Delta-影像组学特征分析中的机器学习方法研究

数据集获取（Figshare）

• 源站标题：An investigation of machine learning methods in delta-radiomics feature analysis
• DOI：10.1371/journal.pone.0226348
• 发布日期：2019-12-13
• 文件数：2 个
• 文件总体积：31.44 KB
• 源站页面：https://plos.figshare.com/articles/dataset/An_investigation_of_machine_learning_methods_in_delta-radiomics_feature_analysis/11366273

命令行下载

curl -L -o "pone.0226348.s001.docx" "https://ndownloader.figshare.com/files/20180969"
curl -L -o "pone.0226348.s002.docx" "https://ndownloader.figshare.com/files/20180972"

文件清单

• pone.0226348.s001.docx（13.09 KB）
• pone.0226348.s002.docx（18.35 KB）

文件总体积 31.44 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

Purpose
This study aimed to investigate the effectiveness of using delta-radiomics to predict overall survival (OS) for patients with recurrent malignant gliomas treated by concurrent stereotactic radiosurgery and bevacizumab, and to investigate the effectiveness of machine learning methods for delta-radiomics feature selection and building classification models.
Methods
The pre-treatment, one-week post-treatment, and two-month post-treatment T1 and T2 fluid-attenuated inversion recovery (FLAIR) MRI were acquired. 61 radiomic features (intensity histogram-based, morphological, and texture features) were extracted from the gross tumor volume in each image. Delta-radiomics were calculated between the pre-treatment and post-treatment features. Univariate Cox regression and 3 multivariate machine learning methods (L1-regularized logistic regression [L1-LR], random forest [RF] or neural networks [NN]) were used to select a reduced number of features, and 7 machine learning methods (L1-LR, L2-LR, RF, NN, kernel support vector machine [KSVM], linear support vector machine [LSVM], or naïve bayes [NB]) was used to build classification models for predicting OS. The performances of the total 21 model combinations built based on single-time-point radiomics (pre-treatment, one-week post-treatment, and two-month post-treatment) and delta-radiomics were evaluated by the area under the receiver operating characteristic curve (AUC).
Results
For a small cohort of 12 patients, delta-radiomics resulted in significantly higher AUC than pre-treatment radiomics (p-valueConclusions
The results indicated that delta-features could potentially provide better treatment assessment than single-time-point features. The treatment assessment is substantially affected by the time point for computing the delta-features and the combination of machine learning methods for feature selection and classification.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0226348（source: https://plos.figshare.com/articles/dataset/An_investigation_of_machine_learning_methods_in_delta-radiomics_feature_analysis/11366273）。