Abstract：
OBJECTIVE: 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.
CONCLUSIONS: Deep learning-based prognostication from chest radiographs offers comparable-to-higher prognostic performance than forced vital capacity.
CONCLUSIONS: • 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.
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.
CONCLUSIONS: Deep learning-based prognostication from chest radiographs offers comparable-to-higher prognostic performance than forced vital capacity.
CONCLUSIONS: • 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.
摘要：
目的：使用胸部X光片开发并验证基于深度学习的特发性肺纤维化（IPF）患者预后模型。
方法：为了使用胸部X光片（DLPM）开发基于深度学习的预后模型，回顾性收集2011-2021年诊断为IPF的患者，并将其分为训练组(n=1007)，验证(n=117)，和内部测试(n=187)数据集。每位患者包括多达10张连续的X射线照片。对于外部测试，我们收集了来自独立机构的3个队列(n=152,141和207).使用3年生存期的时间依赖性接收器工作特征曲线（TD-AUC）下的面积评估了DLPM的辨别性能，并将其与强迫肺活量（FVC）进行了比较。进行多变量Cox回归以研究DLPM是否是FVC的独立预后因素。我们设计了改良的性别-年龄-生理学(GAP)指数(GAP-CR)，通过用DLPM替换DLCO。
结果：在三个外部测试队列中，DLPM在预测3年生存率方面比FVC表现相似至更高的性能（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）。在所有三个队列中，DLPM都是FVC的独立预后因素（ps<0.001）。在三个外部测试队列中的两个中，GAP-CR指数显示3年TD-AUC高于原始GAP指数（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)。
结论：深度学习模型通过胸片成功预测了IPF患者的生存率，与FVC相当且独立于FVC。
结论：基于深度学习的胸部X光片预测与强制肺活量相比，提供了相当至更高的预后表现。
结论：•使用6063张X光片开发了基于深度学习的特发性肺纤维化预后模型。•该模型的预后表现与强迫性肺活量相当，并且在所有三个外部测试队列中独立于FVC。•修改后的性别-年龄-生理学指数用深度学习模型代替一氧化碳的扩散能力，在两个外部测试队列中显示出比原始指数更高的性能。
方法：为了使用胸部X光片（DLPM）开发基于深度学习的预后模型，回顾性收集2011-2021年诊断为IPF的患者，并将其分为训练组(n=1007)，验证(n=117)，和内部测试(n=187)数据集。每位患者包括多达10张连续的X射线照片。对于外部测试，我们收集了来自独立机构的3个队列(n=152,141和207).使用3年生存期的时间依赖性接收器工作特征曲线（TD-AUC）下的面积评估了DLPM的辨别性能，并将其与强迫肺活量（FVC）进行了比较。进行多变量Cox回归以研究DLPM是否是FVC的独立预后因素。我们设计了改良的性别-年龄-生理学(GAP)指数(GAP-CR)，通过用DLPM替换DLCO。
结果：在三个外部测试队列中，DLPM在预测3年生存率方面比FVC表现相似至更高的性能（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）。在所有三个队列中，DLPM都是FVC的独立预后因素（ps<0.001）。在三个外部测试队列中的两个中，GAP-CR指数显示3年TD-AUC高于原始GAP指数（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)。
结论：深度学习模型通过胸片成功预测了IPF患者的生存率，与FVC相当且独立于FVC。
结论：基于深度学习的胸部X光片预测与强制肺活量相比，提供了相当至更高的预后表现。
结论：•使用6063张X光片开发了基于深度学习的特发性肺纤维化预后模型。•该模型的预后表现与强迫性肺活量相当，并且在所有三个外部测试队列中独立于FVC。•修改后的性别-年龄-生理学指数用深度学习模型代替一氧化碳的扩散能力，在两个外部测试队列中显示出比原始指数更高的性能。
