The ability to accurately identify cancer—and classify cancer types—using machine learning would provide a tremendous advance in cancer diagnostics for both physicians and patients.
But that is just one role of many that machine learning can play in cancer.
Another application is to predict genomic alterations from morphological characteristics learned from digital slides.
The genomicA team at the University of Chicago (UChicago) Medicine Comprehensive Cancer Center, working with colleagues in Europe, created a deep learning algorithm that can infer molecular alterations directly from routine histology images across multiple common tumor types. It also provides spatially resolved tumor and normal tissue distinction.
The work is published in the study titled, “Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis” published in Nature Cancer.
This paper highlighted the potential of artificial intelligence (AI) to help clinicians make personalized treatment plans for patients based on the information gained from how tissues appear under the microscope.
“We found that using artificial intelligence, we can quickly and accurately screen cancer patient biopsies for certain genetic alterations that may inform their treatment options and likelihood to respond to specific therapies,” said co-corresponding author Alexander Pearson, MD, PhD, assistant professor of medicine at UChicago Medicine.
“We are able to detect these genetic alterations almost instantly from a single slide, instead of requiring additional testing post-biopsy,” said Pearson.
“If this model was validated and deployed at scale, it could dramatically improve the speed of molecular diagnosis across many cancers.”
Pearson and colleagues noted that although comprehensive molecular and genetic tests are difficult to implement at scale, tissue sections stained and mounted on a slide are commonplace and easy to study.
And because molecular alterations in cancer can cause observable changes in tumor cells and their microenvironment, the researchers hypothesized that these structural changes would be visible on images of tissue slices captured under the microscope.
In other words, genotype, the genetic make-up of the tumor cells, including gene mutations in key oncogenic pathways, influences the visible traits of those cells, known as their phenotype.
To test this, they set out to systematically investigate the presence of genotype-phenotype links for a wide range of clinically relevant molecular features across all major solid tumor types.
Specifically, they asked which molecular features leave a strong enough footprint in histomorphology that they can be determined from histology images alone with deep learning.