Multiparametric Characterization of Focal Cortical Dysplasia Using 3D MR Fingerprinting

IF 8.1 1区医学 Q1 CLINICAL NEUROLOGY Annals of Neurology Pub Date : 2024-08-03 DOI:10.1002/ana.27049

Ting-Yu Su, Joon Yul Choi, Siyuan Hu, Xiaofeng Wang, Ingmar Blümcke, Katherine Chiprean, Balu Krishnan, Zheng Ding, Ken Sakaie, Hiroatsu Murakami, Andreas V Alexopoulos, Imad Najm, Stephen E Jones, Dan Ma, Zhong Irene Wang

{"title":"Multiparametric Characterization of Focal Cortical Dysplasia Using 3D MR Fingerprinting","authors":"Ting-Yu Su, Joon Yul Choi, Siyuan Hu, Xiaofeng Wang, Ingmar Blümcke, Katherine Chiprean, Balu Krishnan, Zheng Ding, Ken Sakaie, Hiroatsu Murakami, Andreas V Alexopoulos, Imad Najm, Stephen E Jones, Dan Ma, Zhong Irene Wang","doi":"10.1002/ana.27049","DOIUrl":null,"url":null,"abstract":"<div>\n \n <section>\n \n <h3> Objective</h3>\n \n <p>To develop a multiparametric machine-learning (ML) framework using high-resolution 3 dimensional (3D) magnetic resonance (MR) fingerprinting (MRF) data for quantitative characterization of focal cortical dysplasia (FCD).</p>\n </section>\n \n <section>\n \n <h3> Materials</h3>\n \n <p>We included 119 subjects, 33 patients with focal epilepsy and histopathologically confirmed FCD, 60 age- and gender-matched healthy controls (HCs), and 26 disease controls (DCs). Subjects underwent whole-brain 3 Tesla MRF acquisition, the reconstruction of which generated T1 and T2 relaxometry maps. A 3D region of interest was manually created for each lesion, and z-score normalization using HC data was performed. We conducted 2D classification with ensemble models using MRF T1 and T2 mean and standard deviation from gray matter and white matter for FCD versus controls. Subtype classification additionally incorporated entropy and uniformity of MRF metrics, as well as morphometric features from the morphometric analysis program (MAP). We translated 2D results to individual probabilities using the percentage of slices above an adaptive threshold. These probabilities and clinical variables were input into a support vector machine for individual-level classification. Fivefold cross-validation was performed and performance metrics were reported using receiver-operating-characteristic-curve analyses.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>FCD versus HC classification yielded mean sensitivity, specificity, and accuracy of 0.945, 0.980, and 0.962, respectively; FCD versus DC classification achieved 0.918, 0.965, and 0.939. In comparison, visual review of the clinical magnetic resonance imaging (MRI) detected 48% (16/33) of the lesions by official radiology report. In the subgroup where both clinical MRI and MAP were negative, the MRF-ML models correctly distinguished FCD patients from HCs and DCs in 98.3% of cross-validation trials. Type II versus non-type-II classification exhibited mean sensitivity, specificity, and accuracy of 0.835, 0.823, and 0.83, respectively; type IIa versus IIb classification showed 0.85, 0.9, and 0.87. In comparison, the transmantle sign was present in 58% (7/12) of the IIb cases.</p>\n </section>\n \n <section>\n \n <h3> Interpretation</h3>\n \n <p>The MRF-ML framework presented in this study demonstrated strong efficacy in noninvasively classifying FCD from normal cortex and distinguishing FCD subtypes. ANN NEUROL 2024;96:944–957</p>\n </section>\n </div>","PeriodicalId":127,"journal":{"name":"Annals of Neurology","volume":"96 5","pages":"944-957"},"PeriodicalIF":8.1000,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11496021/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Neurology","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ana.27049","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Objective

To develop a multiparametric machine-learning (ML) framework using high-resolution 3 dimensional (3D) magnetic resonance (MR) fingerprinting (MRF) data for quantitative characterization of focal cortical dysplasia (FCD).

Materials

We included 119 subjects, 33 patients with focal epilepsy and histopathologically confirmed FCD, 60 age- and gender-matched healthy controls (HCs), and 26 disease controls (DCs). Subjects underwent whole-brain 3 Tesla MRF acquisition, the reconstruction of which generated T1 and T2 relaxometry maps. A 3D region of interest was manually created for each lesion, and z-score normalization using HC data was performed. We conducted 2D classification with ensemble models using MRF T1 and T2 mean and standard deviation from gray matter and white matter for FCD versus controls. Subtype classification additionally incorporated entropy and uniformity of MRF metrics, as well as morphometric features from the morphometric analysis program (MAP). We translated 2D results to individual probabilities using the percentage of slices above an adaptive threshold. These probabilities and clinical variables were input into a support vector machine for individual-level classification. Fivefold cross-validation was performed and performance metrics were reported using receiver-operating-characteristic-curve analyses.

Results

FCD versus HC classification yielded mean sensitivity, specificity, and accuracy of 0.945, 0.980, and 0.962, respectively; FCD versus DC classification achieved 0.918, 0.965, and 0.939. In comparison, visual review of the clinical magnetic resonance imaging (MRI) detected 48% (16/33) of the lesions by official radiology report. In the subgroup where both clinical MRI and MAP were negative, the MRF-ML models correctly distinguished FCD patients from HCs and DCs in 98.3% of cross-validation trials. Type II versus non-type-II classification exhibited mean sensitivity, specificity, and accuracy of 0.835, 0.823, and 0.83, respectively; type IIa versus IIb classification showed 0.85, 0.9, and 0.87. In comparison, the transmantle sign was present in 58% (7/12) of the IIb cases.

Interpretation

The MRF-ML framework presented in this study demonstrated strong efficacy in noninvasively classifying FCD from normal cortex and distinguishing FCD subtypes. ANN NEUROL 2024;96:944–957

Abstract Image

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

利用三维磁共振指纹技术对局灶性皮质发育不良进行多参数特征描述

研究目的利用高分辨率三维（3D）磁共振（MR）指纹（MRF）数据开发一种多参数机器学习（ML）框架，用于定量表征局灶性皮质发育不良（FCD）：我们纳入了 119 名受试者，其中包括 33 名局灶性癫痫且组织病理学证实为 FCD 的患者、60 名年龄和性别匹配的健康对照组 (HC) 以及 26 名疾病对照组 (DC)。受试者接受了全脑 3 特斯拉 MRF 采集，重建后生成了 T1 和 T2 弛豫测量图。我们为每个病灶手动创建了一个三维感兴趣区，并使用 HC 数据进行了 z 值归一化。我们使用 MRF T1 和 T2 平均值以及灰质和白质的标准偏差对 FCD 和对照组进行了二维分类。亚型分类还纳入了 MRF 指标的熵和均匀性，以及形态分析程序 (MAP) 的形态特征。我们使用高于自适应阈值的切片百分比将二维结果转化为个体概率。这些概率和临床变量被输入支持向量机进行个体级分类。我们进行了五倍交叉验证，并通过接受者操作特征曲线分析报告了性能指标：结果：FCD 与 HC 分类的平均灵敏度、特异度和准确度分别为 0.945、0.980 和 0.962；FCD 与 DC 分类的平均灵敏度、特异度和准确度分别为 0.918、0.965 和 0.939。相比之下，对临床磁共振成像（MRI）的目视检查通过官方放射学报告发现了 48% （16/33）的病变。在临床 MRI 和 MAP 均为阴性的亚组中，在磁共振成像阴性组和 MAP 阴性组的交叉验证试验中，MRF-ML 模型有 98.3% 正确区分了 FCD 患者与 HC 和 DC。II 型与非 II 型分类的平均灵敏度、特异度和准确度分别为 0.835、0.823 和 0.83；IIa 型与 IIb 型分类的平均灵敏度、特异度和准确度分别为 0.85、0.9 和 0.87。相比之下，58%（7/12）的 IIb 型病例存在横纹征：本研究提出的 MRF-ML 框架在无创将 FCD 从正常皮质中分类并区分 FCD 亚型方面显示出强大的功效。ann neurol 2024.

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Annals of Neurology 医学-临床神经学

CiteScore

18.00

自引率

1.80%

发文量

270

审稿时长

3-8 weeks

期刊介绍： Annals of Neurology publishes original articles with potential for high impact in understanding the pathogenesis, clinical and laboratory features, diagnosis, treatment, outcomes and science underlying diseases of the human nervous system. Articles should ideally be of broad interest to the academic neurological community rather than solely to subspecialists in a particular field. Studies involving experimental model system, including those in cell and organ cultures and animals, of direct translational relevance to the understanding of neurological disease are also encouraged.