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Next-Generation Morphometry for pathomics-data mining in histopathology
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DOI：10.1038/s41467-023-36173-0.png)
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摘要
En 中文
Pathology diagnostics relies on the assessment of morphology by trained experts, which remains subjective and qualitative. Here we developed a framework for large-scale histomorphometry (FLASH) performing deep learning-based semantic segmentation and subsequent large-scale extraction of interpretable, quantitative, morphometric features in non-tumour kidney histology. We use two internal and three external, multi-centre cohorts to analyse over 1000 kidney biopsies and nephrectomies. By associating morphometric features with clinical parameters, we confirm previous concepts and reveal unexpected relations. We show that the extracted features are independent predictors of long-term clinical outcomes in IgA-nephropathy. We introduce single-structure morphometric analysis by applying techniques from single-cell transcriptomics, identifying distinct glomerular populations and morphometric phenotypes along a trajectory of disease progression. Our study provides a concept for Next-generation Morphometry (NGM), enabling comprehensive quantitative pathology data mining, i.e., pathomics. Pathology diagnostics still rely on tissue morphology assessment by trained experts. Here, the authors perform deep-learning-based segmentation followed by large-scale feature extraction of histological images, i.e., next-generation morphometry, to enable outcome-relevant and disease-specific pathomics analysis of non-tumor kidney pathology.
Keyword:
OXFORD CLASSIFICATION
IGA NEPHROPATHY
KIDNEY
RATIONALE
BIOPSIES
LESIONS
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
15.7
论文数:
9.4W
被引数:
91.2W
机构
引用论文
Deep learning identified pathological abnormalities predictive of graft loss in kidney transplant biopsies深度学习确定了预测肾移植活检中移植物丢失的病理异常.png)
KIDNEY INTERNATIONAL
OAAI
KIDNEY INTERNATIONALIF12.6
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