During the first part of this project, we were able to proceed in all planned areas. We have published 10 original peer-reviewed papers, co-authored 22 original papers, and written and contributed to 9 review articles.
One of the highlights we have achieved so far is the development of Next-Generation Morphometry for pathomics-data mining in kidney histopathology. Here, we proposed a deep-learning (DL) based framework that automatically and reproducibly extracts large-scale digital, quantitative, and interpretable morphological biomarkers from patients’ histopathology in a high-throughput manner and is even applicable in rare diseases. This represents a novel methodological approach for unbiased large-scale data mining of histomorphology, which we term “Next Generation Morphometry - NGM”, providing a novel “omics” approach, which we term “pathomics”. Similarly to “next-generation sequencing - NGS” and “genomics”, which previously revolutionized molecular pathology diagnostics, our proposed framework for next-generation Morphometry (“NGM”) might have a similar potential to advance morphology-based pathology diagnostics. Using this method enabled us to comprehensively analyze the fates of kidney structures during recovery from kidney injury, revealing that the current available kidney functional parameters do not well reflect the ongoing pathological processes and limited regenerative capacity of the kidney. This substantiates the need to develop non-invasive diagnostic imaging approaches, one of the focuses of this project.
Another example is the area of assessment of kidney transplant biopsies, which is currently the only means to diagnose allograft diseases and particularly transplant rejection. The correct diagnostics of kidney transplant biopsies guide the treatment and management of patients. At the same time, transplant biopsy diagnostics is one of the most complex and time-consuming areas of pathology and is still plagued by considerable variability, some of which are therapeutically relevant. We here addressed this unmet need, and to our knowledge for the first time, developed, tested, and validated deep-learning support systems to augment the histopathological diagnostics of kidney transplant biopsies. This was the largest transplant deep learning study in pathology, bringing together a strong international and interdisciplinary team, focusing on relevant clinical application scenarios. This study opened a new research area and strengthened our position as an internationally leading team in deep learning development in kidney pathology.
