ObjectiveTo summarize current patient-derived organoids as preclinical cancer models, and its potential clinical application prospects. MethodsCurrent patient-derived organoids as preclinical cancer models were reviewed according to the results searched from PubMed database. In addition, how cancer-derived human tumor organoids of pancreatic cancer could facilitate the precision cancer medicine were discussed. ResultsThe cancer-derived human tumor organoids show great promise as a tool for precision medicine of pancreatic cancer, with potential applications for oncogene modeling, gene discovery and chemosensitivity studies. ConclusionThe cancer-derived human tumor organoids can be used as a tool for precision medicine of pancreatic cancer.
Organoids are three-dimensional structures formed by self-organizing growth of cells in vitro, which own many structures and functions similar with those of corresponding in vivo organs. Although the organoid culture technologies are rapidly developed and the original cells are abundant, the organoid cultured by current technologies are rather different with the real organs, which limits their application. The major challenges of organoid cultures are the immature tissue structure and restricted growth, both of which are caused by poor functional vasculature. Therefore, how to develop the vascularization of organoids has become an urgent problem. We presently reviewed the progresses on the original cells of organoids and the current methods to develop organoids vascularization, which provide clues to solve the above-mentioned problems.
Organoids are an in vitro model that can simulate the complex structure and function of tissues in vivo. Functions such as classification, screening and trajectory recognition have been realized through organoid image analysis, but there are still problems such as low accuracy in recognition classification and cell tracking. Deep learning algorithm and organoid image fusion analysis are the most advanced organoid image analysis methods. In this paper, the organoid image depth perception technology is investigated and sorted out, the organoid culture mechanism and its application concept in depth perception are introduced, and the key progress of four depth perception algorithms such as organoid image and classification recognition, pattern detection, image segmentation and dynamic tracking are reviewed respectively, and the performance advantages of different depth models are compared and analyzed. In addition, this paper also summarizes the depth perception technology of various organ images from the aspects of depth perception feature learning, model generalization and multiple evaluation parameters, and prospects the development trend of organoids based on deep learning methods in the future, so as to promote the application of depth perception technology in organoid images. It provides an important reference for the academic research and practical application in this field.