Abstract
A large number of fibroblast foci (FF) predict mortality in idiopathic pulmonary fibrosis (IPF). Other prognostic histological markers have not been identified. Artificial intelligence (AI) offers a possibility to quantitate possible prognostic histological features in IPF. We aimed to test the use of AI in IPF lung tissue samples by quantitating FF, interstitial mononuclear inflammation, and intra-alveolar macrophages with a deep convolutional neural network (CNN). Lung tissue samples of 71 patients with IPF from the FinnishIPF registry were analyzed by an AI model developed in the Aiforia® platform. The model was trained to detect tissue, air spaces, FF, interstitial mononuclear inflammation, and intra-alveolar macrophages with 20 samples. For survival analysis, cut-point values for high and low values of histological parameters were determined with maximally selected rank statistics. Survival was analyzed using the Kaplan-Meier method. A large area of FF predicted poor prognosis in IPF (p = 0.01). High numbers of interstitial mononuclear inflammatory cells and intra-alveolar macrophages were associated with prolonged survival (p = 0.01 and p = 0.01, respectively). Of lung function values, low diffusing capacity for carbon monoxide was connected to a high density of FF (p = 0.03) and a high forced vital capacity of predicted was associated with a high intra-alveolar macrophage density (p = 0.03). The deep CNN detected histological features that are difficult to quantitate manually. Interstitial mononuclear inflammation and intra-alveolar macrophages were novel prognostic histological biomarkers in IPF. Evaluating histological features with AI provides novel information on the prognostic estimation of IPF.
Original language | English |
---|---|
Journal | Human Pathology |
Volume | 107 |
Pages (from-to) | 58-68 |
Number of pages | 11 |
ISSN | 0046-8177 |
DOIs | |
Publication status | Published - Jan 2021 |
MoE publication type | A1 Journal article-refereed |
Fields of Science
- Idiopathic pulmonary fibrosis
- Usual interstitial pneumonia
- Inflammation
- Fibroblast focus
- Artificial intelligence
- Deep neural network
- USUAL INTERSTITIAL PNEUMONIA
- ORGANIZING PNEUMONIA
- HISTOLOGIC FEATURES
- DIAGNOSIS
- SURVIVAL
- LESIONS
- LUNGS
- 3111 Biomedicine