改良MRC分级用于结核性脑膜炎

数据集获取（Figshare）

• 源站标题：Modified MRC Grading for TBM.
• DOI：10.1371/journal.pone.0321655.t001
• 发布日期：2025-05-23
• 文件数：1 个
• 文件总体积：5.63 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Modified_MRC_Grading_for_TBM_/29138832

命令行下载

curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/54784584"

文件清单

• Table 1.xls（5.63 KB）

文件总体积 5.63 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

Introduction
Tuberculous meningitis (TBM) leads to high mortality, especially amongst individuals with HIV. Predicting the incidence of disease-related complications is challenging, for which purpose the value of brain magnetic resonance imaging (MRI) has not been well investigated. We used a convolutional neural network (CNN) to explore the complementary contribution of brain MRI to the conventional prognostic determinants.
Methods
We pooled data from two randomised control trials of HIV-positive and HIV-negative adults with clinical TBM in Vietnam to predict the occurrence of death or new neurological complications in the first two months after the subject’s first MRI session. We developed and compared three models: a logistic regression with clinical, demographic and laboratory data as reference, a CNN that utilised only T1-weighted MRI volumes, and a model that fused all available information. All models were fine-tuned using two repetitions of 5-fold cross-validation. The final evaluation was based on a random 70/30 training/test split, stratified by the outcome and HIV status. Based on the selected model, we explored the interpretability maps derived from the models.
Results
215 patients were included, with an event prevalence of 22.3%. On the test set our non-imaging model had higher AUC (71.2% 1.1%) than the imaging-only model (67.3% 2.6%). The fused model was superior to both, with an average AUC = 77.3% 4.0% in the test set. The non-imaging variables were more informative in the HIV-positive group, while the imaging features were more predictive in the HIV-negative group. All three models performed better in the HIV-negative cohort. The interpretability maps show the model’s focus on the lateral fissures, the corpus callosum, the midbrain, and peri-ventricular tissues.
Conclusion
Imaging information can provide added value to predict unwanted outcomes of TBM. However, to confirm this finding, a larger dataset is needed.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0321655.t001（source: https://plos.figshare.com/articles/dataset/Modified_MRC_Grading_for_TBM_/29138832）。