Quantitative CT Predicts Progression, Nintedanib Effect in PPF

Quantitative computed tomography (CT) methods can help predict disease progression and assess the efficacy of drugs in clinical trials for patients with progressive pulmonary fibrosis (PPF), according to a recent substudy analysis published in the American Journal of Respiratory and Critical Care Medicine.¹

Forced vital capacity (FVC) is commonly used to assess severity and progression of interstitial lung diseases (ILD), making it a key endpoint in clinical trials evaluating this patient population. But more direct and sensitive measures are needed, specifically to further identify structural deterioration in patients with lung fibrosis.¹ This is not the first instance in which researchers aimed to find more definitive tools to measure or predict fibrotic outcomes in patients with ILD. Another study, published in CHEST, used a combined pulmonary fibrosis and emphysema index and CT imaging to predict outcomes in fibrotic ILD.² Prior research has also shown that quantitative CT methods, improved by advances such as machine learning and adaptive denoising, can predict decline in FVC and short-term mortality in patients with various ILDs.¹

The current study analyzed data from patients with PPF in the double-blind, placebo-controlled INBUILD trial (NCT02999178), which assessed the efficacy of nintedanib across various fibrosing lung diseases.³ The optional longitudinal substudy took high-resolution CT (HRCT) scans in patients with an ILD other than idiopathic pulmonary fibrosis (IPF) at baseline, week 24, and week 52.¹

The scans were separately assessed using an algorithm developed by the University of California Los Angeles (UCLA) and e-Lung, an algorithm developed by Brainomix. The UCLA algorithm classified abnormal lung tissue based on pixel density or texture. It also used a series of component scores that total a quantitative ILD (QILD) score. These included the quantitative lung fibrosis (QLF) score, measuring the extent of reticular patterns with architectural distortion due to fibrosis; the QHC score, measuring the extent of honeycomb cysts; and the QGGO score, measuring the extent of ground-glass opacities (GGO).

The e-Lung algorithm, by contrast, is an artificial intelligence–developed image processing module. It measured total disease extent (TDE), GGO, reticulovascular score (RVS), and the weighted RVS (WRVS).¹

How the UCLA and e-Lung CT Algorithms Compared in INBUILD

Of the 663 patients in the INBUILD trial, 474 participated in the HRCT substudy. Mean (SD) exposure to trial medication among patients in the HRCT substudy was 15.1 (6.9) months in the nintedanib group and 16.3 (5.4) months in the placebo group.¹

At baseline, evaluable scans from 403 patients (197 nintedanib, 206 placebo) were assessed with the UCLA algorithm, and scans from 371 patients (179 nintedanib, 192 placebo) were assessed with the e-Lung algorithm. Correlations between UCLA and e-Lung parameters (total lung capacity [TLC] vs e-Lung volume; QLF vs e-Lung WRVS and RVS; QILD vs e-Lung TDE) were strong at baseline, week 24, and week 52, as were correlations between changes from baseline in these parameters at weeks 24 and 52.¹

Overall, small absolute differences were observed in TLC/total lung volume between the 2 quantitative imaging methods. However, there was a larger difference in the total extent of ILD measured with the UCLA QILD score and e-Lung TDE, mainly due to differences in how the 2 algorithms quantified GGO.¹

In the nintedanib and placebo groups, respectively, correlations between change in FVC % predicted and change in QILD score were –0.35 (95% CI, –0.48 to –0.20) and –0.35 (95% CI, –0.47 to –0.22) at week 24 and –0.37 (95% CI, –0.50 to –0.21) and –0.46 (95% CI, –0.58 to –0.32) at week 52.

Correlations between change in FVC % predicted and change in e-Lung TDE were –0.29 (95% CI, –0.43 to –0.13) and –0.34 (95% CI, –0.46 to –0.20) at week 24 and –0.29 (95% CI, –0.44 to –0.12) and –0.38 (95% CI, –0.51 to –0.23) at week 52. The authors characterized these correlations as moderate.¹

“These findings suggest that quantitative CT measures provide complementary information to that provided by FVC and assess structural changes in the lungs that are not completely reflected in changes in FVC,” the study authors wrote. “In the future, quantitative CT may have a role in monitoring the progression of ILD in clinical practice.”

Higher Baseline CT Scores Signal Faster FVC Decline

In the placebo group, higher baseline QILD, QLF, e-Lung TDE, WRVS, and RVS scores were associated with a greater rate of decline in FVC over 52 weeks. Patients with scores above vs below the median also had a higher risk of an absolute decline in FVC % predicted of 10% or more or death over 52 weeks, with differences in restricted mean survival time ranging from 40 to 65 days.¹

Nintedanib had significant effects vs placebo on changes in QILD score and e-Lung TDE at week 24 (relative difference, –7% [95% CI, –11% to –2%]; P = .005 and –8% [95% CI, –12% to –4%]; P < .001, respectively) and at week 52 (–7% [95% CI, –12% to –2%]; P < .05 for both).¹

The authors noted several limitations: HRCT scans at weeks 24 and 52 were only available from the substudy, the sample was too small to assess subgroups by individual ILD diagnosis, and the results apply only to the INBUILD population, which had a fibrosis extent greater than 10% on HRCT.

“Data from the INBUILD trial in patients with PPF suggest that quantitative CT
methods can facilitate the prediction of clinically relevant outcomes and be used to assess the efficacy of drugs in clinical trials,” the study authors concluded.

References

1. Devaraj A, George PM, Joly O, et al. Quantitative computed tomography in progressive pulmonary fibrosis: data from a sub-study of the double-blind, randomized, placebo-controlled INBUILD trial. Am J Respir Crit Care Med. Published online 2026. doi:10.1093/ajrccm/aamag526

2. McCrear S. CPFE: why spirometry alone misses emphysema in fibrotic ILD. AJMC®. August 31, 2026. Accessed October 12, 2026. https://www.ajmc.com/view/cpfe-why-spirometry-alone-misses-emphysema-in-fibrotic-ild

3. Flaherty KR, Wells AU, Cottin V, et al. Nintedanib in progressive fibrosing interstitial lung diseases. N Engl J Med. 2019;381(18):1718-1727. doi:10.1056/NEJMoa1908681