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Using Machine Learning in Biomarker Development to Advance Patient Treatment and Management

Article

Recent advances in immunotherapy, such as the use of checkpoint inhibitors, are transforming the treatment of many cancers. Yet, not all patients respond sufficiently; indeed, some experience an accelerated rate of tumor growth. Clinicians thus need to be able to select and stratify patients according to their likely responses.

Over the past decade, digital pathology or “virtual microscopy” has emerged as an exciting innovation in the field of pathology, providing a flexible platform using an image-based environment for managing and interpreting information generated from a digitized glass slide. This digitized information is now being harnessed to develop novel digital tissue biomarkers with high predictive and prognostic value via cutting-edge, deep-learning technology. This data-driven technology represents a rapidly growing area of precision medicine that will potentially advance and guide patient treatment.

This white paper explores this emerging AI-based digital diagnostic pathology platform, how it’s changing the future of immunotherapy and advancing patient treatment and management through more efficient diagnostic stratification methods.

Download the full white paper below.

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