基于深度学习的局灶性皮质发育不良(FCD)组织病理学分类器揭示了合并 FCD 亚型的复杂情况。

IF 6.6 1区 医学 Q1 CLINICAL NEUROLOGY
Epilepsia Pub Date : 2024-10-23 DOI:10.1111/epi.18161
Jörg Vorndran, Ingmar Blümcke
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引用次数: 0

摘要

目的:最近,我们开发了首个基于人工智能(AI)的数字病理分类器,用于分类 ILAE 分类定义的局灶性皮质发育不良(FCD)。在此,我们测试了该分类器在回顾性组织病理学检查中的实用性:我们选取了86例组织病理学确诊的FCD ILAE Ia型(FCDIa)、FCDIIa、FCDIIb、癫痫少突胶质增生性皮质发育轻度畸形(MOGHE)或皮质发育轻度畸形的新病例,其中20例确诊有基因嵌合:在所有病例中,分类器总能在四张或更多 1000 × 1000μm 数字瓷砖上识别出正确的组织病理学诊断。此外,在所有病例中,80.2%的最终诊断与算法分配给一个诊断实体的最大批次瓦片重叠。然而,86.2%的病例显示了一个以上的诊断类别。举例来说,在 23 例组织病理学诊断为 FCDIIb 的患者中,FCDIIb 被全部识别出来,而分类器在其中的 19 例(83%)中正确识别出了 FCDIIa 图谱,即神经元畸形,但没有气球细胞。与此相反,在 23 个组织病理学归类为 FCDIIa 的病例中,分类器误诊了 7 个 FCDIIb 瓦片(33%)。这就要求签字的组织病理学家再次检查,以确认气球细胞或与反应性星形胶质细胞相鉴别。该算法还能识别并存的结构发育不良,例如,在 22% 被归类为 FCDIIa 的病例和 62% 伴有 MOGHE 的病例中,存在与 FCDIa 相同的垂直方向的微柱。显微镜检查证实,大多数瓷砖上都有微柱,这表明垂直方向的建筑异常比之前预计的更为常见:基于人工智能的诊断分类器将成为我们未来组织病理学实验室的有用工具,尤其是在癫痫手术的大面积解剖切除需要大量资源时。我们还提供了一个开放访问的网络应用程序,允许组织病理学家虚拟查看从癫痫手术中获得的数字瓷砖,以证实他们的最终诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A deep-learning-based histopathology classifier for focal cortical dysplasia (FCD) unravels a complex scenario of comorbid FCD subtypes.

Objective: Recently, we developed a first artificial intelligence (AI)-based digital pathology classifier for focal cortical dysplasia (FCD) as defined by the ILAE classification. Herein, we tested the usefulness of the classifier in a retrospective histopathology workup scenario.

Methods: Eighty-six new cases with histopathologically confirmed FCD ILAE type Ia (FCDIa), FCDIIa, FCDIIb, mild malformation of cortical development with oligodendroglial hyperplasia in epilepsy (MOGHE), or mild malformations of cortical development were selected, 20 of which had confirmed gene mosaicism.

Results: The classifier always recognized the correct histopathology diagnosis in four or more 1000 × 1000-μm digital tiles in all cases. Furthermore, the final diagnosis overlapped with the largest batch of tiles assigned by the algorithm to one diagnostic entity in 80.2% of all cases. However, 86.2% of all cases revealed more than one diagnostic category. As an example, FCDIIb was identified in all of the 23 patients with histopathologically assigned FCDIIb, whereas the classifier correctly recognized FCDIIa tiles in 19 of these cases (83%), that is, dysmorphic neurons but no balloon cells. In contrast, the classifier misdiagnosed FCDIIb tiles in seven of 23 cases histopathologically assigned to FCDIIa (33%). This mandates a second look by the signing histopathologist to either confirm balloon cells or differentiate from reactive astrocytes. The algorithm also recognized coexisting architectural dysplasia, for example, vertically oriented microcolumns as in FCDIa, in 22% of cases classified as FCDII and in 62% of cases with MOGHE. Microscopic review confirmed microcolumns in the majority of tiles, suggesting that vertically oriented architectural abnormalities are more common than previously anticipated.

Significance: An AI-based diagnostic classifier will become a helpful tool in our future histopathology laboratory, in particular when large anatomical resections from epilepsy surgery require extensive resources. We also provide an open access web application allowing the histopathologist to virtually review digital tiles obtained from epilepsy surgery to corroborate their final diagnosis.

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来源期刊
Epilepsia
Epilepsia 医学-临床神经学
CiteScore
10.90
自引率
10.70%
发文量
319
审稿时长
2-4 weeks
期刊介绍: Epilepsia is the leading, authoritative source for innovative clinical and basic science research for all aspects of epilepsy and seizures. In addition, Epilepsia publishes critical reviews, opinion pieces, and guidelines that foster understanding and aim to improve the diagnosis and treatment of people with seizures and epilepsy.
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