Objective Classification of Lupus Nephritis

狼疮性肾炎的客观分类

• 批准号: 10683624
• 负责人: CHANDRA MOHAN
• 金额: $ 66.94万
• 依托单位: UNIVERSITY OF HOUSTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-20 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10683624
• 关键词: Adult; Algorithms; Automobile Driving; Cells; Child; Chronic; Classification; Computer Vision Systems; Diagnosis; Diagnostic; Educational process of instructing; End stage renal failure; Feedback; Histologic; Human; Image; Kidney; Lupus; Lupus Erythematosus; Lupus Nephritis; Machine Learning; Nephritis; Outcome; Outcome Study; Pathologist; Pathology; Patients; Performance; Phenotype; Prediction of Response to Therapy; Reading; Reporting; Reproducibility; Retrieval; Structure; Systemic Lupus Erythematosus; Testing; Tissues; Training; Uncertainty; accurate diagnosis; convolutional neural network; deep learning; diagnosis standard; diagnostic criteria; falls; improved; indexing; innovation; kidney biopsy; neural network; novel; predict clinical outcome; time interval; tool; treatment response; user-friendly; web portal

Up to 60% of adults and 80% of children with systemic lupus erythematosus (SLE) develop nephritis (LN), with 10–30% progressing to end-stage renal disease (ESRD). The gold standard for diagnosis of LN is a renal biopsy. Histological parameters remain the best predictors of ESRD. Despite being the gold standard, histological diagnosis of LN has several shortcomings. In multiple inter-observer renal pathology assessment studies reported thus far, the inter- pathologist correlation coefficients, or concordance, in assessing most histological parameters have been sub-optimal. This has provided the impetus for the current proposal. We propose to leverage the power of computer vision and deep learning to build a classifier that rivals the best-trained renal pathologists in making a histological diagnosis of LN using current diagnostic criteria. We propose to train a deep convolutional neural network to distinguish the different LN classes, and to identify a full spectrum of histological attributes useful for diagnosis. We will compare the performance of the newly generated neural network in scoring glomerular/tubulo-interstitial features and LN classes, against a panel of human renal pathologists. Finally, we propose to build a neural network that can predict clinical outcome based on baseline renal pathology. Reliable and reproducible classification of LN could dramatically improve patient management and long-term renal and patient survival.

多达60％的成年人和80％的全身性红斑狼疮儿童（SLE）发展 肾炎（LN），有10-30％的肾病发展为终末期肾脏疾病（ESRD）。黄金标准 为了诊断LN是肾脏活检。组织学参数仍然是最好的预测指标 ESRD。尽管是黄金标准，但LN的组织学诊断有几个缺点。 在迄今为止报道的多个观察者间肾脏病理评估研究中， 病理学家相关系数或一致性在评估大多数组织学参数时 是最佳的。这为当前建议提供了动力。 我们建议利用计算机视觉和深度学习的力量来建立一个分类器 竞争最受训练的肾脏病理学家在使用电流进行LN的组织学诊断方面竞争 诊断标准。我们建议训练深层卷积神经网络，以区分 不同的LN类，并确定对诊断有用的整个组织学属性。 我们将比较新生成的神经网络的性能 肾小球/微管互化特征和LN类，针对人类肾脏 病理学家。最后，我们建议建立一个可以预测临床结果的神经元网络 基于基线肾脏病理。 LN可靠且可重复的分类可以 动态改善患者管理以及长期肾脏和患者生存。