Applying machine learning to automated segmentation of head and neck tumour volumes and organs at risk on radiotherapy planning CT and MRI scans

Carlton Chu,Julien Cornebise,Bernardino Romera Paredes,Derek D'Souza,Ruheena Mendes,Trevor Back,Olaf Ronneberger,Jeffrey De Fauw,Hugh Montgomery,Joseph Ledsam,Geraint Rees,Syed Moinuddin,Nenad Tomasev,Rosalind Raine,Cían Hughes,Kevin Sullivan

doi:10.12688/f1000research.9525.1

Abstract

Radiotherapy is one of the main ways head and neck cancers are treated; radiation is used to kill cancerous cells and prevent their recurrence. Complex treatment planning is required to ensure that enough radiation is given to the tumour, and little to other sensitive structures (known as organs at risk) such as the eyes and nerves which might otherwise be damaged. This is especially difficult in the head and neck, where multiple at-risk structures often lie in extremely close proximity to the tumour. It can take radiotherapy experts four hours or more to pick out the important areas on planning scans (known as segmentation). This research will focus on applying machine learning algorithms to automatic segmentation of head and neck planning computed tomography (CT) and magnetic resonance imaging (MRI) scans at University College London Hospital NHS Foundation Trust patients. Through analysis of the images used in radiotherapy DeepMind Health will investigate improvements in efficiency of cancer treatment pathways.

Highlights

Cancers of the head and neck account for 2% of all cancers, and worldwide they account for around 300,000 deaths each year
This is especially difficult in the head and neck, where multiple at-risk structures often lie in extremely close proximity to the tumour
This study aims to achieve this with a dataset of expertly labelled images from previously-treated patients at University College London Hospital (UCLH) NHS Foundation Trust (London, UK), with the ultimate objective of improving outcomes for patients with head and neck cancers

Summary

Introduction

Cancers of the head and neck account for 2% of all cancers, and worldwide they account for around 300,000 deaths each year. This study aims to achieve this with a dataset of expertly labelled images from previously-treated patients at University College London Hospital (UCLH) NHS Foundation Trust (London, UK), with the ultimate objective of improving outcomes for patients with head and neck cancers. Most such cancers are treated using radiotherapy (CRUK, 2016). Planning such treatment involves delineating the tumour to be irradiated, and the structures (organs at risk, OAR) to which administration of radiation should be minimised. As tumour and body shape change over a course of treatment (which can last weeks), it can be necessary to repeat the segmentation analysis at both fiscal and temporal cost; longer times between cancer diagnosis and treatment increase mortality and worsen outcomes (Chen et al, 2008; Mikeljevic et al, 2004)

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