Lucarelli Nicholas, Yun Donghwan, Han Dohyun, Ginley Brandon, Moon Kyung Chul, Rosenberg Avi, Tomaszewski John, Han Seung Seok, Sarder Pinaki
Department of Pathology and Anatomical Sciences, University at Buffalo - The State University of New York, Buffalo, New York.
Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
Proc SPIE Int Soc Opt Eng. 2022 Feb-Mar;12039. doi: 10.1117/12.2613500. Epub 2022 Apr 4.
Histological image data and molecular profiles provide context into renal condition. Often, a biopsy is drawn to diagnose or monitor a suspected kidney problem. However, molecular profiles can go beyond a pathologist's ability to see and diagnose. Using AI, we computationally incorporated urinary proteomic profiles with microstructural morphology from renal biopsy to investigate new and existing molecular links to image phenotypes. We studied whole slide images of periodic acid-Schiff stained renal biopsies from 56 DN patients matched with 2,038 proteins measured from each patient's urine. Using Seurat, we identified differentially expressed proteins in patients that developed end-stage renal disease within 2 years of biopsy. Glomeruli, globally sclerotic glomeruli, and tubules were segmented from WSI using our previously published HAIL pipeline. For each glomerulus, 315 handcrafted digital image features were measured, and for tubules, 207 features. We trained fully connected networks to predict urinary protein measurements that were differentially expressed between patients who did/ did not progress to ESRD within 2 years of biopsy. The input to this network was either glomerular or tubular histomorphological features in biopsy. Trained network weights were used as a proxy to rank which morphological features correlated most highly with specific urinary proteins. We identified significant image feature-protein pairs by ranking network weights by magnitude. We also looked at which features on average were most significant in predicting proteins. For both glomeruli and tubules, RGB color values and variance in PAS areas (specifically basement membrane for tubules) were, on average, more predictive of molecular profiles than other features. There is a strong connection between molecular profile and image phenotype, which can be elucidated through computational methods. These discovered links can provide insight to disease pathways, and discover new factors contributing to incidence and progression.
组织学图像数据和分子图谱为肾脏状况提供了背景信息。通常，会进行活检以诊断或监测疑似肾脏问题。然而，分子图谱可能超出病理学家的观察和诊断能力。利用人工智能，我们通过计算将尿蛋白质组图谱与肾活检的微观结构形态相结合，以研究与图像表型新的和现有的分子联系。我们研究了56例糖尿病肾病（DN）患者的高碘酸-希夫（PAS）染色肾活检全切片图像，并匹配了每位患者尿液中检测的2038种蛋白质。使用Seurat软件，我们确定了在活检后2年内发展为终末期肾病的患者中差异表达的蛋白质。使用我们之前发表的HAIL管道从全切片图像中分割出肾小球、全局硬化肾小球和肾小管。对于每个肾小球，测量了315个手工制作的数字图像特征，对于肾小管，测量了207个特征。我们训练了全连接网络来预测在活检后2年内进展/未进展为终末期肾病的患者之间差异表达的尿蛋白测量值。该网络的输入是活检中的肾小球或肾小管组织形态学特征。训练后的网络权重被用作代理，对与特定尿蛋白相关性最高的形态学特征进行排名。我们通过按大小对网络权重进行排名来确定显著的图像特征-蛋白质对。我们还研究了哪些特征在预测蛋白质方面平均最显著。对于肾小球和肾小管，RGB颜色值和PAS区域的方差（特别是肾小管的基底膜）平均比其他特征更能预测分子图谱。分子图谱与图像表型之间存在紧密联系，这可以通过计算方法来阐明。这些发现的联系可以为疾病途径提供见解，并发现导致发病率和进展的新因素。
