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Prognostic value of a composite physiologic index developed by adding bronchial and hyperlucent volumes quantified via artificial intelligence technology. | LitMetric
Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Published: December 2024
AI Article Synopsis
Article Abstract
Background: The composite physiologic index (CPI) was developed to estimate the extent of interstitial lung disease (ILD) in idiopathic pulmonary fibrosis (IPF) patients based on pulmonary function tests (PFTs). The CALIPER-revised version of the CPI (CALIPER-CPI) was also developed to estimate the volume fraction of ILD measured by CALIPER, an automated quantitative CT postprocessing software. Recently, artificial intelligence-based quantitative CT image analysis software (AIQCT), which can be used to quantify the bronchial volume separately from the ILD volume, was developed and validated in IPF. The aim of this study was to develop AIQCT-derived CPI formulas to quantify CT abnormalities in IPF and to investigate the associations of these CPI formulas with survival.
Methods: The first cohort included 116 patients with IPF. In this cohort, ILD, bronchial, and hyperlucent volumes on CT were quantified using AIQCT. New CPI formulas were developed based on PFTs to estimate the volume fraction of ILD (ILD-CPI), the sum of the ILD and bronchial volume fractions (ILDB-CPI), and the sum of the ILD, bronchial and hyperlucent volume fractions (ILDBH-CPI). The associations of the original CPI, the CALIPER-CPI and the AIQCT-derived CPIs with survival were analyzed in the first cohort and in a second cohort of patients with IPF (n = 72).
Results: In the first cohort, over a median observation time of 92.8 months, 79 patients (68.1%) died, and one patient (0.9%) underwent living-donor lung transplantation. The original CPI, the CALIPER-CPI, and all AIQCT-derived CPIs were associated with overall survival (hazard ratios: 1.07-1.22). The C-index of the ILDB-CPI (0.759) was the highest among all AIQCT-derived CPIs and was comparable to that of the original CPI (0.765) and the CALIPER-CPI (0.749). The C-index of the ILDBH-CPI (0.729) was lower than that of the other CPI variables. The second cohort yielded similar C-indices as the first cohort for the original CPI (0.738), CALIPER-CPI (0.757) and ILDB-CPI (0.749).
Conclusions: The ILDB-CPI can predict the outcomes of IPF patients with a similar performance to that of the original CPI and the CALIPER-CPI. Adding the hyperlucent volume to the CPI formula did not improve its predictive accuracy for mortality.
Trial Registration: None (no health care interventions were performed).
