基于CT影像组学的急性缺血性脑卒中出血性转化预测数据表

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Radiomics-based infarct features on CT predict hemorrhagic transformation in patients with acute ischemic stroke.CSV
• DOI：10.3389/fnins.2022.1002717.s001
• 发布日期：2022-09-21
• 文件数：1 个
• 文件总体积：398.07 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Radiomics-based_infarct_features_on_CT_predict_hemorrhagic_transformation_in_patients_with_acute_ischemic_stroke_CSV/21175777

命令行下载

curl -L -o "Data_Sheet_1_Radiomics-based infarct features on CT predict hemorrhagic transformation in patients with acute ischemic stroke.CSV" "https://ndownloader.figshare.com/files/37549168"

文件清单

• Data_Sheet_1_Radiomics-based infarct features on CT predict hemorrhagic transformation in patients with acute ischemic stroke.CSV（398.07 KB）

文件总体积 398.07 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

ObjectiveTo develop and validate a model based on the radiomics features of the infarct areas on non-contrast-enhanced CT to predict hemorrhagic transformation (HT) in acute ischemic stroke.
Materials and methodsA total of 118 patients diagnosed with acute ischemic stroke in two centers from January 2019 to February 2022 were included. The radiomics features of infarcted areas on non-contrast-enhanced CT were extracted using 3D-Slicer. A univariate analysis and the least absolute shrinkage and selection operator (LASSO) were used to select features, and the radiomics score (Rad-score) was then constructed. The predictive model of HT was constructed by analyzing the Rad-score and clinical and imaging features in the training cohort, and it was verified in the validation cohort. The model was evaluated with the receiver operating characteristic curve, calibration curve and decision curve, and the prediction performance of the model in different scenarios was further discussed hierarchically.
ResultsOf the 118 patients, 52 developed HT, including 21 cases of hemorrhagic infarct (HI) and 31 cases of parenchymal hematoma (PH). The Rad-score was constructed from five radiomics features and was the only independent predictor for HT. The predictive model was constructed from the Rad-score. The area under the curve (AUCs) of the model for predicting HT in the training and validation cohorts were 0.845 and 0.750, respectively. Calibration curve and decision curve analyses showed that the model performed well. Further analysis found that the model predicted HT for different infarct sizes or treatment methods in the training and validation cohorts with 78.3 and 71.4% accuracy, respectively. For all samples, the model predicted an AUC of 0.754 for HT in patients within 4.5 h since stroke onset, and predicted an AUC of 0.648 for PH.
ConclusionThis model, which was based on CT radiomics features, could help to predict HT in the setting of acute ischemic stroke for any infarct size and provide guiding suggestions for clinical treatment and prognosis evaluation.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fnins.2022.1002717.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Radiomics-based_infarct_features_on_CT_predict_hemorrhagic_transformation_in_patients_with_acute_ischemic_stroke_CSV/21175777）。