Table 1_基于机器学习的放射组学方法使用Q-Dixon MRI区分髌股骨关节炎与非髌股骨关节炎.docx

数据集获取（Figshare）

• 源站标题：Table 1_A machine learning-based radiomics approach for differentiating patellofemoral osteoarthritis from non-patellofemoral osteoarthritis using Q-Dixon MRI.docx
• DOI：10.3389/fspor.2025.1535519.s001
• 发布日期：2025-01-17
• 文件数：1 个
• 文件总体积：19.36 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_A_machine_learning-based_radiomics_approach_for_differentiating_patellofemoral_osteoarthritis_from_non-patellofemoral_osteoarthritis_using_Q-Dixon_MRI_docx/28226960

命令行下载

curl -L -o "Table 1_A machine learning-based radiomics approach for differentiating patellofemoral osteoarthritis from non-patellofemoral osteoarthritis using Q-D.docx" "https://ndownloader.figshare.com/files/51730958"

文件清单

• Table 1_A machine learning-based radiomics approach for differentiating patellofemoral osteoarthritis from non-patellofemoral osteoarthritis using Q-D.docx（19.36 KB）

文件总体积 19.36 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

This prospective diagnostic study aimed to assess the utility of machine learning-based quadriceps fat pad (QFP) radiomics in distinguishing patellofemoral osteoarthritis (PFOA) from non-PFOA using Q-Dixon MRI in patients presenting with anterior knee pain. This diagnostic accuracy study retrospectively analyzed data from 215 patients (mean age: 54.2 ± 11.3 years; 113 women). Three predictive models were evaluated: a proton density-weighted image model, a fat fraction model, and a merged model. Feature selection was conducted using analysis of variance, and logistic regression was applied for classification. Data were collected from training, internal, and external test cohorts. Radiomics features were extracted from Q-Dixon MRI sequences to distinguish PFOA from non-PFOA. The diagnostic performance of the three models was compared using the area under the curve (AUC) values analyzed with the Delong test. In the training set (109 patients) and internal test set (73 patients), the merged model exhibited optimal performance, with AUCs of 0.836 [95% confidence interval (CI): 0.762–0.910] and 0.826 (95% CI: 0.722–0.929), respectively. In the external test set (33 patients), the model achieved an AUC of 0.885 (95% CI: 0.768–1.000), with sensitivity and specificity values of 0.833 and 0.933, respectively (p < 0.001). Fat fraction features exhibited a stronger predictive value than shape-related features. Machine learning-based QFP radiomics using Q-Dixon MRI accurately distinguishes PFOA from non-PFOA, providing a non-invasive diagnostic approach for patients with anterior knee pain.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fspor.2025.1535519.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_A_machine_learning-based_radiomics_approach_for_differentiating_patellofemoral_osteoarthritis_from_non-patellofemoral_osteoarthritis_using_Q-Dixon_MRI_docx/28226960）。