Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:234-237. doi: 10.1109/EMBC48229.2022.9871543.
Traditionally, the diagnosis of schizophrenia was based on the psychiatrist's introspective diagnosis through clinical stratification factors and score-scales, which led to heterogeneity and discrepancy in the symptoms and results. However, there are many studies trying to improve and assist in how its diagnosis could be performed. To objectively classify schizophrenia patients it is required to determine quantitative biomarkers of the disease. In this contribution we propose a method based on feature extraction both in magnetic resonance (MR) and Positron Emission Tomography (PET) imaging. A dataset of 34 participants (17 patients and 17 control subjects) were analyzed and 5 different brain regions were studied (frontal cortex, posterior cingulate cortex, temporal cortex, primary auditory cortex and thalamus). Following a radiomics approach, 43 texture features were extracted using five different statistical methods. These features were used for the training of the five different predictive models (Linear SVM, Gaussian SVM, Bagged Tree, KNN and Naive Bayes). The precision results were obtained classifying schizophrenia both in MR images (89% Area Under the Curve (AUC) in the posterior cingulate cortex) and with PET images (82% AUC in the frontal cortex), being Linear SVM and Naive Bayes the classification models with the highest predictive power. Clinical Relevance- The current study establishes a methodology to classify schizophrenia disease based on quantitative biomarkers using MR and PET images. This tool could assist the psychiatrist as an additional criterion for the diagnosis evaluation.
传统上，精神分裂症的诊断基于精神科医生通过临床分层因素和评分量表进行的内省诊断，这导致症状和结果存在异质性和差异。然而，有许多研究试图改进和协助其诊断的执行。为了客观地对精神分裂症患者进行分类，需要确定疾病的定量生物标志物。在本研究中，我们提出了一种基于磁共振（MR）和正电子发射断层扫描（PET）成像的特征提取方法。对 34 名参与者（17 名患者和 17 名对照）进行了分析，并研究了 5 个不同的脑区（额叶、后扣带回皮质、颞叶、初级听觉皮层和丘脑）。采用放射组学方法，使用五种不同的统计方法提取了 43 个纹理特征。这些特征用于训练五种不同的预测模型（线性 SVM、高斯 SVM、袋装树、KNN 和朴素贝叶斯）。在对 MR 图像（后扣带回皮质的 AUC 为 89%）和 PET 图像（额叶的 AUC 为 82%）进行分类时，获得了准确率结果，线性 SVM 和朴素贝叶斯是具有最高预测能力的分类模型。临床相关性-本研究建立了一种基于 MR 和 PET 图像的定量生物标志物对精神分裂症进行分类的方法。该工具可以作为精神病医生诊断评估的附加标准。
