  Cancer histology reveals disease progression and associated molecular
processes, and contains rich phenotypic information that is predictive of
outcome. In this paper, we developed a computational approach based on deep
learning to predict the overall survival and molecular subtypes of glioma
patients from microscopic images of tissue biopsies, reflecting measures of
microvascular proliferation, mitotic activity, nuclear atypia, and the presence
of necrosis. Whole-slide images from 663 unique patients [IDH: 333
IDH-wildtype, 330 IDH-mutants, 1p/19q: 201 1p/19q non-codeleted, 129 1p/19q
codeleted] were obtained from TCGA. Sub-images that were free of artifacts and
that contained viable tumor with descriptive histologic characteristics were
extracted, which were further used for training and testing a deep neural
network. The output layer of the network was configured in two different ways:
(i) a final Cox model layer to output a prediction of patient risk, and (ii) a
final layer with sigmoid activation function, and stochastic gradient decent
based optimization with binary cross-entropy loss. Both survival prediction and
molecular subtype classification produced promising results using our model.
The c-statistic was estimated to be 0.82 (p-value=4.8x10-5) between the risk
scores of the proposed deep learning model and overall survival, while
accuracies of 88% (area under the curve [AUC]=0.86) were achieved in the
detection of IDH mutational status and 1p/19q codeletion. These findings
suggest that the deep learning techniques can be applied to microscopic images
for objective, accurate, and integrated prediction of outcome for glioma
patients. The proposed marker may contribute to (i) stratification of patients
into clinical trials, (ii) patient selection for targeted therapy, and (iii)
personalized treatment planning.
