Unsupervised 3D prediction model for pulmonary disease progression in chest CT

Janne J. Näppi; Tomoki Uemura; Chinatsu Watari; Toru Hironaka; Hiroyuki Yoshida

doi:10.1117/12.2612375

4 April 2022 Unsupervised 3D prediction model for pulmonary disease progression in chest CT

Janne J. Näppi, Tomoki Uemura, Chinatsu Watari, Toru Hironaka, Hiroyuki Yoshida

Proceedings Volume 12037, Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications; 120370O (2022) https://doi.org/10.1117/12.2612375
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

We developed a 3D-image-based unsupervised prediction model, called vox2pred, for predicting the progression of pulmonary diseases based on a conditional generative adversarial network (cGAN). The architecture of the vox2pred model includes a time generator that consists of an encoding convolutional network and a fully connected prediction network, and a discriminator network. The time generator is trained to generate the progression time from the chest 3D CT image volumes of each patient. The discriminator is a patch-based 3D-convolutional network that is trained to differentiate between “predicted pairs” of a chest CT image volume and a predicted progression time from “true pairs” of the chest CT image volume and the corresponding observed progression time of the patient. For a pilot evaluation, we retrospectively collected high-resolution chest CT images of 141 patients with the coronavirus disease 2019 (COVID-19). The progression predictions of the vox2pred model on these patients were compared with those of existing clinical prognostic biomarkers by use of a two-sided t-test with bootstrapping. Concordance index (C-index) and relative absolute error (RAE) were used as measures of the prediction performance. The bootstrap evaluation yielded C-index and RAE values of 87.4% and 18.5% for the vox2pred model, whereas those for the visual assessment of the CT images in terms of a total severity score were 62.4% and 51.8%, and for the total severity score for crazy paving and consolidation (CPC), they were 64.7% and 51.3%, respectively. The increase in the accuracy of the progression prediction by the vox2pred model was statistically significant (p < 0.0001), indicating the high effectiveness of vox2pred as a prediction model for pulmonary disease progression in chest CT.

Conference Presentation

Citation Download Citation

Janne J. Näppi, Tomoki Uemura, Chinatsu Watari, Toru Hironaka, and Hiroyuki Yoshida "Unsupervised 3D prediction model for pulmonary disease progression in chest CT", Proc. SPIE 12037, Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications, 120370O (4 April 2022); https://doi.org/10.1117/12.2612375

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