Award Title: Identifying Cancer Recurrence within Patient Care Pathways across Linked National Clinical Datasets
Funder: Department of Health and Social Care

Abstract: For patients having curative surgery or radiotherapy for their cancer, the aim of their treatment is to get rid of the cancer without it coming back several years later. For these patients, cancer recurrence is the single most important outcome of care. Cancer recurrence is a key outcome in research of primary cancer treatments. Also, cancer recurrence is a starting point of research that will help to improve the care and outcomes of patients whose cancer recurs. Treatment options for cancer are increasingly complex and there are growing gaps in evidence on the best treatment combinations. Data on cancer recurrence is needed to address these gaps and to ensure all patients receive the best care._x000D_
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Routinely collected digital healthcare data can be used to identify cancer patients but cannot currently be used to detect cancer recurrence. Studies using these types of data, including national clinical audits and studies comparing different treatment options, are therefore unable to use cancer recurrence as an outcome or to study the care and outcomes of patients whose cancer recurs. National clinical data on cancer recurrence is needed to be able to compare treatment options in all patients, not just the selected groups who are included in randomised clinical trials. It would also allow comparisons of treatments that are unlikely to be assessed in randomised clinical trials._x000D_
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With more information collected in national clinical datasets, we can build a detailed picture of patient care from diagnosis and treatment, through monitoring, to later investigations and treatments. Patients who are treated with an aim to cure them of their cancer tend to have predictable patterns of care, first with a treatment phase followed by a longer period of regular but less frequent hospital visits for monitoring. If their cancer returns, this will appear in the data as a change in frequency and type of hospital visits, tests and treatments. A group of clinical experts will use evidence from research, clinical guidelines and clinical audit to help to develop clinical rules which will identify cancer recurrence in the data. Sophisticated computer science methods, known as ‘machine learning’, offer alternative ways to detect patterns of care that tell us it is very likely the cancer has recurred. _x000D_
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The research will develop methods to identify cancer recurrence for bowel cancer in national clinical datasets, and find out how well the methods work for identifying breast and prostate cancer recurrence. To do this, we will:_x000D_
- Identify the care and outcomes pathways of cancer patients across national clinical datasets with information on: diagnosis, pathology; imaging; chemotherapy; radiotherapy; surgery; hospital attendances; and dates and causes of death._x000D_
- Split this data into a development dataset to be used to develop cancer recurrence indicators and a validation dataset to check how well these indicators work._x000D_
- Develop methods to identify if and when bowel cancer has recurred in the development dataset using clinical rule-based approaches and machine learning methods. _x000D_
- Test the validity of the different indicators in the validation dataset using clinical expert adjudication for a sub-set of patients. Use this to recommend the best indicator._x000D_
- Demonstrate the use of the indicator for bowel cancer recurrence._x000D_
- Test how well the indicator works for identifying breast and prostate cancer recurrence.

Lay Abstract: Background:_x000D_
Information on cancer recurrence is not available in routine national clinical data, preventing important cancer research from being carried out in population-based studies. Providing this outcome would allow researchers and analysts to estimate the risk of recurrence for different groups of patients, to provide much needed evidence on the best combinations of treatments, and to evaluate the care and outcomes of patients whose cancer has recurred._x000D_
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Aims and objectives:_x000D_
The aim is to develop and validate methods to phenotype cancer recurrence after curative treatment for bowel cancer in linked national clinical datasets. And to assess how well the methods extend to breast and prostate cancer. Specifically:_x000D_
1. Construct care and outcomes pathways of cancer patients across datasets, from diagnosis and treatment to subsequent investigations and treatments for recurrence_x000D_
2. Develop four indicators of the presence and timing of bowel cancer recurrence, one using clinical rule-based methods, one using statistical modelling and two using machine learning (ML) methods _x000D_
3. Validate the four indicators, including using clinical adjudication for a subset of patients_x000D_
4. Demonstrate the clinical use of the optimal indicator for bowel cancer_x000D_
5. Assess how well the optimal indicator extends to breast and prostate cancer_x000D_
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Methods and timeline:_x000D_
WP1 (pre-start to month 3) Construct the diagnostic, care and outcomes pathways of cancer patients across national Cancer Registration data, Cancer Waiting Times, administrative, imaging, chemotherapy, radiotherapy and mortality data._x000D_
WP2 (months 2-10):  Develop a clinical rule-based indicator for bowel cancer recurrence: starting with a forward searching step using pre-defined sets of codes; enhanced by a backward-searching step to identify additional common coding patterns missed by the forward searching step; and applying a final iteration of the clinical expert review to these additional codes._x000D_
WP3 (months 6-14): Use unsupervised ML methods to identify clusters of bowel cancer patients with distinct patterns of types and timings of healthcare activity and classify each cluster as indicating recurrence or not. Use statistical modelling and supervised ML methods to identify patterns of diagnoses and healthcare activity that accurately predict that the cancer has recurred, using any signal from the data across the entire care pathway._x000D_
WP4 (months 12-18): Validate the indicators, including using clinical adjudication for a subset of patients. Recommend the optimal indicator._x000D_
WP5 (months 19-27): Demonstrate the clinical use of the optimal indicator for bowel cancer._x000D_
WP6 (months 28-34): Assess how well the optimal indicator extends to breast and prostate cancer._x000D_
Dissemination (months 32-36). _x000D_
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Dissemination and impact:_x000D_
Algorithms and methods will be published in full in peer reviewed articles and in online data science repositories, and publicised through stakeholders on the steering committee. The research will accelerate improvements in cancer services by enabling (amongst others): _x000D_
1. Prediction of prognosis to better inform patients_x000D_
2. Evidence on the optimum modalities of cancer care_x000D_
3. Conduct of clinical trials (by identifying patients with recurrence for inclusion in trials and providing recurrence as an outcome measure)_x000D_
4. Service evaluation and feedback to care providers for quality improvement_x000D_
5. Discovery of aetiological factors and novel cancer treatment targets

Research Activity Codes: 8.1 ORGANISATION AND DELIVERY OF SERVICES
Health Categories: CANCER AND NEOPLASMS