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

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http://hdl.handle.net/10138/328040

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Mäkelä , K , Mäyränpää , M I , Sihvo , H-K , Bergman , P , Sutinen , E , Ollila , H , Kaarteenaho , R & Myllärniemi , M 2021 , ' Artificial intelligence identifies inflammation and confirms fibroblast foci as prognostic tissue biomarkers in idiopathic pulmonary fibrosis ' , Human Pathology , vol. 107 , pp. 58-68 . https://doi.org/10.1016/j.humpath.2020.10.008

Title: Artificial intelligence identifies inflammation and confirms fibroblast foci as prognostic tissue biomarkers in idiopathic pulmonary fibrosis
Author: Mäkelä, Kati; Mäyränpää, Mikko I.; Sihvo, Hanna-Kaisa; Bergman, Paula; Sutinen, Eva; Ollila, Hely; Kaarteenaho, Riitta; Myllärniemi, Marjukka
Contributor: University of Helsinki, HUS Heart and Lung Center
University of Helsinki, HUSLAB
University of Helsinki, Aiforia Technologies Oy
University of Helsinki, Department of Public Health
University of Helsinki, HUS Heart and Lung Center
University of Helsinki, INDIVIDRUG - Individualized Drug Therapy
University of Helsinki, HUS Heart and Lung Center
Date: 2021-01
Language: eng
Number of pages: 11
Belongs to series: Human Pathology
ISSN: 0046-8177
URI: http://hdl.handle.net/10138/328040
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.
Subject: Idiopathic pulmonary fibrosis
Usual interstitial pneumonia
Inflammation
Fibroblast focus
Artificial intelligence
Deep neural network
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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