| Autor: |
Humphries SM; Department of Radiology., Thieke D; Department of Radiology., Baraghoshi D; Division of Biostatistics, and., Strand MJ; Division of Biostatistics, and., Swigris JJ; Division of Pulmonary and Critical Care Medicine, National Jewish Health, Denver, Colorado., Chae KJ; Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, South Korea., Hwang HJ; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea., Oh AS; Department of Radiology, University of California Los Angeles, Los Angeles, California., Flaherty KR; Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, Michigan., Adegunsoye A; Section of Pulmonary and Critical Care, Department of Medicine., Jablonski R; Section of Pulmonary and Critical Care, Department of Medicine., Lee CT; Section of Pulmonary and Critical Care, Department of Medicine., Husain AN; Department of Pathology, The University of Chicago, Chicago, Illinois., Chung JH; Department of Radiology, and., Strek ME; Section of Pulmonary and Critical Care, Department of Medicine., Lynch DA; Department of Radiology. |
| Abstrakt: |
Rationale: Computed tomography (CT) enables noninvasive diagnosis of usual interstitial pneumonia (UIP), but enhanced image analyses are needed to overcome the limitations of visual assessment. Objectives: 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. Methods: We trained an MIL algorithm using a pooled dataset ( n = 2,143) and tested it in three independent populations: data from a prior publication ( n = 127), a single-institution clinical cohort ( n = 239), and a national registry of patients with pulmonary fibrosis ( n = 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. Measurements and Main Results: In two cohorts with biopsy data, MIL improved accuracy for histologic UIP (area under the curve, 0.77 [ n = 127] and 0.79 [ n = 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 ( n = 239, mortality to April 2021: unadjusted hazard ratio, 3.1; 95% confidence interval [CI], 1.96-4.91; P < 0.001; and n = 979, mortality to July 2022: unadjusted hazard ratio, 3.64; 95% CI, 2.66-4.97; P < 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; n = 979; P < 0.01), adjusting for extent of lung fibrosis. Conclusions: 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. |
