基于年龄、性别和超声特征区分良恶性甲状腺结节的预测模型

数据集获取（Figshare）

• 源站标题：A predictive model to distinguish malignant and benign thyroid nodules based on age, gender and ultrasonographic features
• DOI：10.6084/m9.figshare.7710674.v1
• 发布日期：2019-02-13
• 文件数：5 个
• 文件总体积：36.35 KB
• 源站页面：https://scielo.figshare.com/articles/dataset/A_predictive_model_to_distinguish_malignant_and_benign_thyroid_nodules_based_on_age_gender_and_ultrasonographic_features/7710674

命令行下载

curl -L -o "Table_1.xls" "https://ndownloader.figshare.com/files/14350823"
curl -L -o "Table_2.xls" "https://ndownloader.figshare.com/files/14350829"
curl -L -o "Table_3.xls" "https://ndownloader.figshare.com/files/14350838"
curl -L -o "Table_4.xls" "https://ndownloader.figshare.com/files/14350847"
curl -L -o "Table_5.xls" "https://ndownloader.figshare.com/files/14350853"

文件清单

• Table_1.xls（13.82 KB）
• Table_2.xls（5.63 KB）
• Table_3.xls（5.63 KB）
• Table_4.xls（5.63 KB）
• Table_5.xls（5.63 KB）

文件总体积 36.35 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

Abstract Introduction: A discussion in literature about a standardized decision support tool for the management of thyroid nodules remains. Objective: The purpose of this study was to create a statistical prediction model for thyroid nodules management. Methods: Two hundred and four benign and 57 malignant thyroid nodules were selected for a retrospective study. The variables age, gender and ultrasonographic features were examined using univariate and multivariate models. A statistical formula was used to calculate the risk of cancer of each case. Results: In multivariate analysis, irregular shape, absence of halo, lower mean age, homogeneous echotexture, microcalcifications and solid content were associated with cancer. After applying the formula, 20 cases (7.6%) with a calculated risk for malignancy ≤3.0% were found, all of them benign. Setting the calculated risk in ≥80%, 21 (8.0%) cases were selected, and in 85.7% of them cancer was confirmed in histopathology. Internal accuracy of the prediction formula was 92.5%. Conclusions: The prediction formula reached high accuracy and may be an alternative to other decision support tools for thyroid nodule management.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.7710674.v1（source: https://scielo.figshare.com/articles/dataset/A_predictive_model_to_distinguish_malignant_and_benign_thyroid_nodules_based_on_age_gender_and_ultrasonographic_features/7710674）。