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May 20, 2026American Journal of Respiratory and Critical Care Medicine0 citations

B23-25 FAPI PET/CT for Severity Assessment and Acute Exacerbation Prediction in Fibrotic Interstitial Lung Disease: A Prospective Observational Pilot Study

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ZWZ WangJLJ LinlinLYL Ying

Key Points

  • This study aimed to evaluate the effectiveness of FAPI PET/CT in assessing disease severity and predicting acute exacerbations in fibrotic interstitial lung disease (f-ILD).
  • Patients underwent FAPI PET/CT, HRCT, pulmonary function tests, 6-minute walk tests, and serum KL-6 assessments.
  • Quantitative analysis of FAPI PET/CT parameters (SUVmax, SUVmean, MAV, SUVtotal) and HRCT was performed.
  • Follow-up monitored for acute exacerbations (AEs) of f-ILD.
  • No significant differences in FAPI SUVmax and SUVmean among f-ILD subtypes were found, except higher values in IPF and unclassifiable f-ILD than CTD-f-ILD (P < 0.05).
  • Moderate correlations exist between FAPI MAV/SUVtotal and fibrosis score from HRCT (R = 0.454, P < 0.001).
  • Combining fibrosis score, FAPI MAV, SUVtotal, and KL-6 yielded the highest diagnostic accuracy for predicting AEs (AUC: 0.884, sensitivity: 0.73, specificity: 1.00).

Abstract

Abstract Rationale Fibrotic interstitial lung diseases (f-ILD) encompass several distinct clinical entities that share pathologic hallmarks of lung injury, inflammation, and fibrosis. Fibroblast activation protein inhibitor (FAPI) PET/CT has emerged as a potential molecular imaging technique; however, its role in f-ILD remains unclear. This study aimed to assess the utility of FAPI PET/CT in evaluating disease severity, differentiating subtypes, and predicting AE. Methods Patients were recruited from Ningxia Medical University’s General Hospital. All participants underwent FAPI PET/CT, HRCT, pulmonary function tests, 6-minute walk tests(6MWT), and serum KL-6 at baseline. The parameters of FAPI PET/CT were quantitatively analyzed, which including maximum and mean standardized uptake values (SUVmax, SUVmean), metabolic active volume (MAV), and total uptake (SUVtotal) of lung regions. Meanwhile, Quantitative analysis of HRCT images was performed using AVIEW software. AE of f-ILD were recorded during follow-up. Results 90 f-ILD patients and 28 healthy controls were enrolled in study. Etiological classification of f-ILD participants yielded three subtypes: 59 idiopathic pulmonary fibrosis (IPF), 22 connective tissue disease-associated f-ILD (CTD-f-ILD), and 9 unclassifiable f-ILD. No statistically significant differences were observed in FAPI SUVmax and SUVmean among subtypes of f-ILD. Compared with the CTD-ILD, significantly elevated FAPI-MAV and SUVtotal values were observed in IPF and unclassifiable f-ILD (P 0.05). Moderate correlations were observed between FAPI MAV and SUVtotal and fibrosis score of quantitative analysis of HRCT (FAPI MAV: R = 0.454, P 0.001; SUVtotal: R = 0.426, P 0.001, respectively). During follow-up period, 32 patients experienced AE. Baseline FVC%, TLC, TLC%, KL-6, fibrosis score, FAPI-MAV and SUVtotal differed significantly between AE and non-AE groups (P ≤ 0.05). For AE prediction, FAPI MAV (cc) and SUVtotal showed AUC values of 0.631 (95% CI: 0.51-0.75) and 0.623 (95% CI: 0.50-0.74), respectively. FAPI MAV and SUVtotal provided the highest specificity (0.96, 1, respectively) and positive predictive value (PPV: 0.95,1 respectively) but lower sensitivity (0.31, 0.28, respectively) for predicting AE-f-ILD (AUC: 0.621, 0.623, respectively). The fibrosis score and KL-6 showed moderate diagnostic value, with AUCs of 0.701 (95% CI: 0.59-0.81) and 0.730 (95% CI: 0.59-0.81), and sensitivities of 0.81 and 0.76, respectively. The combination of fibrosis score, FAPI MAV, SUVtotal, and KL-6 achieved the highest diagnostic accuracy, with an AUC of 0.884 (95% CI: 0.768-1), sensitivity of 0.73, and specificity of 1.00. Conclusion Our research indicates that FAPI PET/CT is a useful tool for evaluating the severity of f-ILD and distinguishing fILD subtypes. Furthermore, multiparametric evaluation significantly enhances the accuracy of predicting AE-f-ILD. This abstract is funded by: None

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Wang et al. (2026) studied this question.

synapsesocial.com/papers/6a0d4f19f03e14405aa9a441https://doi.org/10.1093/ajrccm/aamag162.2462
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