Abstract
.New increasingly complex in vitro cancer cell models are being developed. These new models seem to represent the cell behavior in vivo more accurately and have better physiological relevance than prior models. An efficient testing method for selecting the most optimal drug treatment does not exist to date. One proposed solution to the problem involves isolation of cancer cells from the patients’ cancer tissue, after which they are exposed to potential drugs alone or in combinations to find the most optimal medication. To achieve this goal, methods that can efficiently quantify and analyze changes in tested cell are needed. Our study aimed to detect and segment cells and structures from cancer cell cultures grown on vascular structures in phase-contrast microscope images using U-Net neural networks to enable future drug efficacy assessments. We cultivated prostate carcinoma cell lines PC3 and LNCaP on the top of a matrix containing vascular structures. The cells were imaged with a Cell-IQ phase-contrast microscope. Automatic analysis of microscope images could assess the efficacy of tested drugs. The dataset included 36 RGB images and ground-truth segmentations with mutually not exclusive classes. The used method could distinguish vascular structures, cells, spheroids, and cell matter around spheroids in the test images. Some invasive spikes were also detected, but the method could not distinguish the invasive cells in the test images.
Highlights
In many cases, chemotherapeutic anticancer therapies’ contribution to life extension is low, and serious adverse effects are common.[1,2] the cost of cancer treatments is extremely high and is increasing constantly.[2]To improve the clinical benefits of treatments, there is a need to find new ways to treat cancer and to investigate which drugs or drug combinations are the most effective for individual cancers and patients
To get more relevant and reliable information from in vitro techniques, in vitro cancer research has been increasingly interested in alternative models that better mimic the tumor environment in vivo.[3]
The cells were cultured in flasks for 3 days and were seeded at a density of 5000 cells/well on the top of in vitro vascular structures in 96-well plates
Summary
To improve the clinical benefits of treatments, there is a need to find new ways to treat cancer and to investigate which drugs or drug combinations are the most effective for individual cancers and patients. This could be achieved by developing new in vitro models that are able to reliably predict the effect of drugs in vivo. To get more relevant and reliable information from in vitro techniques, in vitro cancer research has been increasingly interested in alternative models that better mimic the tumor environment in vivo.[3] These new models are often complex and have many parameters that need to be considered simultaneously. There is a need for computational methods to recognize and quantify these new parameters and handle the massive amount of data with high throughput
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