Clinical Relevance of Computational Pathology Analysis of Interplay Between Kidney Microvasculature and Interstitial Microenvironment.

IF 8.5 1区 医学 Q1 UROLOGY & NEPHROLOGY
Yijiang Chen, Bangchen Wang, Dawit Demeke, Fan Fan, Celine Berthier, Laura Mariani, Kyle Lafata, Lawrence Holzman, Jeffrey Hodgin, Andrew Janowczyk, Laura Barisoni, Anant Madabhushi
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引用次数: 0

Abstract

Background: Interstitial fibrosis and tubular atrophy (IFTA), and density and shape of peritubular capillaries (PTCs), are independently prognostic of disease progression. This study aimed to identify novel digital biomarkers of disease progression and assess the clinical relevance of the interplay between a variety of PTC characteristics and their microenvironment in glomerular diseases.

Methods: A total of 344 NEPTUNE/CureGN participants were included: 112 minimal change disease, 134 focal segmental glomerulosclerosis, 61 membranous nephropathy, and 37 IgA nephropathy. A PAS-stained whole slide image per patient was manually segmented for cortex, pre- and mature IFTA. Interstitial fractional space (IFS) was computationally quantified. A deep-learning model was applied to segment PTCs. Spatial and shape PTC pathomic features (230) were extracted from the cortex, IFTA, and non-IFTA sub-regions. Participants were divided into training and testing datasets (1:1). Univariate models incorporating IFTA subregions, and IFS-PTC density were constructed. LASSO regression models were used to identify the top PTC features associated with disease progression stratified by IFTA and non-IFTA sub-regions. Machine learning models were built using the top PTC features in IFTA and non-IFTA sub-regions to prognosticate disease progression.

Results: PTC density in pre+mature IFTA and IFS, shape features in pre+mature IFTA, and spatial architecture features in the non-IFTA regions associated with disease progression. The machine learning generated risk scores showed a significant association with disease progression on the independent testing set.

Conclusion: We uncovered previously underrecognized digital biomarkers of disease progression and the clinical relevance of the complex interplay between the status of the PTCs and the interstitial microenvironment.

肾微血管与间质微环境相互作用的计算病理学分析的临床意义。
背景:间质纤维化和小管萎缩(IFTA)以及小管周围毛细血管(ptc)的密度和形状是疾病进展的独立预后因素。本研究旨在确定疾病进展的新型数字生物标志物,并评估肾小球疾病中各种PTC特征及其微环境之间相互作用的临床相关性。方法:共纳入344名NEPTUNE/CureGN参与者:112例轻度改变疾病,134例局灶节段性肾小球硬化,61例膜性肾病和37例IgA肾病。每个患者的pas染色的整张幻灯片图像被人工分割为皮质,预成熟IFTA。对间隙分数空间(IFS)进行计算量化。将深度学习模型应用于分段ptc。从皮层、IFTA和非IFTA次区域提取PTC的空间和形状病理特征(230)。参与者被分成训练数据集和测试数据集(1:1)。构建了包含IFTA次区域和IFS-PTC密度的单变量模型。LASSO回归模型用于识别与IFTA和非IFTA次区域分层的疾病进展相关的PTC顶级特征。利用IFTA和非IFTA次区域的顶级PTC特征建立机器学习模型,以预测疾病进展。结果:预成熟IFTA和IFS的PTC密度、预成熟IFTA的形状特征和非IFTA区域的空间结构特征与疾病进展相关。机器学习生成的风险评分在独立测试集上显示出与疾病进展的显著关联。结论:我们发现了以前未被充分认识的疾病进展的数字生物标志物,以及ptc状态与间质微环境之间复杂相互作用的临床相关性。
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来源期刊
CiteScore
12.20
自引率
3.10%
发文量
514
审稿时长
3-6 weeks
期刊介绍: The Clinical Journal of the American Society of Nephrology strives to establish itself as the foremost authority in communicating and influencing advances in clinical nephrology by (1) swiftly and effectively disseminating pivotal developments in clinical and translational research in nephrology, encompassing innovations in research methods and care delivery; (2) providing context for these advances in relation to future research directions and patient care; and (3) becoming a key voice on issues with potential implications for the clinical practice of nephrology, particularly within the United States. Original manuscript topics cover a range of areas, including Acid/Base and Electrolyte Disorders, Acute Kidney Injury and ICU Nephrology, Chronic Kidney Disease, Clinical Nephrology, Cystic Kidney Disease, Diabetes and the Kidney, Genetics, Geriatric and Palliative Nephrology, Glomerular and Tubulointerstitial Diseases, Hypertension, Maintenance Dialysis, Mineral Metabolism, Nephrolithiasis, and Transplantation.
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