Idiopathic Pulmonary Fibrosis Mortality Risk Prediction Based on Artificial Intelligence: The CTPF Model

Xuening Wu; Chengsheng Yin; Xianqiu Chen; Yuan Zhang; Yiliang Su; Jingyun Shi; Dong Weng; Xing Jiang; Aihong Zhang; Wenqiang Zhang; Huiping Li

doi:10.3389/fphar.2022.878764

Idiopathic Pulmonary Fibrosis Mortality Risk Prediction Based on Artificial Intelligence: The CTPF Model

Front Pharmacol. 2022 Apr 26:13:878764. doi: 10.3389/fphar.2022.878764. eCollection 2022.

Authors

Xuening Wu¹, Chengsheng Yin^{2

3}, Xianqiu Chen², Yuan Zhang², Yiliang Su², Jingyun Shi⁴, Dong Weng², Xing Jiang², Aihong Zhang⁵, Wenqiang Zhang¹, Huiping Li²

Affiliations

¹ The Academy for Engineering and Technology, Fudan University, Shanghai, China.
² Department of Respiratory Medicine, Shanghai Pulmonary Hospital, Tongji University, School of Medicine, Shanghai, China.
³ Department of Pulmonary and Critical Care Medicine, Yijishan Hospital of Wannan Medical College, Wuhu, China.
⁴ Department of Radiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
⁵ Department of Medical Statistics, School of Medicine, Tongji University, Shanghai, China.

Abstract

Background: 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. Methods: 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 (n = 165) and a verification set (n = 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. Result: 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. Conclusion: This CTPF model with AI technology can predict mortality risk in IPF precisely.

Keywords: artificial intelligence (AI); deep learning; disease severity grade; idiopathic pulmonary fibrosis (IPF); pulmonary fibrosis stage; semantic segmentation.