Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study.
Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study.
复制标题
DOI：
10.1093/brain/awac224
复制
发表时间：
2022-11-21
期刊：
影响因子：
14.5
通讯作者：
Wagstyl, Konrad
中科院分区：
文献类型：
--
作者：
Spitzer, Hannah;Ripart, Mathilde;Whitaker, Kirstie;D'Arco, Felice;Mankad, Kshitij;Chen, Andrew A.;Napolitano, Antonio;De Palma, Luca;De Benedictis, Alessandro;Foldes, Stephen;Humphreys, Zachary;Zhang, Kai;Hu, Wenhan;Mo, Jiajie;Likeman, Marcus;Davies, Shirin;Guttler, Christopher;Lenge, Matteo;Cohen, Nathan T.;Tang, Yingying;Wang, Shan;Chari, Aswin;Tisdall, Martin;Bargallo, Nuria;Conde-Blanco, Estefania;Pariente, Jose Carlos;Pascual-Diaz, Saul;Delgado-Martinez, Ignacio;Perez-Enriquez, Carmen;Lagorio, Ilaria;Abela, Eugenio;Mullatti, Nandini;O'Muircheartaigh, Jonathan;Vecchiato, Katy;Liu, Yawu;Caligiuri, Maria Eugenia;Sinclair, Ben;Vivash, Lucy;Willard, Anna;Kandasamy, Jothy;McLellan, Ailsa;Sokol, Drahoslav;Semmelroch, Mira;Kloster, Ane G.;Opheim, Giske;Ribeiro, Leticia;Yasuda, Clarissa;Rossi-Espagnet, Camilla;Hamandi, Khalid;Tietze, Anna;Barba, Carmen;Guerrini, Renzo;Gaillard, William Davis;You, Xiaozhen;Wang, Irene;Gonzalez-Ortiz, Sofia;Severino, Mariasavina;Striano, Pasquale;Tortora, Domenico;Kalviainen, Reetta;Gambardella, Antonio;Labate, Angelo;Desmond, Patricia;Lui, Elaine;O'Brien, Terence;Shetty, Jay;Jackson, Graeme;Duncan, John S.;Winston, Gavin P.;Pinborg, Lars H.;Cendes, Fernando;Theis, Fabian J.;Shinohara, Russell T.;Cross, J. Helen;Baldeweg, Torsten;Adler, Sophie;Wagstyl, Konrad
诊断生物医学成像中机器学习的一个突出挑战是算法的可解释性。一个关键的应用是从结构MRI中识别细微的致癫痫局灶性皮质发育不良（FCD）。FCD在结构MRI上难以可视化，但通常适合手术切除。我们的目标是开发一种开源的、可解释的、基于表面的机器学习算法，以自动识别来自全球癫痫手术中心的异构结构MRI数据上的FCD。多中心癫痫病灶检测（MELD）项目整理并协调了来自全球22个癫痫中心的1015名参与者（618名局灶性FCD相关癫痫患者和397名对照）的回顾性MRI队列。我们基于33个基于表面的特征创建了一个用于FCD检测的神经网络。该网络在总队列的50%上进行了训练和交叉验证，并在剩余的50%以及2个独立的测试站点上进行了测试。多维特征分析和综合梯度显着性被用来询问网络性能。我们的管道输出个体患者报告，这些报告识别预测病变的位置，以及它们的成像特征和相对于分类器的显著性。在具有T1和液体衰减反转恢复MRI数据的FCD IIB型无脑梗死患者的限制性“金标准”子队列中，MELD FCD基于表面的算法的灵敏度为85%。在整个保留的测试队列中，灵敏度为59%，特异性为54%。在包括病变周围的边界区域后，考虑到手动划定的病变掩模边界周围的不确定性，灵敏度为67%。这项具有开放获取协议和代码的多中心，多国研究开发了一种强大且可解释的机器学习算法，用于自动检测局灶性皮质发育不良，使医生对癫痫患者的细微MRI病变的识别更有信心。Spitzer等人提出了一种用于局灶性皮质发育不良自动检测的强大且可解释的开源机器学习算法。管道输出个体患者报告，其识别预测病变的位置，以及它们的成像特征和相对显著性到分类器。
One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted ‘gold-standard’ subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy. Spitzer et al. present a robust and interpretable open-source machine-learning algorithm for automated detection of focal cortical dysplasias. The pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier.
登录
查看更多内容
利用基于表面的磁共振成像后处理和机器学习自动检测 II 型局灶性皮质发育不良
影响因子：
5.6
作者：
Jin B;Krishnan B;Adler S;Wagstyl K;Hu W;Jones S;Najm I;Alexopoulos A;Zhang K;Zhang J;Ding M;Wang S;Pediatric Imaging, Neurocognition, and Genetics Study;Wang ZI
通讯作者：
Wang ZI
DOI：
10.1109/tpami.2018.2858826
发表时间：
2020-02-01
影响因子：
23.6
作者：
Lin, Tsung-Yi;Goyal, Priya;Dollar, Piotr
通讯作者：
Dollar, Piotr
影响因子：
5.6
作者：
Bernasconi, Andrea;Cendes, Fernando;Bernasconi, Neda
通讯作者：
Bernasconi, Neda
影响因子：
9.9
作者：
Hong, Seok-Jun;Kim, Hosung;Bernasconi, Andrea
通讯作者：
Bernasconi, Andrea
影响因子：
14.5
作者：
McGonigal, Aileen;Bartolomei, Fabrice;Chauvel, Patrick
通讯作者：
Chauvel, Patrick