Multimodal tissue imaging and machine learning to advance precision medicine

The effective treatment of cancer and many other diseases is increasingly dependent on a precision approach in which the quantification of molecular features at the level of individual patients is used to guide treatment plans. However, cancer diagnosis and staging are currently performed primarily via direct examination of biopsy and resection specimens by histopathologists. These classical methods provide insufficient molecular insight to guide the use of targeted and immunotherapies even when supplemented by knowledge of tumor genotypes. You will learn: How spatial biomarkers of high prognostic value can be identified using biomarker quantitation and traditional histology of the same tissue section. The features required of a multi-modal digital pathology approach for validation of spatial biomarkers across patient cohorts. The importance of unbiased, whole-slide analysis and reproducible, trustworthy quantitation and cellular classification for patient stratification.