Wu Xuening, Yin Chengsheng, Chen Xianqiu, Zhang Yuan, Su Yiliang, Shi Jingyun, Weng Dong, Jiang Xing, Zhang Aihong, Zhang Wenqiang, Li Huiping
The Academy for Engineering and Technology, Fudan University, Shanghai, China.
Department of Respiratory Medicine, Shanghai Pulmonary Hospital, Tongji University, School of Medicine, Shanghai, China.
Front Pharmacol. 2022 Apr 26;13:878764. doi: 10.3389/fphar.2022.878764. eCollection 2022.
Idiopathic pulmonary fibrosis (IPF) needs a precise prediction method for its prognosis. This study took advantage of artificial intelligence (AI) deep learning to develop a new mortality risk prediction model for IPF patients. We established an artificial intelligence honeycomb segmentation system that segmented the honeycomb tissue area automatically from 102 manually labeled (by radiologists) cases of IPF patients' CT images. The percentage of honeycomb in the lung was calculated as the CT fibrosis score (CTS). The severity of the patients was evaluated by pulmonary function and physiological feature (PF) parameters (including FVC%pred, DLco%pred, SpO2%, age, and gender). Another 206 IPF cases were randomly divided into a training set ( = 165) and a verification set ( = 41) to calculate the fibrosis percentage in each case by the AI system mentioned previously. Then, using a competing risk (Fine-Gray) proportional hazards model, a risk score model was created according to the training set's patient data and used the validation data set to validate this model. The final risk prediction model (CTPF) was established, and it included the CT stages and the PF (pulmonary function and physiological features) grades. The CT stages were defined into three stages: stage I (CTS≤5), stage II (5 < CTS<25), and stage III (≥25). The PF grades were classified into mild (a, 0-3 points), moderate (b, 4-6 points), and severe (c, 7-10 points). The AUC index and Briers scores at 1, 2, and 3 years in the training set were as follows: 74.3 [63.2,85.4], 8.6 [2.4,14.8]; 78 [70.2,85.9], 16.0 [10.1,22.0]; and 72.8 [58.3,87.3], 18.2 [11.9,24.6]. The results of the validation sets were similar and suggested that high-risk patients had significantly higher mortality rates. This CTPF model with AI technology can predict mortality risk in IPF precisely.
特发性肺纤维化（IPF）需要一种精确的预后预测方法。本研究利用人工智能（AI）深度学习技术为IPF患者开发了一种新的死亡风险预测模型。我们建立了一个人工智能蜂窝分割系统，该系统能从102例由放射科医生手动标注的IPF患者CT图像中自动分割出蜂窝组织区域。计算肺内蜂窝组织的百分比作为CT纤维化评分（CTS）。通过肺功能和生理特征（PF）参数（包括预测FVC%、预测DLco%、SpO2%、年龄和性别）评估患者的严重程度。另外206例IPF病例被随机分为训练集（=165）和验证集（=41），通过上述AI系统计算每例患者的纤维化百分比。然后，使用竞争风险（Fine-Gray）比例风险模型，根据训练集患者数据创建风险评分模型，并使用验证数据集对该模型进行验证。最终建立了风险预测模型（CTPF），它包括CT分期和PF（肺功能和生理特征）分级。CT分期分为三个阶段：I期（CTS≤5）、II期（5 < CTS<25）和III期（≥25）。PF分级分为轻度（a，0-3分）、中度（b，4-6分）和重度(c，7-10分）。训练集1年、2年和3年的AUC指数和Briers评分如下：74.3 [63.2,85.4]，8.6 [2.4,14.8]；78 [70.2,85.9]，16.0 [10.1,22.0]；72.8 [58.3,8,7.3]，18.2 [11.9,24.6]。验证集的结果相似，提示高危患者的死亡率显著更高。这种采用AI技术的CTPF模型能够精确预测IPF患者的死亡风险。
