# Deep convolution neural network model for automatic risk assessment of patients with non-metastatic nasopharyngeal carcinoma

## TL;DR

This study develops a deep convolutional neural network to predict 3-year disease progression in non-metastatic nasopharyngeal carcinoma patients using MRI scans, aiming to improve risk stratification post-treatment.

## Contribution

It introduces a novel deep learning model that combines tumor segmentation and classification without manual segmentation, enhancing prognostic accuracy for NPC.

## Key findings

- Achieved an AUC of 0.828 on validation data.
- Model performance dropped to an AUC of 0.69 on external test data.
- Deep learning shows promise for NPC prognosis but needs better generalization.

## Abstract

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.