Distinguishing Focal Cortical Dysplasia From Glioneuronal Tumors in Patients with Epilepsy by Machine Learning

This dataset covers 96 patients. Ten features, i.e., Medication, Past History, Family history, Seizure frequency, Seizure type, FCD typical electrical discharge, Gender, Age at seizure onset, Course of disease, Typical image characteristics, Consistence of MRI and pathology, Surgical site are introduced to describe a patient. Our goal is to build a supervised machine learning-based classifier, in order to preoperatively distinguish focal cortical dysplasia (FCD) from glioneuronal tumors (GNTs) in patients with epilepsy.

本数据集共纳入96例患者，通过以下特征对患者进行描述：用药史（Medication）、既往病史（Past History）、家族病史（Family History）、癫痫发作频率（Seizure Frequency）、癫痫发作类型（Seizure Type）、局灶性皮层发育不良（Focal Cortical Dysplasia, FCD）典型放电、性别（Gender）、癫痫起病年龄（Age at Seizure Onset）、病程（Course of Disease）、典型影像学特征（Typical Image Characteristics）、MRI与病理结果一致性（Consistence of MRI and Pathology）以及手术部位（Surgical Site）。本研究旨在构建基于监督式机器学习的分类器，以在癫痫患者术前实现局灶性皮层发育不良与神经节胶质瘤（Glioneuronal Tumors, GNTs）的鉴别诊断。