  This study aimed to develop a classifier of prognosis after resection or
liver transplantation (LT) for HCC by directly analysing the ubiquitously
available histological images using deep learning based neural networks.
Nucleus map set was used to train U-net to capture the nuclear architectural
information. Train set included the patients with HCC treated by resection and
has a distinct outcome. LT set contained patients with HCC treated by LT. Train
set and its nuclear architectural information extracted by U-net were used to
train MobileNet V2 based classifier (MobileNetV2_HCC_Class), purpose-built for
classifying supersized heterogeneous images. The MobileNetV2_HCC_Class
maintained relative higher discriminatory power than the other factors after
HCC resection or LT in the independent validation set. Pathological review
showed that the tumoral areas most predictive of recurrence were characterized
by presence of stroma, high degree of cytological atypia, nuclear
hyperchomasia, and a lack of immune infiltration. A clinically useful
prognostic classifier was developed using deep learning allied to histological
slides. The classifier has been extensively evaluated in independent patient
populations with different treatment, and gives consistent excellent results
across the classical clinical, biological and pathological features. The
classifier assists in refining the prognostic prediction of HCC patients and
identifying patients who would benefit from more intensive management.
