Leveraging AI and image analysis technology to improve prognostication in colorectal cancer
Korsuk Sirinukunwattana completed a PhD degree in Computer Science from the University of Warwick, focusing on computational pathology. He then became a postdoctoral research fellow at Beth Israel Deaconess Hospital, Harvard Medical School and is currently a postdoctoral research assistant at the University of Oxford, based at the Big Data Institute.
Korsuk’s research concerns leveraging artificial intelligence and image analysis technologies for the development of novel biomarkers extracted from histological slides with molecular and biological interpretability has remarkable potential for clinical translation. Using deep learning, he has predicted consensus molecular subtypes (CMS) of colorectal cancer (CRC) from standard histology sections.
The current standard for prognostication of CRC patients is based on the assessment of histologic materials and tumour progression as defined by the anatomical criteria (TNM staging system). This information supports the definition of broad prognostic risk groups but has no predictive value. The integration of genomic technologies in the clinical care of CRC patients has immense potential to drive personalised treatment but requires substantial financial, personnel and infrastructural resources. On the other hand, histology slides are generated as part of the standard work-up of any CRC treated by surgical resection. Combining morphological information derived from histology slides with molecular profiles to identify genotype-phenotype correlations is a promising and cost-effective approach to extend the amount of clinically relevant information that can be extracted from standard histologic slides.
In Oxford Korsuk collaborates with Jens Rittscher (IBME), and Enric Domingo and Tim Maughan (Oncology) as part of the S:CORT consortium. Internationally, he works with Viktor Koezler at the University of Zurich. He is funded by the NIHR Oxford Biomedical Research Centre.