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.

Background Histopathological assessment of transplant biopsies is currently the standard method to diagnose allograft rejection and can help guide patient management, but it is one of the most challenging areas of pathology, requiring considerable expertise, time, and effort. We aimed to analyse the utility of deep learning to preclassify histology of kidney allograft biopsies into three main broad categories (ie, normal, rejection, and other diseases) as a potential biopsy triage system focusing on transplant rejection. MethodsWe performed a retrospective, multicentre, proof-of-concept study using 5844 digital whole slide images of kidney allograft biopsies from 1948 patients. Kidney allograft biopsy samples were identified by a database search in the Departments of Pathology of the Amsterdam UMC, Amsterdam, Netherlands (1130 patients) and the University Medical Center Utrecht, Utrecht, Netherlands (717 patients). 101 consecutive kidney transplant biopsies were

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we are still lacking computational methods to analyse single-cell and pathomics data at a patient level for finding patient trajectories associated with diseases. This is challenging as a single-cell/pathomics data is represented by clusters of cells/structures, which cannot be easily compared with other samples. We here propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two single single-cell experiments. This allows us to perform unsupervised analysis at the sample level and to uncover trajectories associated with disease progression. Moreover, PILOT provides a statistical approach to delineate non-linear changes in cell populations, gene expression and tissues structures related to the disease trajectories. We evaluate PILOT and competing approaches in disease single-cell genomics and pathomics studies with up to 1.000 patients/donors and millions of cells or structures. Results demonstrate that PILOT detects disease-associated samples, cells, structures and genes from large and complex single-cell and pathomics data.