多模态数据深度学习预测肺癌放疗联合免疫治疗引起的症状性肺炎表格4

数据集获取（Figshare）

• 源站标题：Table 4_Multimodal data deep learning method for predicting symptomatic pneumonitis caused by lung cancer radiotherapy combined with immunotherapy.xlsx
• DOI：10.3389/fimmu.2024.1492399.s004
• 发布日期：2025-01-08
• 文件数：1 个
• 文件总体积：33.95 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_4_Multimodal_data_deep_learning_method_for_predicting_symptomatic_pneumonitis_caused_by_lung_cancer_radiotherapy_combined_with_immunotherapy_xlsx/28159082

命令行下载

curl -L -o "Table 4_Multimodal data deep learning method for predicting symptomatic pneumonitis caused by lung cancer radiotherapy combined with immunotherapy.xlsx" "https://ndownloader.figshare.com/files/51536636"

文件清单

• Table 4_Multimodal data deep learning method for predicting symptomatic pneumonitis caused by lung cancer radiotherapy combined with immunotherapy.xlsx（33.95 KB）

文件总体积 33.95 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

ObjectivesThe pairing of immunotherapy and radiotherapy in the treatment of locally advanced nonsmall cell lung cancer (NSCLC) has shown promise. By combining radiotherapy with immunotherapy, the synergistic effects of these modalities not only bolster antitumor efficacy but also exacerbate lung injury. Consequently, developing a model capable of accurately predicting radiotherapy- and immunotherapy-related pneumonitis in lung cancer patients is a pressing need. Depth image features extracted from deep learning, combined with radiomics and clinical characteristics, were used to create a deep learning model. This model was developed to forecast symptomatic pneumonitis (SP) (≥Grade 2) in lung cancer patients undergoing thoracic radiotherapy in combination with immunotherapy.
MethodsThe prediction was based on CT scans taken prior to the start of thoracic radiotherapy. Retrospective collection of clinical data was conducted on 261 lung cancer patients undergoing a combination of thoracic radiotherapy and immunotherapy from January 2018 to May 2023. Imaging data in the form of pre-RT-CT scans were obtained for all individuals included in the study. The region of interest (ROI) in the lung parenchyma was outlined separately from the tumor volume, and standard radiomic features were obtained through the use of 3D Slicer software. In addition, the images were cropped to a uniform size of 224x224 pixels. Data augmentation techniques, including random horizontal flipping, were employed. The normalized image data was then input into a pre-trained deep residual network, ResNet34, which utilized convolutional layers and global average pooling layers for deep feature extraction. A five-fold cross-validation approach was implemented to construct the model, automatically splitting the dataset into training and validation sets at an 8:2 ratio. This process was repeated five times, and the results from these iterations were aggregated to compute the average values of performance metrics, thereby assessing the overall performance and stability of the model.
ResultsThe multimodal fusion model developed in this research, which incorporated depth image characteristics, radiomics pro

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fimmu.2024.1492399.s004（source: https://frontiersin.figshare.com/articles/dataset/Table_4_Multimodal_data_deep_learning_method_for_predicting_symptomatic_pneumonitis_caused_by_lung_cancer_radiotherapy_combined_with_immunotherapy_xlsx/28159082）。