Title: Investigating the utility of machine learning methods to predict prognosis and guide treatment decisions for people with lung cancer (Lung-ORACLE)
Funder: Department of Health and Social Care

Abstract: Research question Is it possible to use machine learning methods to guide treatment decisions within a clinical setting for lung cancer patients? Background Prognostic models enable clinicians to design tailored treatment regimes by estimating survival profiles specific to an individuals' clinicopathological features, with the application of machine learning (ML) methods facilitating increasingly accurate predictions. No such prognostic model is currently available for lung cancer patients, despite the complexity in treatment options available and with lung cancer being the leading cause of death from cancer worldwide. Aim To develop clinically informed prognostic models with the potential to improve decision making for lung cancer patients Objectives Identify from published research prognostic and predictive factors to predict survival and quality of life, for patients with lung cancer Derive and validate multiple prognostic models for lung cancer patients, using ML and standard statistical approaches, to identify the most accurate modelling approach Analyse whether a prognostic model will more accurately estimate survival when compared with clinician estimates, for patients with lung cancer Analyse how use of a prognostic model for lung cancer patients may alter clinician treatment recommendations.Methods I will perform a systematic review to identify the prognostic and predictive factors - for example clinical, demographic, psychological, family factors -h; which impact the survival and quality of life for lung cancer patients undergoing treatment (including no treatment) Following this, I will use data retrieved from cancer registries to derive and validate prognostication models for lung cancer patients-h; utilising statistical and ML methods. A clinical vignette-based survey will be undertaken, with clinicians estimating prognosis of clinical vignettes and results compared with prognostication model predictions. An extension of the survey will randomise clinicians to two groups with each group viewing opposing vignettes with clinical information alone, or alongside prognostication tool predictions; treatment recommendations with and without seeing model predictions will be compared. Timelines for delivery Year 1: Protocol finalisation, ethical approval, recruitment of Clinician/patient expert panel. Rapid systematic review (objective 1), retrieval of data from cancer registries and data cleaning (objective 2) Year 2: Model development and validation (objective 2), design clinical vignette-based survey (objectives 3/4) Year 3: Recruitment of clinicians, survey completion and analysis (objectives 3/4), disseminate findings Anticipated impact and dissemination The research will develop a validated prognostication tool to help guide treatment discussions for lung cancer patients. By working with clinicians and patients to begin to understand how this research may inform introduction of such a tool into clinical practice, the research will lay the foundations for developing and undertaking an implementation trial with important implications for cancer prognosis and integration of ML-algorithms into clinical practice. Findings will be shared with academics and clinicians via publication in academic journals and conference presentations. I will also present at public events and work with cancer charities to disseminate findings to patients.

Lay abstract: Background to this research Lung cancer is the leading cause of death from cancer worldwide. There are many different treatments available for those with lung cancer, and these are often given in combination - for example, surgery followed by chemotherapy. This means there are a large number of possible options available to patients, and although research provides good evidence for treatment success across large groups of patients with lung cancer, it is very difficult to know what the best option is for each individual patient. Doctors and patients are therefore faced with making difficult decisions regarding the best treatment course to take. To address this problem, machine learning (ML) (a type of artificial intelligence) has been used in other cancers, such as breast cancer, to guide what treatments would work best for different people. In this way, ML can assist doctors and patients when making treatment decisions and, hopefully, lead to a better chance of surviving cancer. However, no such information exists for lung cancer patients. Aims of the research To improve treatment decisions for lung cancer patients. I will do this by: Understanding what affects lung cancer patient survival and quality of life Developing an online tool that uses ML to accurately predict patient survival and quality of life following differing treatment courses.Design and methods used I will review previous research to understand what factors are known to affect quality of life and a persons' chance of survival if they are treated for lung cancer. The online tool will be generated using techniques such as artificial intelligence (ability of a computer to do human tasks). I will use data collected routinely for patients diagnosed with lung cancer across England and the US to develop and test this tool. For each patient with lung cancer, the tool will predict the likelihood the patient will be alive in five years' time and provide an estimate of quality of life, based on the treatment chosen. I will ask physicians to complete a survey to investigate how using this online tool may alter their treatment recommendationsPatient/service user, carer and public involvement I have worked with a public involvement group with an interest in this research, made up of patient and public representatives. This group have helped develop the research aim and approach and I will continue to work with them throughout this research; including in study design and spreading word of the findings. An example of the influence this group have already had is to ensure the online tool provides an estimate of quality of life, rather than survival estimates alone. I will also involve members on the research advisory panel and I will set up a physician/patient expert panel at the start of the project and work with them throughout to gather their thoughts on the research. Anticipated outcomes The research will develop a new decision support tool to help guide the discussion between patients and physicians on treatment options for lung cancer. The approach used to produce this tool may help to develop other online tools in the future (e.g. for other cancer types). And by re-using these methods, we will speed up the process. By working with professionals and patients, we will start to think of how best to introduce these tools into medical practice, and by allowing the use of these tools to assist when making treatment decisions, this could lead to a better chance of survival. Dissemination Findings will be communicated with the help of the public involvement group via: Presentations at conferences and meetings Health media Scientific publications and talks

Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY);7.3 MANAGEMENT AND DECISION MAKING
Health categories: CANCER AND NEOPLASMS