甲状腺肿瘤良恶性鉴别机器学习数据集（Data_Sheet_1）

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Machine learning for identifying benign and malignant of thyroid tumors: A retrospective study of 2,423 patients.docx
• DOI：10.3389/fpubh.2022.960740.s001
• 发布日期：2022-09-14
• 文件数：1 个
• 文件总体积：142.65 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_learning_for_identifying_benign_and_malignant_of_thyroid_tumors_A_retrospective_study_of_2_423_patients_docx/21101395

命令行下载

curl -L -o "Data_Sheet_1_Machine learning for identifying benign and malignant of thyroid tumors: A retrospective study of 2,423 patients.docx" "https://ndownloader.figshare.com/files/37442173"

文件清单

• Data_Sheet_1_Machine learning for identifying benign and malignant of thyroid tumors: A retrospective study of 2,423 patients.docx（142.65 KB）

文件总体积 142.65 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

Thyroid tumors, one of the common tumors in the endocrine system, while the discrimination between benign and malignant thyroid tumors remains insufficient. The aim of this study is to construct a diagnostic model of benign and malignant thyroid tumors, in order to provide an emerging auxiliary diagnostic method for patients with thyroid tumors. The patients were selected from the Chongqing General Hospital (Chongqing, China) from July 2020 to September 2021. And peripheral blood, BRAFV600E gene, and demographic indicators were selected, including sex, age, BRAFV600E gene, lymphocyte count (Lymph#), neutrophil count (Neu#), neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), red blood cell distribution width (RDW), platelets count (PLT), red blood cell distribution width—coefficient of variation (RDW–CV), alkaline phosphatase (ALP), and parathyroid hormone (PTH). First, feature selection was executed by univariate analysis combined with least absolute shrinkage and selection operator (LASSO) analysis. Afterward, we used machine learning algorithms to establish three types of models. The first model contains all predictors, the second model contains indicators after feature selection, and the third model contains patient peripheral blood indicators. The four machine learning algorithms include extreme gradient boosting (XGBoost), random forest (RF), light gradient boosting machine (LightGBM), and adaptive boosting (AdaBoost) which were used to build predictive models. A grid search algorithm was used to find the optimal parameters of the machine learning algorithms. A series of indicators, such as the area under the curve (AUC), were intended to determine the model performance. A total of 2,042 patients met the criteria and were enrolled in this study, and 12 variables were included. Sex, age, Lymph#, PLR, RDW, and BRAFV600E were identified as statistically significant indicators by univariate and LASSO analysis. Among the model we constructed, RF, XGBoost, LightGBM and AdaBoost with the AUC of 0.874 (95% CI, 0.841–0.906), 0.868 (95% CI, 0.834–0.901), 0.861 (95% CI, 0.826–0.895), and 0.837 (95% CI, 0.802–0.873) in the first model. With the AUC of

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fpubh.2022.960740.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_learning_for_identifying_benign_and_malignant_of_thyroid_tumors_A_retrospective_study_of_2_423_patients_docx/21101395）。