数据表1：颞叶致痫区定位的机器学习——量化多模态临床-症状学与影像一致性的价值

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Machine Learning for Localizing Epileptogenic-Zone in the Temporal Lobe: Quantifying the Value of Multimodal Clinical-Semiology and Imaging Concordance.docx
• DOI：10.3389/fdgth.2021.559103.s001
• 发布日期：2021-02-10
• 文件数：1 个
• 文件总体积：3.04 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_Learning_for_Localizing_Epileptogenic-Zone_in_the_Temporal_Lobe_Quantifying_the_Value_of_Multimodal_Clinical-Semiology_and_Imaging_Concordance_docx/13846340

命令行下载

curl -L -o "Data_Sheet_1_Machine Learning for Localizing Epileptogenic-Zone in the Temporal Lobe: Quantifying the Value of Multimodal Clinical-Semiology and Imagi.docx" "https://ndownloader.figshare.com/files/26420207"

文件清单

• Data_Sheet_1_Machine Learning for Localizing Epileptogenic-Zone in the Temporal Lobe: Quantifying the Value of Multimodal Clinical-Semiology and Imagi.docx（3.04 MB）

文件总体积 3.04 MB（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

Background: Epilepsy affects 50 million people worldwide and a third are refractory to medication. If a discrete cerebral focus or network can be identified, neurosurgical resection can be curative. Most excisions are in the temporal-lobe, and are more likely to result in seizure-freedom than extra-temporal resections. However, less than half of patients undergoing surgery become entirely seizure-free. Localizing the epileptogenic-zone and individualized outcome predictions are difficult, requiring detailed evaluations at specialist centers.
Methods: We used bespoke natural language processing to text-mine 3,800 electronic health records, from 309 epilepsy surgery patients, evaluated over a decade, of whom 126 remained entirely seizure-free. We investigated the diagnostic performances of machine learning models using set-of-semiology (SoS) with and without hippocampal sclerosis (HS) on MRI as features, using STARD criteria.
Findings: Support Vector Classifiers (SVC) and Gradient Boosted (GB) decision trees were the best performing algorithms for temporal-lobe epileptogenic zone localization (cross-validated Matthews correlation coefficient (MCC) SVC 0.73 ± 0.25, balanced accuracy 0.81 ± 0.14, AUC 0.95 ± 0.05). Models that only used seizure semiology were not always better than internal benchmarks. The combination of multimodal features, however, enhanced performance metrics including MCC and normalized mutual information (NMI) compared to either alone (p Interpretation: Machine learning models using only the set of seizure semiology (SoS) cannot unequivocally perform better than benchmarks in temporal epileptogenic-zone localization. However, the combination of SoS with an imaging feature (HS) enhance epileptogenic lobe localization. We quantified this added NMI value to be 25% in absolute terms. Despite good performance in localization, no model was able to predict seizure-freedom better than benchmarks. The methods used are widely applicable, and the performance enhancements by combining other clinical, imaging and neurophysiological features could be similarly quantified. Multicenter studies are required to confirm generalizability.
Funding: Wellcome/EPSRC Ce

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fdgth.2021.559103.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_Learning_for_Localizing_Epileptogenic-Zone_in_the_Temporal_Lobe_Quantifying_the_Value_of_Multimodal_Clinical-Semiology_and_Imaging_Concordance_docx/13846340）。