Deep learning model predicts microsatellite instability in tumors and flags uncertain cases

One in every three people is expected to have cancer in their lifetime, making it a major health concern for mankind. A crucial indicator of the outcome of cancer is its tumor microsatellite status—whether it is stable or unstable. It refers to how stable the DNA is in tumors with respect to the number of mutations within microsatellites.

The tumor microsatellite status has important clinical value because patients with microsatellite instability-high (MSI-H) cancers usually have more promising outcomes compared to patients with microsatellite stable tumors. Furthermore, tumors deficient in mismatch repair proteins—these are cells with mutations in specific genes that are involved in correcting mistakes made when DNA is copied in a cell—respond well to immune checkpoint inhibitors (ICIs) and not necessarily to chemotherapeutics.

Therefore, health practitioners and experts suggest MSI testing for newly diagnosed gastric and colorectal cancers. In recent years, artificial intelligence (AI) has made significant strides in this field and its incorporation in clinical workflow is expected to provide cost-efficient and highly accessible MSI testing.

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