Computer-aided CT imaging and integration with molecular endotyping to stratify fibrotic lung disease

Background:
Interstitial lung diseases (ILDs) account for approximately 7000 deaths in the UK every year, with idiopathic pulmonary fibrosis (IPF) identified as the most common diagnosis. Classification of ILDs is based upon causation, high resolution (HR) CT scan appearance and lung histology, which requires surgical biopsy.
This is problematic for numerous reasons, namely: 1. There is strong reliance on lung biopsy, an invasive procedure with a 2-7% mortality that many patients will not undergo, leading to a diagnosis of "unclassifiable disease"; 2. The current methods do not reliably inform of either prognosis or treatment efficacy; 3. Clinical reporting of CT's is subjective and not quantitative.
Lung fibrosis is notable for lacking definitive tools to achieve diagnostic precision, resulting in highly heterogenous disease entities.

Recent work:
CT texture analysis platforms, such as the Adaptive Multiple Features Method (AMFM) and the Computer-Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER), have previously been studied with applications in clinical settings. However, these have not been validated in longitudinal cohorts in which ground truth (survival, time to hospitalisation, rate of decline in lung function, response to treatment) is known.
Over the past few years, advances in diagnostic and prognostic biomarker and genetic profiling in lung fibrosis have been made. Some of these have been validated in several cohorts of patients. However, the vast majority of these studies are confined to IPF. Additionally, the largest studies are based on patients recruited to clinical trials and not 'real-world' subjects.

Resources:
Data is available from established gene-, bio- and image-banks, and a unique, ethically approved, prospectively populated database designed to depict the natural history of lung fibrosis. The cohort consists of >1100 consecutively presenting consented patients with lung fibrosis since 2002, with less than 1% lost to follow-up. All patients have CT scans, and more than 800 patients have serial scans. CT scans are hosted within National Services Scotland (NSS) and this is co-located with the Farr network in the Edinburgh Farr node, enabling a safe haven analytic environment for imaging, clinical and 'omic data'.
Furthermore, serum and genomic DNA samples are available from the majority of subjects from the cohort, along with a complete dataset of variables including disease phenotype according to clinical-, CT-, biopsy-category, serial lung function.

Aims:
To integrate known and novel biomarkers, genetic polymorphisms and quantitative CT imaging (radiogenomics) such that these data can be effectively interrogated through machine learning approaches to define clinically meaningful clusters of disease. The aim is to determine homogenous subgroups that better define patient prognosis and response to therapy.

Preliminary programme of work:
1. Identify serum biomarkers, which may effectively discriminate between progressors and non-progressors.
2. Genotyping: perform analysis of GWAS and RNA-seq datasets.
3. Quantitative CT analysis with the CALIPER texture analysis platform. Validate the platform on our datasets. Develop and test an interactive protocol for classification of scans into diagnostic groups.
4. Integration and interrogation of molecular, imaging and phenotypical data such that analyses can be performed for the stratification of disease.

Ultimately, an automated and assistive tool would be developed for personalised predictions of diagnosis, prognosis, rate of decline and response to treatment in lung fibrosis, based on a diverse set of pre-defined variables.