The last few years have seen AI technologies become increasingly adroit at finding needles in the hay of health data. The latest example comes via a paper, published in Cell Reports, in which AI was used to produce maps of how our immune system fights cancer.
The researchers trained their algorithm on high-resolution images of tumor tissues from 13 different types of cancers alongside clinical and genomic data. They were able to identify so called tumor-infiltrating lymphocytes (TILs), or TIL maps, which they hope will allow cancer specialists to generate effective tumor-immune information from the images.
Key to survival
These TIL maps are key to the survival of the patient, and the team believe they provide the foundation for better diagnoses and treatment plans for cancers that are responsive to immune-based therapies, including lung, bladder and melanoma.
The current gold standard in cancer diagnoses is the pathology report, which is created from a biopsy of the tumor tissue. In immune-based therapies, the pathologist is also tasked with making observations about the immunologic features of the tumor to determine whether the patient will benefit from these therapies.
“This paper demonstrates that we can now use deep learning methods such as artificial intelligence to extract and classify patterns of immune cells in ubiquitously obtained pathology studies, and to relate immune cell patterns to the many other types of cancer patient molecular and clinical data,” the authors say.
The AI-generated TIL maps will hopefully act as a potential guide for both diagnoses and treatment planning. The work itself is part of a wider project called The Cancer Genome Atlas (TCGA), which is a decade-long piece of work by the National Cancer Institute and the National Human Genome Research Institute in collaboration with the cancer research community worldwide.