Deep learning model classifies cancer cells by type

18 May 2023

cancer cells, cancers, cancer

Cancers are classified in two ways: by the type of tissue in which the cancer originates (histological type) and by primary site, or the location in the body where the cancer first developed. Now, researchers from Texas Tech University developed a deep learning model to classify cancer cells by type. The deep learning network analyzes images to categorize cell types accurately and efficiently.

The findings are published in APL Machine Learning in an articled titled, “Label-free identification of different cancer cells using deep learning-based image analysis.”

“Cancer cells are highly heterogeneous, and recent studies suggest that specific cell subpopulations, rather than the whole, are responsible for cancer metastasis,” said author Wei Li, PhD, associate professor, Texas Tech University. “Identifying subpopulations of cancer cells is a critical step to determine the severity of the disease.”

“Cancer diagnostics is an important field of cancer recovery and survival with many expensive procedures needed to administer the correct treatment,” wrote the researchers. “Machine learning (ML) approaches can help with the diagnostic prediction from circulating tumor cells in liquid biopsy or from a primary tumor in solid biopsy. After predicting the metastatic potential from a deep learning model, doctors in a clinical setting can administer a safe and correct treatment for a specific patient. This paper investigates the use of deep convolutional neural networks for predicting a specific cancer cell line as a tool for label-free identification.”

“The problem with these complicated and lengthier techniques is that they require resources and effort that could be spent exploring different areas of cancer prevention and recovery,” said author Karl Gardner, PhD, research assistant, Texas Tech University.

“Our classification procedure does not consist of additional chemicals or biological solutions when taking pictures of the cells,” said Gardner. “It is a ‘label-free’ identification method of metastatic potential.”

The team’s neural network is also simple to use, efficient, and automated. After feeding it an image, the tool converts the data to a probability. A result lower than 0.5 categorises the cancer as one cell type, while a number higher than 0.5 designates another.

The tool was trained to optimise the accuracy of predictions with a set of images of two cancer cell lines. It reached over 94% accuracy across the data sets used in the study.

The authors aim to extend and generalise the model to include both single cells and clusters.

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