  Malignant mesothelioma is classified into three histological subtypes,
Epithelioid, Sarcomatoid, and Biphasic according to the relative proportions of
epithelioid and sarcomatoid tumor cells present. Biphasic tumors display
significant populations of both cell types. This subtyping is subjective and
limited by current diagnostic guidelines and can differ even between expert
thoracic pathologists when characterising the continuum of relative proportions
of epithelioid and sarcomatoid components using a three class system. In this
work, we develop a novel dual-task Graph Neural Network (GNN) architecture with
ranking loss to learn a model capable of scoring regions of tissue down to
cellular resolution. This allows quantitative profiling of a tumor sample
according to the aggregate sarcomatoid association score of all the cells in
the sample. The proposed approach uses only core-level labels and frames the
prediction task as a dual multiple instance learning (MIL) problem. Tissue is
represented by a cell graph with both cell-level morphological and regional
features. We use an external multi-centric test set from Mesobank, on which we
demonstrate the predictive performance of our model. We validate our model
predictions through an analysis of the typical morphological features of cells
according to their predicted score, finding that some of the morphological
differences identified by our model match known differences used by
pathologists. We further show that the model score is predictive of patient
survival with a hazard ratio of 2.30. The code for the proposed approach, along
with the dataset, is available at: https://github.com/measty/MesoGraph.
