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김종수Jong-Su Kim
서울대학교병원
Radiology, May 2025, 315 (2) | https://doi.org/10.1148/radiol.243463
Association of Deep Learning-based Chest CT-derived Respiratory Parameters with Disease Progression in Amyotrophic Lateral Sclerosis
Seok-Jin Choi*,1,2, Jong-Su Kim*,3, Seong Yun Jeong4, Hyeryeon Son4, Jung-Joon Sung1,5, Chang-Min Park4,5, Kyu Sung Choi4,5
1Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
2Center for Hospital Medicine, Seoul National University Hospital, Seoul, Republic of Korea
3Department of Translational Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
4Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
5Department of Radiology, Seoul National University College of Medicine, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea
* S.J.C. and J.S.K. contributed equally to this work.
Address correspondence to: K.S.C.
1Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
2Center for Hospital Medicine, Seoul National University Hospital, Seoul, Republic of Korea
3Department of Translational Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
4Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
5Department of Radiology, Seoul National University College of Medicine, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea
* S.J.C. and J.S.K. contributed equally to this work.
Address correspondence to: K.S.C.
Abstract
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.
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.
논문정보
- Research article
- 2025년 05월 (BRIC 등록일 2025-05-14)
- 국내 연구진
- 의약학 > 종양의학
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