Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, Ohio.
Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania.
Kidney360. 2023 May 1;4(5):648-658. doi: 10.34067/KID.0000000000000116. Epub 2023 Apr 5.
Computational image analysis allows for the extraction of new information from whole-slide images with potential clinical relevance. Peritubular capillary (PTC) density is decreased in areas of interstitial fibrosis and tubular atrophy when measured in interstitial fractional space. PTC shape (aspect ratio) is associated with clinical outcome in glomerular diseases.
The association between peritubular capillary (PTC) density and disease progression has been studied in a variety of kidney diseases using immunohistochemistry. However, other PTC attributes, such as PTC shape, have not been explored yet. The recent development of computer vision techniques provides the opportunity for the quantification of PTC attributes using conventional stains and whole-slide images.
To explore the relationship between PTC characteristics and clinical outcome, =280 periodic acid–Schiff-stained kidney biopsies (88 minimal change disease, 109 focal segmental glomerulosclerosis, 46 membranous nephropathy, and 37 IgA nephropathy) from the Nephrotic Syndrome Study Network digital pathology repository were computationally analyzed. A previously validated deep learning model was applied to segment cortical PTCs. Average PTC aspect ratio (PTC major to minor axis ratio), size (PTC pixels per PTC segmentation), and density (PTC pixels per unit cortical area) were computed for each biopsy. Cox proportional hazards models were used to assess associations between these PTC parameters and outcome (40% eGFR decline or kidney failure). Cortical PTC characteristics and interstitial fractional space PTC density were compared between areas of interstitial fibrosis and tubular atrophy (IFTA) and areas without IFTA.
When normalized PTC aspect ratio was below 0.6, a 0.1, increase in normalized PTC aspect ratio was significantly associated with disease progression, with a hazard ratio (95% confidence interval) of 1.28 (1.04 to 1.59) ( = 0.019), while PTC density and size were not significantly associated with outcome. Interstitial fractional space PTC density was lower in areas of IFTA compared with non-IFTA areas.
Computational image analysis enables quantification of the status of the kidney microvasculature and the discovery of a previously unrecognized PTC biomarker (aspect ratio) of clinical outcome.
计算图像分析可以从具有潜在临床相关性的全切片图像中提取新信息。在间质纤维化和肾小管萎缩区域测量时，肾小管周毛细血管（PTC）密度降低，间质分数空间。PTC 形状（纵横比）与肾小球疾病的临床结局相关。
使用免疫组织化学技术在多种肾脏疾病中研究了 PTC 密度与疾病进展之间的关系。然而，其他 PTC 属性，如 PTC 形状，尚未得到探索。计算机视觉技术的最新发展为使用常规染色和全切片图像定量 PTC 属性提供了机会。
为了探索 PTC 特征与临床结局之间的关系，对来自肾病综合征研究网络数字病理学存储库的 280 例过碘酸-Schiff 染色肾活检（88 例微小病变性疾病，109 例局灶节段性肾小球硬化症，46 例膜性肾病和 37 例 IgA 肾病）进行了计算分析。应用经过验证的深度学习模型对皮质 PTC 进行分割。为每个活检计算平均 PTC 纵横比（PTC 长轴与短轴之比）、大小（PTC 像素数/PTC 分割）和密度（PTC 像素数/单位皮质面积）。Cox 比例风险模型用于评估这些 PTC 参数与结局（40% eGFR 下降或肾衰竭）之间的关系。比较间质纤维化和肾小管萎缩（IFTA）区域与无 IFTA 区域之间的皮质 PTC 特征和间质分数空间 PTC 密度。
当归一化 PTC 纵横比低于 0.6 时，归一化 PTC 纵横比增加 0.1 与疾病进展显著相关，风险比（95%置信区间）为 1.28（1.04 至 1.59）（ = 0.019），而 PTC 密度和大小与结局无显著相关性。与无 IFTA 区域相比，IFTA 区域的间质分数空间 PTC 密度较低。
计算图像分析能够定量评估肾脏微血管的状态，并发现以前未被识别的临床结局的 PTC 生物标志物（纵横比）。
