Association of Deep Learning-based Chest CT-derived Respiratory Parameters with Disease Progression in Amyotrophic Lateral Sclerosis.

Background Forced vital capacity (FVC) is a standard measure of respiratory function in patients with amyotrophic lateral sclerosis (ALS) but has limitations, particularly for patients with bulbar impairment. Purpose To determine the value of deep learning-based chest CT-derived respiratory parameters in predicting ALS progression and survival. Materials and Methods This retrospective study included patients with ALS diagnosed between January 2010 and July 2023 who underwent chest CT at a tertiary hospital. Deep learning-based software was used to measure lung and respiratory muscle volume, normalized for height as the lung volume index (LVI) and respiratory muscle index (RMI). Differences in these parameters across King clinical stages were assessed using ordinal logistic regression. Tracheostomy-free survival was evaluated using Cox regression and time-dependent receiver operating characteristic analysis. Subgroup analysis was conducted for patients with bulbar impairment. In addition, a Gaussian process regressor model was developed to estimate FVC based on lung volume, respiratory muscle volume, age, and sex. Results A total of 261 patients were included in the study (mean age, 65.2 years ± 11.9 [SD]; 156 male patients). LVI and RMI decreased with increasing King stage (both P < .001). The high LVI and high RMI groups had better survival (both P < .001). After adjustment, LVI (hazard ratio [HR] = 0.998 [95% CI: 0.996, 1.000]; P = .021) and RMI (HR = 0.992 [95% CI: 0.988, 0.996]; P < .001) remained independent prognostic factors. In patients with bulbar impairment, LVI (HR = 0.998 [95% CI: 0.996, 1.000]; P = .029) and RMI (HR = 0.991 [95% CI: 0.987, 0.996]; P < .001) were independent prognostic factors. Time-dependent receiver operating characteristic curve analysis revealed no significant differences in survival prediction performance among LVI, RMI, and FVC. The Gaussian process regressor model estimated FVC with approximately 8% error. Conclusion The deep learning-derived CT metrics LVI and RMI reflected ALS stage, enabled FVC prediction, and supported assessment in patients with limited respiratory function. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Rahsepar and Bedayat in this issue.