Summary: A new study published in Biology Methods & Protocols reveals that artificial intelligence (AI) can detect and diagnose cancer from tissue samples with 98.2% accuracy, potentially allowing for earlier and more effective treatment.

Key Takeaways:

  1. Researchers trained an AI model to identify 13 different cancer types from DNA methylation patterns with high accuracy.
  2. The model relies on tissue samples and requires further training on diverse biopsy samples for clinical application.
  3. Early detection using AI could significantly improve patient outcomes, as many cancers are treatable or curable when caught early.

A new paper in Biology Methods & Protocols, published by Oxford University Press, indicates that it may soon be possible for doctors to use artificial intelligence (AI) to detect and diagnose cancer in patients, allowing for earlier treatment. Cancer remains one of the most challenging human diseases, with over 19 million cases and 10 million deaths annually. The evolutionary nature of cancer makes it difficult to treat late-stage tumours.

Significance of DNA methylation for Cancer Diagnosis

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.” Each individual cell possesses millions of these DNA methylation marks. Researchers have observed changes to these marks in early cancer development; they could assist in early diagnosis of cancer. It’s possible to examine which bases in DNA are methylated in cancers and to what extent, compared to healthy tissue. Identifying the specific DNA methylation signatures indicative of different cancer types is akin to searching for a needle in a haystack. This is where the researchers involved in this study believe that AI can help.

Further reading: Cancer Survivors Have Greater Disease Risk Throughout Life

Training AI for Cancer Diagnosis

Investigators 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 cancer types (including breast, liver, lung, and prostate cancers) from non-cancerous tissue with 98.2% accuracy. This model relies on tissue samples (not DNA fragments in blood) and would need additional training and testing on a more diverse collection of biopsy samples to be ready for clinical use. 

The researchers here believe that an important aspect of this study was the use of an explainable and interpretable core AI model, which provided insights into the reasoning behind its predictions. The researchers explored their model’s inner workings and showed that it reinforces and enhances understanding of the underlying processes contributing to cancer.

Identifying these unusual methylation patterns (potentially from biopsies) would allow health care providers to detect cancer early. This could potentially improve patient outcomes dramatically, as most cancers are treatable or curable if detected early enough.

“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,” says the paper’s lead author, Shamith Samarajiwa. “This will provide better patient outcomes.”