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Diagnostics Biochemistry and molecular biology, Digital and computational pathology, Precision medicine, Oncology

Deep Learning for Molecular Tumor Biomarker Prediction

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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About the Author

Heather D. Couture

Founder of machine learning consulting firm Pixel Scientia Labs, which solves image analysis tasks for pathology applications. She recently completed a doctoral degree in Computer Science at the University of North Carolina at Chapel Hill, North Carolina, USA.

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