Artificial intelligence identifies inflammation and confirms fibroblast foci as prognostic tissue biomarkers in idiopathic pulmonary fibrosis
Artificial intelligence identifies inflammation and confirms fibroblast foci as prognostic tissue biomarkers in idiopathic pulmonary fibrosis
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DOI：
10.1016/j.humpath.2020.10.008
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发表时间：
2021-01-01
期刊：
影响因子：
3.3
通讯作者：
Myllarniemi, Marjukka
中科院分区：
文献类型：
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作者：
Makela, Kati;Mayranpaa, Mikko I.;Myllarniemi, Marjukka
在特发性肺纤维化(IPF)中，大量的成纤维细胞病灶(FF)可预测死亡率。其他预后组织学标记物尚未确定。人工智能(AI)提供了量化IPF可能的预后组织学特征的可能性。我们的目的是通过使用深度卷积神经网络(CNN)对肺组织中的FF、间质单核炎症和肺泡内巨噬细胞进行定量来测试AI在IPF肺组织样本中的应用。来自芬兰IPF登记的71名IPF患者的肺组织样本通过在Aiforia(R)平台上开发的人工智能模型进行了分析。该模型被训练来检测20个样本的组织、气隙、FF、间质单核炎症和肺泡内巨噬细胞。对于生存分析，组织学参数的高值和低值的切点值通过最大限度地选择等级统计来确定。生存分析采用Kaplan-Meier方法。大面积FF预示IPF预后不良(p=0.01)。肺间质单个核炎性细胞和肺泡内巨噬细胞数量增多与存活时间延长有关(p=0.01和p=0.01)。在肺功能值中，一氧化碳弥散量低与肺泡巨噬细胞密度高相关(p=0.03)，用力肺活量预测值高与肺泡内巨噬细胞密度高相关(p=0.03)。深层CNN检测到了人工难以量化的组织学特征。间质单个核炎症和肺泡内巨噬细胞是预测IPF预后的新的组织学标志物。用AI评估组织学特征为IPF的预后评估提供了新的信息。(C)2020作者。由爱思唯尔公司出版。
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 (R) 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. (C) 2020 The Authors. Published by Elsevier Inc.