Artificial intelligence identifies inflammation and confirms fibroblast foci as prognostic tissue biomarkers in idiopathic pulmonary fibrosis

Kati Mäkelä, Mikko I. Mäyränpää, Hanna-Kaisa Sihvo, Paula Bergman, Eva Sutinen, Hely Ollila, Riitta Kaarteenaho, Marjukka Myllärniemi

Research output: Contribution to journalArticleScientificpeer-review

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 languageEnglish
JournalHuman Pathology
Volume107
Pages (from-to)58-68
Number of pages11
ISSN0046-8177
DOIs
Publication statusPublished - Jan 2021
MoE publication typeA1 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

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