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Abstract Patient-derived xenografts (PDXs) provide biologically relevant models and potential platforms for the development of treatment strategies for precision medicine in pancreatic cancer. Furthermore, circulating epithelial tumor cells (CETCs/CTCs) are released into the bloodstream by solid tumors and a rare subpopulation—circulating cancer stem cells (cCSCs) – is considered to be responsible for recurrence and plays a key role in metastasis. For the identification of cCSCs, an innovative in vitro assay to generate tumorspheres was established in this study. The number of tumorspheres and CETCs/CTCs was analyzed perioperatively in 25 pancreatic cancer patients. Additionally, an individual in vivo chorioallantoic membrane (CAM) culture system was used to generate PDXs from these tumorspheres. While overall correlations of CETCs/CTCs with clinicopathological parameters did not reach statistical significance, a significant difference in the number of tumorspheres was observed between patient subgroups with lower and higher UICC stages. This finding underscores their potential as biomarkers, providing valuable insights into clinical decision-making and tumor progression. The application of tumorspheres on the CAM successfully established PDXs within 7 days. These xenografts closely resembled the histological features of the primary tumor. Hence, this model represents a novel and fast option for individualized testing of new therapies for PDAC.

Cell nuclei interpretation is crucial in pathological diagnostics, especially in tumor specimens. A critical step in computational pathology is to detect and analyze individual nuclear properties using segmentation algorithms. Conventionally, a semantic segmentation network is used, where individual nuclear properties are derived after post-processing a segmentation mask. In this study, we focus on showing that an object-detection-based instance segmentation network, the Mask R-CNN, after integrating it with a Feature Pyramidal Network (FPN), gives mature and reliable results for nuclei detection without the need for additional post-processing. The results were analyzed using the Kumar dataset, a public dataset with over 20,000 nuclei annotations from various organs. The dice score of the baseline Mask R-CNN improved from 76% to 83% after integration with an FPN. This was comparable with the 82.6% dice score achieved by modern semantic-segmentation-based networks. Thus, evidence is provided that an end-to-end trainable detection-based instance segmentation algorithm with minimal post-processing steps can reliably be used for the detection and analysis of individual nuclear properties. This represents a relevant task for research and diagnosis in digital pathology, which can improve the automated analysis of histopathological images.