Intelligent and Personalised Risk Stratification and Early Diagnosis of Lung Cancer

Lung cancer is the second most common cancer in both males and females, and has a very poor prognosis, causing >35,000 of cancer-related deaths each year (nearly 100 every day). This is due to the mostly very late-stage diagnosis of cancer: nearly 50% of all lung cancer cases are only diagnosed at very late Stage IV where no curative treatment exists. The annual cost of lung cancer to the UK economy is estimated to be around £2.4 billion, taking into account the cost of treatment and premature death, the cost to business of sick leave and of unpaid care by friends and family. It eclipses the cost of any other cancer, and continues to present a significant economic and healthcare burden. There is currently no national lung cancer screening programme in the UK, as current tests are deemed to be inadequate, and not outweighing risks associated with screening that involves radiation exposure.

The vision for our Healthcare Impact Partnership is to pave the way for a fundamentally different approach in which lung cancer can be detected at an early stage in patients identified as being at high risk of developing lung cancer: In a close three-way partnership between clinicians specialising in lung disease, healthcare industry, and computational imaging/machine learning researchers, we will jointly develop, test and clinically evaluate new computational methods for detecting, classifying and monitoring lung nodules in low-dose longitudinal CT of patients identified of being at risk of developing lung cancer. Specifically, we will embed powerful machine learning methods within a lung registration framework that has been previous developed using EPSRC funding. Through the use of deep feature learning techniques as well as through learning complex respiratory motion patterns, we will explore transfer learning from large, annotated lung CT image databases to a high-risk patient cohort receiving low-dose CT imaging.

Through this partnership, which brings together expertise in computational imaging and deep learning, expert clinicians and a leading CT manufacturer, we aim to achieve both computational innovations in personalised diagnostics, as well as a strong translational focus for improved patient management, effectively leading the way in providing advanced healthcare technologies for a future UK lung cancer screening programme.

Lung cancer is the second most common cancer in men and women in the UK, with >45,000 new diagnoses each year and a high morbidity (5-year survival is 10%, with 100 deaths per day). 50% of all cases are diagnosis only at late stage IV, when curative treatment is no longer possible. The annual cost of lung cancer to the UK economy is estimated to be around £2.4 billion, taking into account the cost of treatment and premature death, the cost to business of sick leave and of unpaid care by friends and family. It eclipses the cost of any other cancer, with each lung cancer patient costing the UK healthcare system alone over £9k p.a. Lung cancer therefore continues to present a significant economic and healthcare burden. There is currently no national lung cancer screening programme in the UK, as current tests are deemed to be inadequate, and not outweighing risks associated with screening that involves radiation exposure (Source: CRUK).

This Healthcare Impact Partnership will form a close partnership with clinicians and a leading computed tomography (CT) manufacturer, as well as scientists in computational imaging and machine learning, to develop and clinically evaluate new computational approaches for early detection of lung cancer, to improve patient management, reduce morbidity and reduce healthcare costs. Our goal of designing an intelligent computational tool for personalised risk stratification for patients identified as being at risk of developing lung cancer will pave the way for a future UK national lung cancer screening programme.

The main beneficiaries are patients with chronic obstructive pulmonary disease (COPD), who may be identified as high-risk patients due to smoking habits or other factors. These patients will benefit from early diagnosis of cancer, where it may still be treatable, and would be at least trebling their survival rate, and improve their quality of life and patient experience. Beneficiaries also include their families or carers, as well as their employers and through that the UK economy. Healthcare savings would potentially be substantial, e.g. by reducing major surgeries to minor interventions, which would be of benefit to healthcare providers.
Clinicians (radiologists, oncologists, physicians) would also benefit from the proposed research, being assisted by an intelligent tool for early clinical diagnosis, reducing the conventional radiological reading and providing automated, objective and predictive measures. The risk stratification would further aid in clinical decision making for optimal treatment selection, patient recall or referrals. Early diagnosis of lung cancer would widen the currently very small window of opportunity of developing effective cancer treatments, which would also be of benefit to pharmaceutical industries.
Healthcare industry, represented by a market leader in medical imaging in this Healthcare Impact Partnership, are also a beneficiary of the proposed research, as they would significantly benefit from advanced diagnostics tools that provide meaningful clinical information to the end user, aiding their product development and marketing.

Finally, other researchers would benefit from translating ideas generated by this research to other clinical applications in cancer, cardiology or neurology, where patient risk stratification is required. Through that, the societal and healthcare benefit would further increase as a wider range of patients would received optimised treatment based on more accurate and early diagnosis.