@article{Thillai:2024:10.1164/rccm.202311-2185OC,
author = {Thillai, M and Oldham, JM and Ruggiero, A and Kanavati, F and McLellan, T and Saini, G and Johnson, SR and Ble, F-X and Azim, A and Ostridge, K and Platt, A and Belvisi, M and Maher, TM and Molyneaux, PL},
doi = {10.1164/rccm.202311-2185OC},
journal = {Am J Respir Crit Care Med},
pages = {465--472},
title = {Deep Learning-based Segmentation of Computed Tomography Scans Predicts Disease Progression and Mortality in Idiopathic Pulmonary Fibrosis.},
url = {http://dx.doi.org/10.1164/rccm.202311-2185OC},
volume = {210},
year = {2024}
}

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TY  - JOUR
AB  - Rationale: Despite evidence demonstrating a prognostic role for computed tomography (CT) scans in idiopathic pulmonary fibrosis (IPF), image-based biomarkers are not routinely used in clinical practice or trials. Objectives: To develop automated imaging biomarkers using deep learning-based segmentation of CT scans. Methods: We developed segmentation processes for four anatomical biomarkers, which were applied to a unique cohort of treatment-naive patients with IPF enrolled in the PROFILE (Prospective Observation of Fibrosis in the Lung Clinical Endpoints) study and tested against a further United Kingdom cohort. The relationships among CT biomarkers, lung function, disease progression, and mortality were assessed. Measurements and Main Results: Data from 446 PROFILE patients were analyzed. Median follow-up duration was 39.1 months (interquartile range, 18.1-66.4 mo), with a cumulative incidence of death of 277 (62.1%) over 5 years. Segmentation was successful on 97.8% of all scans, across multiple imaging vendors, at slice thicknesses of 0.5-5 mm. Of four segmentations, lung volume showed the strongest correlation with FVC (r = 0.82; P < 0.001). Lung, vascular, and fibrosis volumes were consistently associated across cohorts with differential 5-year survival, which persisted after adjustment for baseline gender, age, and physiology score. Lower lung volume (hazard ratio [HR], 0.98 [95% confidence interval (CI), 0.96-0.99]; P = 0.001), increased vascular volume (HR, 1.30 [95% CI, 1.12-1.51]; P = 0.001), and increased fibrosis volume (HR, 1.17 [95% CI, 1.12-1.22]; P < 0.001) were associated with reduced 2-year progression-free survival in the pooled PROFILE cohort. Longitudinally, decreasing lung volume (HR, 3.41 [95% CI, 1.36-8.54]; P = 0.009) and increasing fibrosis volume (HR, 2.23 [95% CI, 1.22-4.08]; P = 0.009) were associated with differen
AU  - Thillai,M
AU  - Oldham,JM
AU  - Ruggiero,A
AU  - Kanavati,F
AU  - McLellan,T
AU  - Saini,G
AU  - Johnson,SR
AU  - Ble,F-X
AU  - Azim,A
AU  - Ostridge,K
AU  - Platt,A
AU  - Belvisi,M
AU  - Maher,TM
AU  - Molyneaux,PL
DO  - 10.1164/rccm.202311-2185OC
EP  - 472
PY  - 2024///
SP  - 465
TI  - Deep Learning-based Segmentation of Computed Tomography Scans Predicts Disease Progression and Mortality in Idiopathic Pulmonary Fibrosis.
T2  - Am J Respir Crit Care Med
UR  - http://dx.doi.org/10.1164/rccm.202311-2185OC
UR  - https://www.ncbi.nlm.nih.gov/pubmed/38452227
VL  - 210
ER  - 

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