DIFFUSE LUNG DISEASE: ORIGINAL RESEARCH| VOLUME 162, ISSUE 4, P815-823, OCTOBER 01, 2022

Predicting Usual Interstitial Pneumonia Histopathology From Chest CT Imaging With Deep Learning

Alex Bratt, James M. Williams, Grace Liu, MD 

Chest 2022; 162: 815-823 Published: April 08, 2022

DOI:https://doi.org/10.1016/j.chest.2022.03.044

Background

Idiopathic pulmonary fibrosis (IPF) is a progressive, often fatal form of interstitial lung disease (ILD) characterized by the absence of a known cause and usual interstitial pneumonitis (UIP) pattern on chest CT imaging and/or histopathology. Distinguishing UIP/IPF from other ILD subtypes is essential given different treatments and prognosis. Lung biopsy is necessary when noninvasive data are insufficient to render a confident diagnosis.

Research Question

Can we improve noninvasive diagnosis of UIP be improved by predicting ILD histopathology from CT scans by using deep learning?

Study Design and Methods

This study retrospectively identified a cohort of 1,239 patients in a multicenter database with pathologically proven ILD who had chest CT imaging. Each case was assigned a label based on histopathologic diagnosis (UIP or non-UIP). A custom deep learning model was trained to predict class labels from CT images (training set, n = 894) and was evaluated on a 198-patient test set. Separately, two subspecialty-trained radiologists manually labeled each CT scan in the test set according to the 2018 American Thoracic Society IPF guidelines. The performance of the model in predicting histopathologic class was compared against radiologists’ performance by using area under the receiver-operating characteristic curve as the primary metric. Deep learning model reproducibility was compared against intra-rater and inter-rater radiologist reproducibility.

Results

For the entire cohort, mean patient age was 62 ± 12 years, and 605 patients were female (49%). Deep learning performance was superior to visual analysis in predicting histopathologic diagnosis (area under the receiver-operating characteristic curve, 0.87 vs 0.80, respectively; P < .05). Deep learning model reproducibility was significantly greater than radiologist inter-rater and intra-rater reproducibility (95% CI for difference in Krippendorff’s alpha did not include zero).

Interpretation

Deep learning may be superior to visual assessment in predicting UIP/IPF histopathology from CT imaging and may serve as an alternative to invasive lung biopsy.