Imperial College Healthcare NHS Trust

Government Agency (Health and Adult Social Care)

Susannah Bloch

Unknown

No

NIBDAPC_2021_0002

Lung cancer is one of the most common cancers within the UK, and continues to be diagnosed at late stages, where curative treatment cannot be offered. The symptom burden and mortality in advanced lung cancer is significant. Early diagnosis can be increased by efficient pick-up and surveillance of lung nodules, small spots on the lung which are common incidental findings when CT scans are done for other reasons in healthcare. The majority of lung nodules are not concerning, but up to 10% of lung nodules can become cancerous. Careful follow-up scans under specialists (Respiratory services) detect nodule growth, enabling us to identify and curatively treat lung cancers as early as possible. This is vital to improve lung cancer survival. Although national guidelines guide surveillance, variation still exists in practice, and follow-up relies on individual clinicians reading lengthy reports. This poses a significant safety risk to patients, of loss to follow-up or delay in referral. This project utilises computer coding to develop a search strategy which acts as a safety-net to identify scans reporting a lung nodule needing specialist input. Automating this process reduces risk of losing patients, and crucially of missing any opportunities to diagnose lung cancer at an early stage. The first stage will refine coding developed collaboratively with the Royal Marsden Informatics team, to accurately identify scans reporting lung nodules. The second stage will retrospectively test the code and cross-link findings with electronic patient records to evaluate if referral occurred, and how referral time correlates with stage and treatment if cancer was diagnosed. From this, we will analyse which patient groups are particularly at risk of delay. Finally, this project will directly improve clinical care for patients as it can be implemented into hospital systems to reduce variation in follow up, supporting efficient early cancer diagnosis pathways.

As part of the pilot work towards this study, which is supported by the West London Cancer Alliance (Royal Marsden Partners), a feedback exercise was undertaken with patients under lung nodule surveillance, or who had recently completed their surveillance. The local care team spoke to patients to invite them to a short telephone interview with one of the study team (PR), in conjunction with their local care team, to seek feedback on the nodule surveillance service. One of the common themes identified through feedback at multiple Trusts was anxiety around the long waiting time of surveillance for a potentially pre-malignant lesion, and the concern about “being forgotten”, a sentiment which was amplified by the cessation of routine care during the first wave of the Covid-19 pandemic. This directly highlights the value of our research question around how an iterative machine learning approach can offer a safety net to patients.

Public Health Research

02/08/2021

30/07/2021

De-Personalised

Not applicable

Not applicable

One-off

02/08/2021

TRE