Brainomix has debuted data from a Phase III progressive pulmonary fibrosis (PPF) study that highlights the ability of its AI-powered medical imaging tool, e-Lung, to predict a patient’s disease progression trajectory and assess how effectively they have responded to pharmaceutical treatment.
According to analysis from the INBUILD study (NCT02999178), which evaluated the quantitative CT measurements taken using both Brainomix’s e-Lung software and the University of California, Los Angeles’ (UCLA) research algorithm, both systems were consistently and sensitively able to measure the effects of antifibrotic treatment in 474 patients with PPF.
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During the trial, researchers demonstrated that antifibrotic treatment, nintedanib, significantly impacted e-Lung-specific measures, including total disease extent (TDE) at weeks 24 and 52, as well as reticulovascular score (RVS) and weighted reticulovascular score (WRVS) at the 24-week mark.
On top of this, the analysis revealed that higher baseline quantitative CT measures of factors like TDE were linked with a larger rate of decline in forced vital capacity (FVC), a key indicator of disease progression, over 52 weeks.
Brainomix notes that these findings could highlight the potential of quantitative CT in securing richer insights from clinical trials that can complement traditional measurements such as FVC. By collating more sensitive data on any disease-related changes happening in the lungs, the medtech company notes that technologies such as e-Lung could hold the potential to expedite the development and assessment of new treatments for interstitial lung diseases, including PPF.
According to Brainomix’s medical director Anand Devaraj, the findings also support the “continued development and incorporation of quantitative CT as an objective biomarker for clinical trials”.
Devaraj adds that such technology could also hold a place in clinical practice, as it could help to monitor a patient’s disease progression over time.
Meanwhile, Susanne Stowasser, INBUILD co-author and head of pulmonology/rheumatology, clinical development at Boehringer Ingelheim, noted that detecting meaningful changes in lung structure could build stronger foundations for evidence around antifibrotic therapies, while guiding the design of future clinical studies.
Brainomix debuts these results as imaging becomes an increasingly important element of clinical trials, with images now often being used as endpoints across the cardiovascular and neurological fields.
This comes amid the backdrop of AI making its way into the imaging paradigm, with many healthcare systems and clinical trial operators employing machine learning models to rapidly assess and flag imaging scans for tasks like diagnosing diseases or mapping progression.
However, when technologies such as AI are implemented into the workflow, users must make several considerations around the governance of such a technology, which has presented hurdles for some healthcare systems.
Despite the challenges AI may bring to the table, a recent report from GlobalData, the parent company of Clinical Trials Arena, predicts that the healthcare AI segment will be worth $57.4bn by 2029.
