Melanoma affects a large portion of the population. Nearly 15% of the suspected cases each year cannot be accurately diagnosed using traditional methods. For these cases, a more in depth look at the biopsy is needed to render an accurate diagnosis. Our test offers physicians that insight by generating a molecular fingerprint of the patient's biopsy. This molecular information is then analyzed using artificial intelligence to derive a test result that improves the physician’s ability to accurately diagnose a patient’s lesion as benign or malignant.
Once a patient is diagnosed with esophageal cancer, the conventional treatment plan is chemoradiotherapy followed by extensive surgery to remove all or part of the esophagus. Patients that fully respond to chemoradiotherapy (i.e., cancer cells are eliminated) do not need to undergo the subsequent surgery. No test exists, however, to help physicians distinguish between patients that will likely experience a full response to chemoradiotherapy and those that will likely not. This inability of physicians to predict whether a patient will fully respond to chemoradiotherapy results in a significant portion of esophageal cancer patients undergoing unnecessary surgery. We are developing a test that provides physicians with predictive knowledge about whether a patient is likely to respond to chemoradiotherapy. We believe this test has the potential to reduce the number of unnecessary, high risk surgeries.