Artificial Intelligence Brings Hope for Early Detection of Cancers

Artificial intelligence (AI) may help doctors to detect and diagnose cancer in patients that will allow for earlier treatment, reports a new study. According to the study published in the journal Biology Methods and Protocols (1^✔ ✔Trusted Source
Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns

Go to source), the researchers from Cambridge University and Imperial College London trained an AI mode, using a combination of machine and deep learning, to look at the ‘DNA methylation’ patterns and identify 13 different types of cancer, including breast, liver, lung, and prostate cancers -- from non-cancerous tissue with 98.2 percent accuracy.

What is DNA Methylation?

"Genetic information is encoded in DNA by patterns of the four bases -- denoted by A, T, G and C -- that make up its structure. Environmental changes outside the cell can cause some DNA bases to be modified by adding a methyl group. This process is called ’DNA methylation’," the researchers explained. Each cell possesses millions of these DNA methylation marks.

Researchers have observed changes to these marks in early cancer development; they could aid in early cancer diagnosis.

"Computational methods such as this model, through better training on more varied data and rigorous testing in the clinic, will eventually provide AI models that can help doctors with early detection and screening of cancers," said the paper’s lead author, Shamith Samarajiwa.

"This will provide better patient outcomes," he added.

In addition, the researchers mentioned that identifying these unusual methylation patterns (potentially from biopsies) would allow healthcare providers to detect cancer early. This could potentially improve patient outcomes dramatically, as most cancers are treatable or curable if detected early enough, they added.

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References:

1. Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns - (https://doi.org/10.1093/biomethods/bpae028)

Source-IANS