  Nasopharyngeal Carcinoma (NPC) is endemic cancer in the south-east Asia. With
the advent of intensity-modulated radiotherapy excellent locoregional control
are being achieved. Consequently, this had led to pretreatment clinical staging
classification to be less prognostic of outcomes such as recurrence after
treatment. Alternative pretreatment strategies for prognosis of NPC after
treatment are needed to provide better risk stratification for NPC. In this
study we proposed a deep convolution neural network model based on
contrast-enhanced T1 (T1C) and T2 weighted (T2) MRI scan to predict 3-year
disease progression of NPC patient after primary treatment. We retrospective
obtained 596 non-metastatic NPC patients from four independent centres in Hong
Kong and China. Our model first performs a segmentation of the primary NPC
tumour to localise the tumour, and then uses the segmentation mask as prior
knowledge along with the T1C and T2 scan to classify 3-year disease
progression. For segmentation, we adapted and modified a VNet to encode both
T1C and T2 scan and also encoding to classify T and overall stage
classification. Our modified network performed better than baseline VNet with
T1C and network with no T and overall classification. The classification result
for 3-year disease progression achieved an AUC of 0.828 in the validation set
but did not generalised well for the test set which consist of 146 patients
from a different centre to the training data (AUC = 0.69). Our preliminary
results show that deep learning may offer prognostication of disease
progression of NPC patients after treatment. One advantage of our model is that
it does not require manual segmentation of the region of interest, hence
reducing clinician's burden. Further development in generalising multicentre
data set are needed before clinical application of deep learning models in
assessment of NPC.
