高风险甲状腺结节预测列线图数据表9

数据集获取（Figshare）

• 源站标题：Data Sheet 9_Development and validation of a predictive nomogram for high-risk thyroid nodules: a retrospective analysis of sedentary time, insomnia, and elevated weight.pdf
• DOI：10.3389/fonc.2026.1698466.s005
• 发布日期：2026-04-01
• 文件数：1 个
• 文件总体积：67.92 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_9_Development_and_validation_of_a_predictive_nomogram_for_high-risk_thyroid_nodules_a_retrospective_analysis_of_sedentary_time_insomnia_and_elevated_weight_pdf/31914714

命令行下载

curl -L -o "Data Sheet 9_Development and validation of a predictive nomogram for high-risk thyroid nodules: a retrospective analysis of sedentary time, insomnia, .pdf" "https://ndownloader.figshare.com/files/63370374"

文件清单

• Data Sheet 9_Development and validation of a predictive nomogram for high-risk thyroid nodules: a retrospective analysis of sedentary time, insomnia, .pdf（67.92 KB）

文件总体积 67.92 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

BackgroundThyroid nodules are widely regarded as one of the most prevalent endocrine disorders, and high-risk thyroid nodules are gradually gaining attention due to their potential malignancy. Early detection and active intervention are key to improving prognosis. Therefore, establishing a predictive model for assessing the risk of high-risk thyroid nodules is crucial for adjunctive diagnosis.
MethodsThe clinical data of patients with thyroid nodules admitted to the Hospital of Chengdu University of Traditional Chinese Medicine from October 2023 to June 2024 were retrospectively analyzed. According to the Thyroid Imaging Reporting and Data System classification, the patients were divided into a low-to-moderate risk group and a high-risk group. Multivariate logistic regression analysis was used to explore the influencing factors of high-risk thyroid nodules, and a nomogram was constructed. Internal validation was conducted using bootstrap resampling methods. The predictive performance of the model was evaluated by comparing the area under the receiver operating characteristic curve, the calibration curve, and the decision curve.
ResultsA total of 164 patients with thyroid nodules were included in this study, with an average age of 42.31 years. Among them, 101 patients (61.59%) were diagnosed as high-risk for thyroid nodules. Dietary diversity score (OR: 0.773, 95% CI = 0.639-0.934) and nodule diameter (OR: 0.909, 95% CI = 0.871-0.95) were protective factors for high-risk thyroid nodules, while sedentary time of more than two hours per day (OR: 2.8, 95% CI = 1.276-6.148), the Athens Insomnia Scale score (OR: 1.078, 95% CI = 1.01-1.15), and elevated weight (OR: 1.049, 95% CI = 1.008-1.09) were independent risk factors (all PThe nomogram model we constructed shows good predictive performance for high-risk thyroid nodules after internal validation, and may serve as a practical tool to guide the formulation of disease prevention strategies.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2026.1698466.s005（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_9_Development_and_validation_of_a_predictive_nomogram_for_high-risk_thyroid_nodules_a_retrospective_analysis_of_sedentary_time_insomnia_and_elevated_weight_pdf/31914714）。