Computational Pathology

LSP researchers merge traditional histopathology with high-plex imaging and machine learning to generate biological insights that enable personalized patient care. 

Developing a precise, individualized, patient-centered approach to medicine relies on the ability to accurately diagnose diseases and determine which patients are most likely to benefit from a specific therapy.

Despite a growing understanding of the genetic, cellular, and environmental drivers of human disease, the practice of histopathology—the primary method for diagnosing cancer—remains largely pre-digital, relying on visual examination of tissue slides under a microscope.

To address this gap, LSP researchers combine traditional histopathology with high-plex tissue imaging, spatial transcriptomics, and machine learning algorithms to analyze tissue samples at a much higher level of detail. We recently used multimodal data from patients with colorectal cancer to develop machine learning models that could predict disease progression. Building on this work, we are partnering with clinicians to analyze large cohorts of patients from numerous disease states. Ultimately, we aim to develop robust methods for computational pathology that can be readily applied to a wide variety of clinical settings to improve the diagnosis and management of human disease.