Deep Learning for Molecular Tumor Biomarker Prediction
How deep learning can help predict molecular tumor biomarkers from images
Heather D. Couture | | Longer Read
When choosing a treatment to target an individual tumor’s specific weaknesses, its molecular properties are critical. And yet, despite the importance of this information, our most accurate methods of assessing molecular properties are expensive and often not routinely performed. We need a better solution – and artificial intelligence (AI) may offer one. Using AI to examine microscopic images of tumor tissue could provide a more cost-effective way of identifying individual tumors’ molecular characteristics to inform treatment decisions.
Histopathology, of course, is the gold standard for diagnosing cancer. A pathologist, examining a prepared tumor sample slide under a microscope, decides based on appearance whether or not cancer cells are present. Although pathologists are experts in diagnosing cancer, they can still assess only limited tumor properties, even with the aid of microscopes and special stains. At the University of North Carolina, we investigated whether a computer could find features to predict molecular biomarkers – features too complex for pathologists to assess visually. Our answer, with a focus on breast cancer, was yes – and other researchers have recently found that the same is true for other cancer types.
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