Automatic and explainable grading of meningiomas from histopathology images

Ganz J, Kirsch T, Hoffmann L, Bertram CA, Hoffmann C, Maier A, Breininger K, Blümcke I, Jabari S, Aubreville M (2021)

Publication Type: Conference contribution

Publication year: 2021

Publisher: ML Research Press

Book Volume: 156

Pages Range: 69-80

Conference Proceedings Title: Proceedings of Machine Learning Research

Event location: Virtual, Online

Abstract

Meningioma is one of the most prevalent brain tumors in adults. To determine its malignancy, it is graded by a pathologist into three grades according to WHO standards. This grade plays a decisive role in treatment, and yet may be subject to inter-rater discordance. In this work, we present and compare three approaches towards fully automatic meningioma grading from histology whole slide images. All approaches are following a two-stage paradigm, where we first identify a region of interest based on the detection of mitotic figures in the slide using a state-of-the-art object detection deep learning network. This region of highest mitotic rate is considered characteristic for biological tumor behavior. In the second stage, we calculate a score corresponding to tumor malignancy based on information contained in this region using three different settings. In a first approach, image patches are sampled from this region and regression is based on morphological features encoded by a ResNet-based network. We compare this to learning a logistic regression from the determined mitotic count, an approach which is easily traceable and explainable. Lastly, we combine both approaches in a single network. We trained the pipeline on 951 slides from 341 patients and evaluated them on a separate set of 141 slides from 43 patients. All approaches yield a high correlation to the WHO grade. The logistic regression and the combined approach had the best results in our experiments, yielding correct predictions in 32 and 33 of all cases, respectively, with the image-based approach only predicting 25 cases correctly. Spearman's correlation was 0.7163, 0.7926 and 0.7900 respectively. It might be counter-intuitive at first that morphological features provided by the image patches do not improve model performance. Yet, this mirrors the criteria of the grading scheme, where mitotic count is the only unequivocal parameter.

Authors with CRIS profile

Involved external institutions

Technische Hochschule Ingolstadt Germany (DE) Veterinärmedizinische Universität Wien (Vetmeduni Vienna) / University of Veterinary Medicine, Vienna Austria (AT)

How to cite

APA:

Ganz, J., Kirsch, T., Hoffmann, L., Bertram, C.A., Hoffmann, C., Maier, A.,... Aubreville, M. (2021). Automatic and explainable grading of meningiomas from histopathology images. In Manfredo Atzori, Nikolay Burlutskiy, Francesco Ciompi, Zhang Li, Fayyaz Minhas, Henning Muller, Tingying Peng, Nasir Rajpoot, Ben Torben-Nielsen, Jeroen van der Laak, Mitko Veta, Yinyin Yuan, Inti Zlobec (Eds.), Proceedings of Machine Learning Research (pp. 69-80). Virtual, Online: ML Research Press.

MLA:

Ganz, Jonathan, et al. "Automatic and explainable grading of meningiomas from histopathology images." Proceedings of the 2021 MICCAI Workshop on Computational Pathology, COMPAY 2021, Virtual, Online Ed. Manfredo Atzori, Nikolay Burlutskiy, Francesco Ciompi, Zhang Li, Fayyaz Minhas, Henning Muller, Tingying Peng, Nasir Rajpoot, Ben Torben-Nielsen, Jeroen van der Laak, Mitko Veta, Yinyin Yuan, Inti Zlobec, ML Research Press, 2021. 69-80.

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