Deep learning-based prognostication in idiopathic pulmonary fibrosis using chest radiographs.
Eur Radiol
; 34(7): 4206-4217, 2024 Jul.
Article
en En
| MEDLINE
| ID: mdl-38112764
ABSTRACT
OBJECTIVES:
To develop and validate a deep learning-based prognostic model in patients with idiopathic pulmonary fibrosis (IPF) using chest radiographs.METHODS:
To develop a deep learning-based prognostic model using chest radiographs (DLPM), the patients diagnosed with IPF during 2011-2021 were retrospectively collected and were divided into training (n = 1007), validation (n = 117), and internal test (n = 187) datasets. Up to 10 consecutive radiographs were included for each patient. For external testing, three cohorts from independent institutions were collected (n = 152, 141, and 207). The discrimination performance of DLPM was evaluated using areas under the time-dependent receiver operating characteristic curves (TD-AUCs) for 3-year survival and compared with that of forced vital capacity (FVC). Multivariable Cox regression was performed to investigate whether the DLPM was an independent prognostic factor from FVC. We devised a modified gender-age-physiology (GAP) index (GAP-CR), by replacing DLCO with DLPM.RESULTS:
DLPM showed similar-to-higher performance at predicting 3-year survival than FVC in three external test cohorts (TD-AUC 0.83 [95% CI 0.76-0.90] vs. 0.68 [0.59-0.77], p < 0.001; 0.76 [0.68-0.85] vs. 0.70 [0.60-0.80], p = 0.21; 0.79 [0.72-0.86] vs. 0.76 [0.69-0.83], p = 0.41). DLPM worked as an independent prognostic factor from FVC in all three cohorts (ps < 0.001). The GAP-CR index showed a higher 3-year TD-AUC than the original GAP index in two of the three external test cohorts (TD-AUC 0.85 [0.80-0.91] vs. 0.79 [0.72-0.86], p = 0.02; 0.72 [0.64-0.80] vs. 0.69 [0.61-0.78], p = 0.56; 0.76 [0.69-0.83] vs. 0.68 [0.60-0.76], p = 0.01).CONCLUSIONS:
A deep learning model successfully predicted survival in patients with IPF from chest radiographs, comparable to and independent of FVC. CLINICAL RELEVANCE STATEMENT Deep learning-based prognostication from chest radiographs offers comparable-to-higher prognostic performance than forced vital capacity. KEY POINTS ⢠A deep learning-based prognostic model for idiopathic pulmonary fibrosis was developed using 6063 radiographs. ⢠The prognostic performance of the model was comparable-to-higher than forced vital capacity, and was independent from FVC in all three external test cohorts. ⢠A modified gender-age-physiology index replacing diffusing capacity for carbon monoxide with the deep learning model showed higher performance than the original index in two external test cohorts.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Radiografía Torácica
/
Fibrosis Pulmonar Idiopática
/
Aprendizaje Profundo
Límite:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Año:
2024
Tipo del documento:
Article