  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 0.716, 0.792 and 0.790 respectively. It
may seem counterintuitive at first that morphological features provided by
image patches do not improve model performance. Yet, this mirrors the criteria
of the grading scheme, where mitotic count is the only unequivocal parameter.
