机器学习算法预测甲状腺乳头状癌中央区淋巴结转移的表1

数据集获取（Figshare）

• 源站标题：Table_1_Machine Learning Algorithms for the Prediction of Central Lymph Node Metastasis in Patients With Papillary Thyroid Cancer.docx
• DOI：10.3389/fendo.2020.577537.s001
• 发布日期：2020-10-21
• 文件数：1 个
• 文件总体积：17.83 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Machine_Learning_Algorithms_for_the_Prediction_of_Central_Lymph_Node_Metastasis_in_Patients_With_Papillary_Thyroid_Cancer_docx/13121612

命令行下载

curl -L -o "Table_1_Machine Learning Algorithms for the Prediction of Central Lymph Node Metastasis in Patients With Papillary Thyroid Cancer.docx" "https://ndownloader.figshare.com/files/25181693"

文件清单

• Table_1_Machine Learning Algorithms for the Prediction of Central Lymph Node Metastasis in Patients With Papillary Thyroid Cancer.docx（17.83 KB）

文件总体积 17.83 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

BackgroundCentral lymph node metastasis (CLNM) occurs frequently in patients with papillary thyroid cancer (PTC), but performing prophylactic central lymph node dissection is still controversial. There are no reliable models for predicting CLNM. This study aimed to develop predictive models for CLNM by machine learning (ML) algorithms.
MethodsPatients with PTC who underwent initial thyroid resection at our hospital between January 2018 and December 2019 were enrolled. A total of 22 variables, including clinical characteristics and ultrasonography (US) features, were used for conventional univariate and multivariate analysis and to construct ML-based models. A 5-fold cross validation strategy was used for validation and a feature selection approach was applied to identify risk factors.
ResultsThe areas under the receiver operating characteristic curve (AUC) of 7 models ranged from 0.680 to 0.731. All models performed significantly better than US (AUC=0.623) in predicting CLNM (P 1.1 cm were the most contributing predictors for CLNM.
ConclusionsIt is feasible to develop predictive models of CLNM in PTC patients by incorporating clinical characteristics and US features. The ML algorithm may be a useful tool for the prediction of lymph node metastasis in thyroid cancer.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fendo.2020.577537.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Machine_Learning_Algorithms_for_the_Prediction_of_Central_Lymph_Node_Metastasis_in_Patients_With_Papillary_Thyroid_Cancer_docx/13121612）。