Next-Generation Morphometry for pathomics-data mining in histopathology

Abstract Pathology diagnostics relies on the assessment of morphological features by trained experts, which remains subjective and qualitative. Modern image analysis techniques, particularly deep learning, provide a possible solution, sometimes exceeding human capabilities, e.g., mutation prediction directly from histology. 49 However, categorical model outputs are of limited use for further downstream analyses and limited interpretability. Here we developed a framework for large-scale histomorphometry (FLASH) which performs semantic segmentation and subsequent large-scale extraction of interpretable morphometric features. Two internal and three external, multi-centre cohorts of kidney biopsies were used to generate 40 million data points. Association with clinical data confirmed previous concepts, e.g., the importance of tubular atrophy for kidney function decline, and revealed unexpected findings, such as glomerular tuft hypertrophy in biopsies from patients with vs. without nephrotic range proteinuria. Single-structure analysis identified distinct glomerular populations and morphometric phenotypes along a trajectory of disease progression and features were independently associated with long-term clinical outcomes in IgA nephropathy. These data provide the concept for Next-generation Morphometry (NGM), opening new possibilities for comprehensive quantitative pathology data mining, i.e., pathomics, enabling augmented research and diagnostics.