A new method for measuring treatment response and disease progression in Idiopathic Pulmonary Fibrosis

Abstract

Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic interstitial lung disease
(ILD) with poor prognosis. Pulmonary function test (PFT) and computed tomography
(CT) scan of the chest are the standard tools in routine clinical care for monitoring
disease progression and treatment response. PFT is effort dependent and does not
provide any regional information for this heterogenous disease. CT chest does provide
regional information, however, exposure to high dose ionising radiation limits its
frequent use. Hyperpolarised 129Xenon MRI (HPXMRI) is unique in its ability to
provide concurrent structural and functional information without any
exposure to ionising radiation.
In this thesis, I have explored the potential of HPXMRI in the detection of treatment
response and of progression in IPF. To check that the findings are specific for
IPF, sarcoidosis patients were also examined using the same study
protocol.
My data showed that there was a significant improvement in mean red blood
cell/ tissue-plasma ratio (mRBC/ TP ratio) for IPF patients following
treatment with anti-fibrotic medication at 12months post-treatment. This is
despite no improvement in PFT or CT scan. Moreover, patients with fast
fibrotic progression on CT at 12 months, were noted to have a lower mRBC/
TP ratio at baseline compared to those with slow progression. There was
no significant difference in PFT or extent of fibrosis on CT between the fast
and the slow progressors at baseline. A significant improvement in mRBC/
TP ratio post-treatment with prednisolone was not detected in the
sarcoidosis cohort.
These findings suggest that mRBC/ TP ratio has a role in the detection of
treatment response in IPF and may be more sensitive than CT scan in
identifying the response. There is also potential for mRBC/ TP ratio as a
prognostic tool in IPF through identification of patients who are at risk of
rapid progression.

Date of Award	20 Jan 2026
Original language	English
Awarding Institution	University of Bristol
Supervisor	Nick A Maskell (Supervisor) & Ling-Pei Ho (Supervisor)