Automated Computational Detection of Interstitial Fibrosis, Tubular Atrophy, and Glomerulosclerosis
Automated Computational Detection of Interstitial Fibrosis, Tubular Atrophy, and Glomerulosclerosis
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DOI：
10.1681/asn.2020050652
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发表时间：
2021-04-01
影响因子：
13.6
通讯作者：
Sarder, Pinaki
中科院分区：
文献类型：
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作者：
Ginley, Brandon;Jen, Kuang-Yu;Sarder, Pinaki
背景间质纤维化、肾小管萎缩（IFTA）和肾小球硬化是不可恢复性肾损伤的指标。现代机器学习（ML）工具已经实现了图像结构的鲁棒自动识别，可以与人类专家的分析相媲美。ML算法被开发和测试，以复制肾病理学家所做的IFTA和肾小球硬化的检测和定量。方法肾脏病理学家对116张IFTA和肾小球硬化的全切片（WSIs）肾活检标本进行注释。共有79个wsi用于训练卷积神经网络（CNN）的不同配置，其中17个wsi作为内部测试用例，20个wsi作为外部测试用例。将最佳模型与4名肾脏病理学家在20个新测试载玻片上的输入进行比较。此外，对于87例活检标本，病理学家和CNN测量的IFTA和肾小球硬化测量值使用经典统计工具与患者预后相关。结果在所有图像类别中，在40倍放大率下训练的aDeepLab版本2网络的平均性能最好。从CNN得出的IFTA和肾小球硬化百分比与四位肾脏病理学家的结论高度一致。病理学家和cnn对IFTA和肾小球硬化的分析显示，与所有患者结局变量的相关性具有统计学意义。结论训练ML算法可以复制肾病理学家进行的IFTA和肾小球硬化评估。这表明，在肾脏病理学时间有限或无法获得的情况下，计算方法可能能够提供一种标准化的方法来评估慢性肾损伤的程度。
Background Interstitial fibrosis, tubular atrophy (IFTA), and glomerulosclerosis are indicators of irrecoverable kidney injury. Modern machine learning (ML) tools have enabled robust, automated identification of image structures that can be comparable with analysis by human experts. ML algorithms were developed and tested for the ability to replicate the detection and quantification of IFTA and glomerulosclerosis that renal pathologists perform.Methods A renal pathologist annotated renal biopsy specimens from 116 whole-slide images (WSIs) for IFTA and glomerulosclerosis. A total of 79 WSIs were used for training different configurations of a convolutional neural network (CNN), and 17 and 20 WSIs were used as internal and external testing cases, respectively. The best model was compared against the input of four renal pathologists on 20 new testing slides. Further, for 87 testing biopsy specimens, IFTA and glomerulosclerosis measurements made by pathologists and the CNN were correlated to patient outcome using classic statistical tools.Results The best average performance across all image classes came from aDeepLab version 2 network trained at 40x magnification. IFTA and glomerulosclerosis percentages derived from this CNN achieved high levels of agreement with four renal pathologists. The pathologist- and CNN-based analyses of IFTA and glomerulosclerosis showed statistically significant and equivalent correlation with all patient-outcome variables.Conclusions ML algorithms can be trained to replicate the IFTA and glomerulosclerosis assessment performed by renal pathologists. This suggests computational methods may be able to provide a standardized approach to evaluate the extent of chronic kidney injury in situations in which renal-pathologist time is restricted or unavailable.