Title: Fibrotic lung disease on high-resolution computed tomography: predicting disease behaviour using computer algorithms.
Funder: Department of Health and Social Care

Abstract: Background: Fibrotic lung disease represents a group of conditions characterised by scarring of the lung with fibrotic tissue and is a seriously under-recognised problem for the NHS. In 2016, 1% of all deaths in the UK were due to fibrotic lung disease. Idiopathic pulmonary fibrosis (IPF) is the most common fibrotic lung disease and has an inexorably progressive disease course regardless of treatment.Among the fibrotic lung diseases is a group of patients who, independent of their clinical diagnosis, develop progressive, relentless fibrosis which is unresponsive to treatment and has a 'malignant-like' prognosis, recently termed, 'the progressive fibrotic phenotype'. However, there is currently no way to predict from baseline data which patients will develop this phenotype which negatively impacts treatment decisions and increases patient exposure to risky investigations such surgical lung biopsy. Identifying how fibrotic lung disease will progress is the most significant challenge for effective patient management in these conditions.High-resolution computed tomography (HRCT) is central to diagnosis in fibrotic lung disease, and a pattern of usual interstitial pneumonia (UIP) on HRCT is associated with progressive fibrosis. However, the progressive fibrotic phenotype is not confined to this pattern, and other HRCT appearances (or radiologic phenotypes) with the same progressive disease course have not been defined. Deep learning (DL) is a subset of machine learning which has recently achieved impressive results in several medical image classification problems. DL can provide accurate, universally accessible algorithms for analysing HRCT data and predicting which patients will develop the progressive fibrotic phenotype based on their baseline HRCT. Aims: To develop DL algorithms to predict the development of the progressive fibrotic phenotype using baseline HRCT. To identify novel radiologic phenotypes associated with progressive and stable disease.Methods: A dataset of fibrotic lung disease-specific HRCTs together with linked clinical data will be generated from the ILD unit of the Royal Brompton Hospital. Data and an international repository called The Open Source Imaging Consortium. DL will be used to generate an algorithm which predicts, using HRCT, progressive fibrotic lung disease defined by all-cause mortality or >=10% forced vital capacity decline at 12 months from the date of the HRCT. Novel 'progressive fibrotic radiologic phenotypes' identified by the deep learning algorithm will also be investigated. Algorithm validation will be performed prospectively in two longitudinal fibrotic lung disease cohorts and a prospective trial of anti-fibrotic therapy. The clinical impact of the algorithm will be tested by incorporating it in a multicentre study of multidisciplinary team diagnosis and management in 100 cases of fibrotic lung disease involving expert and non-expert multidisciplinary team groups.Dissemination: The results of this research will be disseminated through peer-reviewed publication and academic congresses. Patient groups, including the Pulmonary Fibrosis Warriors (who were instrumental in the development of this proposal), will assist in raising public and patient awareness of this research and identifying further possible avenues of research. Impact: Improved disease behaviour prediction in fibrotic lung disease will reduce patient exposure to unnecessary, risky investigations and reduce healthcare provider cost associated with those investigations.

Lay abstract: Background:Fibrotic lung disease is a group of conditions that result in stiffening of the lungs by scar tissue. Some types of fibrotic lung disease have an identifiable cause such exposure to moulds or some chemicals, while for others, a cause is not found. Fibrotic lung disease is a severely under-recognised public health problem; in 2016, 1% of all deaths in the United Kingdom were due to fibrotic lung disease. Idiopathic pulmonary fibrosis (IPF) is a fibrotic lung disease of unknown cause and is the most common and deadly fibrotic lung disease. Despite being labelled in the media as the disease nobody has heard of, 5000 new cases of IPF are diagnosed in the UK each year and this figure is increasing by 5% annually. IPF kills as many people in the UK per year as breast cancer. The cornerstone of diagnosis in fibrotic lung disease is high-resolution computed tomography (HRCT) which is a scan that uses X-rays to create detailed images of the lungs. The appearances of the HRCT determine if a diagnosis can be made without any more tests or whether invasive and potentially risky procedures such as surgical lung biopsy are needed. A significant problem in fibrotic lung disease is that predicting the course of a patient's condition (prognosis) is extremely difficult which hampers doctors decisions regarding treatment and the need for surgical lung biopsy. Although a diagnosis of IPF indicates a poor prognosis, other fibrotic lung diseases may remain stable without treatment or get worse even on treatment. Currently, there is no way to predict how fibrotic lung disease will progress in an individual patient when they first present to their doctor (sometimes called, 'at baseline'). Deep learning is a branch of computer science which trains machines to detect patterns in large quantities of data and has the potential to provide inexpensive tools for analysing patients' HRCT scans. I recently developed 'deep learning' software that outperformed two-thirds of specialist radiologists interpreting 3 different fibrotic lung diseases on HRCT. With large quantities of HRCT scans, deep learning software could be developed that can predict the future course of a patient's disease using their initial HRCT scan. Aim, Design and Methods:Using a large database of HRCT imaging from a single specialist fibrotic lung disease hospital as well as HRCT data from an international database, computer software that can predict the disease behaviour in an individual patient based on their baseline HRCT scan will be developed using deep learning. Patient and Public Involvement:During interviews with six members of the Pulmonary Fibrosis Warriors and Action for Pulmonary Fibrosis Group, patients stated that uncertainty regarding their outcome was a significant cause of emotional stress and made it difficult to plan appropriately for the future. Two patients with IPF from the Pulmonary Fibrosis Warriors in the US and the Action for Pulmonary Fibrosis Group will participate on a steering advisory group to help manage the research and assist in disseminating results through lay websites and social media. Dissemination:Results will be reported through presentations at academic congresses and publication in peer-reviewed journals. A lay summary of results will also be provided to patient advocacy groups to post on their websites and social media.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: RESPIRATORY