表2_基于多模态数据整合机器学习的鼻咽癌患者螺旋断层放疗后急性放射性皮炎预测模型

数据集获取（Figshare）

• 源站标题：Table 2_Predictive modeling of acute radiation-induced dermatitis in nasopharyngeal carcinoma patients undergoing tomotherapy using machine learning with multimodal data integration.docx
• DOI：10.3389/fonc.2025.1601493.s002
• 发布日期：2025-10-02
• 文件数：1 个
• 文件总体积：19.69 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Predictive_modeling_of_acute_radiation-induced_dermatitis_in_nasopharyngeal_carcinoma_patients_undergoing_tomotherapy_using_machine_learning_with_multimodal_data_integration_docx/30262363

命令行下载

curl -L -o "Table 2_Predictive modeling of acute radiation-induced dermatitis in nasopharyngeal carcinoma patients undergoing tomotherapy using machine learning w.docx" "https://ndownloader.figshare.com/files/58441660"

文件清单

• Table 2_Predictive modeling of acute radiation-induced dermatitis in nasopharyngeal carcinoma patients undergoing tomotherapy using machine learning w.docx（19.69 KB）

文件总体积 19.69 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

PurposeRadiation dermatitis (RD) is a common and debilitating side effect of radiotherapy in nasopharyngeal carcinoma (NPC) patients. Traditional predictive models lack sufficient accuracy for assessing acute radiation dermatitis (ARD) after tomotherapy treatment. This study aims to integrate clinical, dosimetric, and radiomic features to enhance the accuracy and robustness of predictions, thereby promoting a more personalized risk assessment for NPC patients undergoing tomotherapy.
MethodsA cohort of 161 NPC patients who underwent Tomotherapy was retrospectively analyzed. Clinical, dosimetric, and radiomic features were extracted for the purpose of model development. Feature selection was conducted using statistical tests and Least Absolute Shrinkage and Selection Operator(LASSO) regression. Several machine learning algorithms were then employed to construct the predictive models, including Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, Extra Trees, XGBoost, Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP). These models were built based on clinical, radiomic, dosiomic, and combined feature sets. Model performance was assessed by evaluating the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To ensure fairness in comparisons, five-fold cross-validation was applied during the training of all models in the training cohort.
ResultsThe combined model, integrating clinical, radiomic, and dosiomic features, demonstrated the highest predictive accuracy, achieving an AUC of 0.916 (95% CI: 0.866–0.967) in the training cohort and 0.797 (95% CI: 0.616–0.978) in the validation cohort. In comparison, the clinical model (AUC=0.704), radiomic model (AUC=0.865), and dosiomic model (AUC=0.640) had lower predictive performance. SVM method demonstrated superior overall performance across various model constructions. The combined model based on the SVM method exhibited optimal predictive performance, achieving the best results in both the test and validation cohorts.
ConclusionsThe developed combined prediction system achieves superior performance in anticipating sev

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2025.1601493.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Predictive_modeling_of_acute_radiation-induced_dermatitis_in_nasopharyngeal_carcinoma_patients_undergoing_tomotherapy_using_machine_learning_with_multimodal_data_integration_docx/30262363）。