江苏大学学报（医学版）2026，Vol.36Issue(2)：154-159,6.DOI:10.13312/j.issn.1671-7783.y250128
基于深度学习的CT量化指标在间质性肺疾病进展预测中的临床价值
Clinical value of deep learning-based CT quantitative indexes in predicting the progression of interstitial lung disease
摘要
Abstract
Objective:To construct a quantitative index DL-ILD for interstitial lung disease(ILD)based on deep learning(DL),and to explore its clinical value in predicting the progression of ILD.Methods:A retrospective study was conducted on 112 patients with ILD.A three-dimensional U-Net(U-shaped convolutional neural network)model was constructed based on high resolution computerized tomography(HRCT)to automatically identify lesions and calculate DL-ILD.The evaluation criterion was whether ILD had progressed after a 24-month follow-up.The correlation between DL-ILD,visual scores,and lung function was assessed,and the predictive performance of the two methods for predicting progression and prognosis was compared.Logistic regression,receiver operating characteristic(ROC)curve analysis,Cox analysis,and five-fold cross-validation were used to validate the model.Results:Among the 112 ILD patients,42 experienced progression events.DL-ILD showed a negative correlation with forced vital capacity(FVC％)and diffusion capacity of the lung for carbon monoxide(DLCO％)(r=-0.762,r=-0.685,P＜0.001),outperforming visual scores(Z=2.593,P=0.010;Z=2.598,P=0.009).Logistic regression analysis indicated that DL-ILD was an independent predictor of ILD progression(OR=1.242,P=0.001),with its predictive performance(AUC=0.864)superior to that of visual scores(AUC=0.710).The 1-year and 2-year progression-free survival rates of the high DL-ILD group were 82.1％and 53.7％,respectively,which were significantly lower than 98.4％and 84.6％of the low DL-ILD group(P＜0.01),and the Cox model confirmed it as an independent risk factor for disease progression(HR=2.872,P=0.002).The AUC of the five-fold cross-validation was 0.833,indicating stable model performance.Conclusion:DL-ILD can accurately reflect the prediction of ILD disease progression,outperforming traditional visual scores,and has good reproducibility and clinical application potential.关键词
间质性肺病/深度学习/CT成像/病变指数/疾病进展预测Key words
interstitial lung disease/deep learning/CT imaging/lesion index/disease progression prediction分类
医药卫生引用本文复制引用
刘洋,李月峰,严玉兰,徐梦婷,岳静静..基于深度学习的CT量化指标在间质性肺疾病进展预测中的临床价值[J].江苏大学学报（医学版）,2026,36(2):154-159,6.基金项目
江苏省重点研发计划项目(BE2021693) （BE2021693）
深圳市医学研究专项资金资助项目(C2401017) （C2401017）
