2 min read

Earlier this year, a group of researchers from Osaka University introduced an AI system based on convolutional neural network (CNN) for automatically identifying radioresistant tumor cells.

In a single cancer tumor, there can be tremendous variation in the cancer cells types which can make it difficult for doctors to make accurate assessments of cell types. Further, this process can be time-consuming and can often be hampered by human error. This AI-based system can make it easy for doctors to choose the most effective treatment and also finds applications in preclinical cancer research.

Explaining the utility of this system, one of the researchers, Masayasu Toratani said, “The automation and high accuracy with which this system can identify cells should be very useful for determining exactly which cells are present in a tumor or circulating in the body of cancer patients. For example, knowing whether or not radioresistant cells are present is vital when deciding whether radiotherapy would be effective, and the same approach can then be applied after treatment to see whether it has had the desired effect.

For the study, the researchers used phase-contrast images of radioresistant clones for two cell lines: mouse squamous cell carcinoma NR-S1, and human cervical carcinoma ME-180. They gathered 10,000 images of each of the parental NR-S1 and ME-180 controls as well as radioresistant clones. VGG16, a convolutional neural network for object recognition, was then trained on 8,000 images of cells. For testing the model, the researchers used another 2,000 images to check its accuracy.

The model was able to give an accuracy of 96%. As per the results, it had learned the features that distinguish mouse cancer cells from human ones, and radioresistant cancer cells from radiosensitive ones. The features extracted by this trained CNN were then plotted using t-distributed stochastic neighbor embedding, and the plot showed that the images of each cell line were well clustered.

In the future, the researchers will train the system on different types of cell types to make it a universal system that can automatically identify and distinguish all variants of cancer cells.

To know more in detail, check out the study published by Cancer Research.

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