Deep Learning Classification of Usual Interstitial Pneumonia Predicts Outcomes

Abstract

<b>Rationale:</b> Computed tomography (CT) enables noninvasive diagnosis of usual interstitial pneumonia (UIP), but enhanced image analyses are needed to overcome the limitations of visual assessment. <b>Objectives:</b> Apply multiple instance learning (MIL) to develop an explainable deep learning algorithm for prediction of UIP from CT and validate its performance in independent cohorts. <b>Methods:</b> We trained an MIL algorithm using a pooled dataset (<i>n</i> = 2,143) and tested it in three independent populations: data from a prior publication (<i>n</i> = 127), a single-institution clinical cohort (<i>n</i> = 239), and a national registry of patients with pulmonary fibrosis (<i>n</i> = 979). We tested UIP classification performance using receiver operating characteristic analysis, with histologic UIP as ground truth. Cox proportional hazards and linear mixed-effects models were used to examine associations between MIL predictions and survival or longitudinal FVC. <b>Measurements and Main Results:</b> In two cohorts with biopsy data, MIL improved accuracy for histologic UIP (area under the curve, 0.77 [<i>n</i> = 127] and 0.79 [<i>n</i> = 239]) compared with visual assessment (area under the curve, 0.65 and 0.71). In cohorts with survival data, MIL-UIP classifications were significant for mortality (<i>n</i> = 239, mortality to April 2021: unadjusted hazard ratio, 3.1; 95% confidence interval [CI], 1.96-4.91; <i>P</i> < 0.001; and <i>n</i> = 979, mortality to July 2022: unadjusted hazard ratio, 3.64; 95% CI, 2.66-4.97; <i>P</i> < 0.001). Individuals classified as UIP positive by the algorithm had a significantly greater annual decline in FVC than those classified as UIP negative (-88 ml/yr vs. -45 ml/yr; <i>n</i> = 979; <i>P</i> < 0.01), adjusting for extent of lung fibrosis. <b>Conclusions:</b> Computerized assessment using MIL identifies clinically significant features of UIP on CT. Such a method could improve confidence in radiologic assessment of patients with interstitial lung disease, potentially enabling earlier and more precise diagnosis.