Primary liver cancer classification from routine tumour biopsy using
  weakly supervised deep learning
Aurélie Beaufrère Nora Ouzir Paul Emile Zafar Astrid Laurent-Bellue Miguel Albuquerque Gwladys Lubuela Jules Grégory Catherine Guettier Kévin Mondet Jean-Christophe Pesquet Valérie Paradis
The diagnosis of primary liver cancers (PLCs) can be challenging, especially
on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We
automatically classified PLCs on routine-stained biopsies using a weakly
supervised learning method. Weak tumour/non-tumour annotations served as labels
for training a Resnet18 neural network, and the network's last convolutional
layer was used to extract new tumour tile features. Without knowledge of the
precise labels of the malignancies, we then applied an unsupervised clustering
algorithm. Our model identified specific features of hepatocellular carcinoma
(HCC) and intrahepatic cholangiocarcinoma (iCCA). Despite no specific features
of cHCC-CCA being recognized, the identification of HCC and iCCA tiles within a
slide could facilitate the diagnosis of primary liver cancers, particularly
cHCC-CCA.
  Method and results: 166 PLC biopsies were divided into training, internal and
external validation sets: 90, 29 and 47 samples. Two liver pathologists
reviewed each whole-slide hematein eosin saffron (HES)-stained image (WSI).
After annotating the tumour/non-tumour areas, 256x256 pixel tiles were
extracted from the WSIs and used to train a ResNet18. The network was used to
extract new tile features. An unsupervised clustering algorithm was then
applied to the new tile features. In a two-cluster model, Clusters 0 and 1
contained mainly HCC and iCCA histological features. The diagnostic agreement
between the pathological diagnosis and the model predictions in the internal
and external validation sets was 100% (11/11) and 96% (25/26) for HCC and 78%
(7/9) and 87% (13/15) for iCCA, respectively. For cHCC-CCA, we observed a
highly variable proportion of tiles from each cluster (Cluster 0: 5-97%;
Cluster 1: 2-94%).