021.675877.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Development_and_Validation_of_a_Combined_Model_for_Preoperative_Prediction_of_Lymph_Node_Metastasis_in_Peripheral_Lung_Adenocarcinoma_docx/14659362）。