Same same but different: A Web-based deep learning application revealed classifying features for the histopathologic distinction of cortical malformations.

Abstract

OBJECTIVE:

The microscopic review of hematoxylin-eosin-stained images of focal cortical dysplasia type IIb and cortical tuber of tuberous sclerosis complex remains challenging. Both entities are distinct subtypes of human malformations of cortical development that share histopathological features consisting of neuronal dyslamination with dysmorphic neurons and balloon cells. We trained a convolutional neural network (CNN) to classify both entities and visualize the results. Additionally, we propose a new Web-based deep learning application as proof of concept of how deep learning could enter the pathologic routine.

METHODS:

A digital processing pipeline was developed for a series of 56 cases of focal cortical dysplasia type IIb and cortical tuber of tuberous sclerosis complex to obtain 4000 regions of interest and 200 000 subsamples with different zoom and rotation angles to train a neural network. Guided gradient-weighted class activation maps (Guided Grad-CAMs) were generated to visualize morphological features used by the CNN to distinguish both entities.

RESULTS:

Our best-performing network achieved 91% accuracy and 0.88 area under the receiver operating characteristic curve at the tile level for an unseen test set. Novel histopathologic patterns were found through the visualized Guided Grad-CAMs. These patterns were assembled into a classification score to augment decision-making in routine histopathology workup. This score was successfully validated by 11 expert neuropathologists and 12 nonexperts, boosting nonexperts to expert level performance.

SIGNIFICANCE:

Our newly developed Web application combines the visualization of whole slide images with the possibility of deep learning-aided classification between focal cortical dysplasia IIb and tuberous sclerosis complex. This approach will help to introduce deep learning applications and visualization for the histopathologic diagnosis of rare and difficult-to-classify brain lesions.

• Authors: Joshua Kubach, Angelika Muhlebner-Fahrngruber, Figen Soylemezoglu, Hajime Miyata, Pitt Niehusmann, Mrinalini Honavar, Fabio Rogerio, Se-Hoon Kim, Eleonora Aronica, Rita Garbelli, Samuel Vilz, Alexander Popp, Stefan Walcher, Christoph Neuner, Michael Scholz, Stefanie Kuerten, Verena Schropp, Sebastian Roeder, Philip Eichhorn, Markus Eckstein, Axel Brehmer, Katja Kobow, Roland Coras, Ingmar Blumcke, Samir Jabari
• Journal: Epilepsia (Epilepsia) Vol. 61 Issue 3 Pg. 421-432 (03 2020) ISSN: 1528-1167 [Electronic] United States
• PMID: 32080846 (Publication Type: Journal Article)
• Copyright: © 2020 The Authors. Epilepsia published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy.

Topics

• Algorithms
• Area Under Curve
• Cerebral Cortex (pathology)
• Deep Learning
• Diagnosis, Computer-Assisted
• Epilepsy (diagnosis, pathology)
• Humans
• Internet
• Malformations of Cortical Development, Group I (diagnosis, pathology)
• Neural Networks, Computer
• Neurons (pathology)
• Neuropathology
• Proof of Concept Study
• ROC Curve
• Reproducibility of Results
• Tuberous Sclerosis (diagnosis, pathology)