1.1

To investigate the feasibility of developing computer algorithms that can identify important anatomical structures and the cancers themselves in head and neck cancer planning scans to help target radiotherapy treatment.

Should the primary objective be accomplished, we intend to validate performance using retrospective data through:

2.1

Assessment of quality of automated segmentation using retrospective planning CT images. Expert radiation oncologists, blind to image source, will assess both automatically and manually segmented scans, and determine whether segmentation met a standard for clinical use.

Patients who received radiotherapy treatment for head and neck cancers at UCLH NHS Foundation Trust between 01/01/2008 and 20/03/2016 will be eligible for inclusion in this study.

Data from patients who have previously manually requested that that their data should not be shared, even for research purposes in anonymised form, and have informed the UCLH NHS Foundation Trust of this, will be ineligible and removed by UCLH NHS Foundation Trust staff before research begins.

Approximately 700 retrospective patient cases will be part of this study.

Most recent machine learning algorithms benefit from large datasets on which to train (tens to hundreds of thousands of data instances ( Silver et al., 2016)). Across all machine learning applications the predictive power (as percentage of data instances correctly classified) of the algorithm depends on the size and quality of the dataset.

The sample size is informed by the existing literature (Mnih, 2015; Silver et al., 2016) and by DeepMinds previous work in the field of machine learning. We believe that the research goals are possible despite the relatively small number of scans, as compared to other research projects because of low variation between the different biological images and by limiting the scope of the research to segmentation rather than diagnosis.

For all patients meeting inclusion/exclusion criteria the following electronic health record data will be required to complete this project successfully: (1)

CT scan(s) taken during the course of radiotherapy planning and treatment

The anonymisation procedures adopted will remove any information not specified to further avoid transfer of patient identifiable information. All anonymisation will be formally verified by UCLH NHS Foundation Trust staff before transfer.

In order to develop the algorithms, DeepMind will work with the labelled medical image files to apply machine learning and AI techniques including but not limited to: supervised and semi-supervised convolutional neural networks, recurrent neural networks, unsupervised clustering, reinforcement learning (Murphy, 2012).

Descriptive statistics (such as the Dice similarity coefficient, average surface distance and maximal surface distance) will be used to compare the quality of algorithm segmentation against the expert reference segmentation during algorithm training.

In order to assess the accuracy of the final model segmentation a retrospective test subset of radiotherapy planning images will also have both manual and automatic segmentations corrected by expert clinicians who have not seen the images before and who are blinded to how the segmentation was produced. The same statistical methods described above will be used to compare the ground truth manual segmentation, automatic segmentation and clinically corrected manual and automatic segmentations.

This study requires existing retrospective data only; no prospective data are needed nor will be collected from patients, hospitals or healthcare workers. No direct patient contact will occur and necessary data will be anonymised from this source dataset.

Anonymisation of all image files and clinical information is performed and validated at UCLH NHS Foundation Trust before transfer. No patient identifiers will be transferred to DeepMind. In addition the data will be protected to HSCIC Information Governance standards and access is strictly controlled to prevent any attempt to re-identify the data.

DeepMind Health has developed and established a state-of-the-art secure patient information handling service utilising Common Criteria EAL4 compliant firewalls and on-disk encryption (using Advanced Encryption Standard with a 256-bit key) of all research data, all housed within an ISO 27001 compliant data centre. After anonymisation data will be transferred to our London, UK data centre. This data handling facility conforms to NHS HSCIC Information Governance Statement of Compliance Toolkit (formally assessed at level 3).

Access will be granted by the custodian of data and no other members of the team. Only those working directly on the data in a research capacity will have access.

The data sharing agreement between DeepMind and UCLH NHS Foundation Trust lasts for 5 years. After this period the agreement will be reviewed should future work seek to build on this project. After the data sharing agreement expires all data used in the study will be destroyed. No modification will be made based on the data after destruction.

Data destruction will involve the deletion of the encryption/decryption keys for all project volumes, and 8-pass random data write to all physical disks within the DeepMind Health data infrastructure. A certificate of destruction will be provided to the Trust.

The algorithms developed during the study will not be destroyed. DeepMind Health knows of no way to recreate the patient images transferred from the algorithms developed. No patient identifiable data will be included in the algorithms.

The research on the dataset received formal Research Ethics Committee approval on 6th April 2016 (REC reference 16/SC/0189).

No patient will be approached directly. Only anonymised retrospective data collected as part of routine clinical care are included. In such cases the ICO code of practice states that explicit consent is not generally required ( ICO, 2012).

The project is non-interventional and does not involve any direct patient contact. All patient data is historical and all patients have completed their radiotherapy treatment prior to data transfer.

The study will be monitored both internally and externally. Internally DeepMind managers (TB, JL) will oversee and monitor progress on a day-to-day basis, ensuring the protocol is adhered to and no compliance issues arise.

Clinical and methodological experts (RM, DD, KS, SAM, GR, RR) are working with DeepMind to further oversee the ethical, clinical and methodological considerations of the project and will advise on at least a weekly basis to ensure no deviation from the described protocol.

Externally the information governance team at the UCLH NHS Foundation Trust will be consulted before commencing data collection.

DeepMind has access to the required data to support the research aims of this study. To ensure compliance with the common law principle of data confidentiality, DeepMind will only receive anonymised data from UCLH NHS Foundation Trust. DeepMind works with the Trust to ensure accuracy and clarity in the data to allow useful and consistent interpretation at all times.

The results will be disseminated through normal academic channels, initially focusing on conference proceedings and the indexed peer reviewed literature relevant to the fields of machine learning, artificial intelligence, radiotherapy and clinical research. DeepMind will engage in patient and public involvement groups during the research study.