早期胃癌淋巴结转移预测模型数据表

数据集获取（Figshare）

• 源站标题：Table_1_Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer.XLSX
• DOI：10.3389/fmed.2022.986437.s005
• 发布日期：2022-10-03
• 文件数：1 个
• 文件总体积：15.26 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_a_predictive_model_combining_clinical_radiomics_and_deep_transfer_learning_features_for_lymph_node_metastasis_in_early_gastric_cancer_XLSX/21259896

命令行下载

curl -L -o "Table_1_Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis .XLSX" "https://ndownloader.figshare.com/files/37685007"

文件清单

• Table_1_Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis .XLSX（15.26 KB）

文件总体积 15.26 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

BackgroundThis study aims to develop and validate a predictive model combining deep transfer learning, radiomics, and clinical features for lymph node metastasis (LNM) in early gastric cancer (EGC).
Materials and methodsThis study retrospectively collected 555 patients with EGC, and randomly divided them into two cohorts with a ratio of 7:3 (training cohort, n = 388; internal validation cohort, n = 167). A total of 79 patients with EGC collected from the Second Affiliated Hospital of Soochow University were used as external validation cohort. Pre-trained deep learning networks were used to extract deep transfer learning (DTL) features, and radiomics features were extracted based on hand-crafted features. We employed the Spearman rank correlation test and least absolute shrinkage and selection operator regression for feature selection from the combined features of clinical, radiomics, and DTL features, and then, machine learning classification models including support vector machine, K-nearest neighbor, random decision forests (RF), and XGBoost were trained, and their performance by determining the area under the curve (AUC) were compared.
ResultsWe constructed eight pre-trained transfer learning networks and extracted DTL features, respectively. The results showed that 1,048 DTL features extracted based on the pre-trained Resnet152 network combined in the predictive model had the best performance in discriminating the LNM status of EGC, with an AUC of 0.901 (95% CI: 0.847–0.956) and 0.915 (95% CI: 0.850–0.981) in the internal validation and external validation cohorts, respectively.
ConclusionWe first utilized comprehensive multidimensional data based on deep transfer learning, radiomics, and clinical features with a good predictive ability for discriminating the LNM status in EGC, which could provide favorable information when choosing therapy options for individuals with EGC.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2022.986437.s005（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_a_predictive_model_combining_clinical_radiomics_and_deep_transfer_learning_features_for_lymph_node_metastasis_in_early_gastric_cancer_XLSX/21259896）。