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.

Find out more about our research below

A new FRONTIER for breast cancer

Latest news from FRONTIER, the trial investigating the potential of the radiotracer Fluciclovine in the subtyping and staging of breast cancers

New AI technology to help research into cancer metastasis

DeepScratch is a new AI technology that can be used to analyse how cells move in response to wound, building on the latest advances in deep learning

IL-22 pathway linked to poor prognosis in colon cancer

Research shows how IL-22 interacts with KRAS mutant tumours to promote excessive growth in colorectal cancer

Using machine-learning approaches to identify blood cancer types

Oxford researchers have outlined the applications of AI in the classification of myeloproliferative neoplasm cancers in a new study

New digital classification method using AI developed for colorectal cancer

A new study from S:CORT demonstrates an easy, cheap way to determine colorectal cancer molecular subtype using AI deep-learning digital pathology technology

Tackling oesophageal cancer early detection challenges through AI

Dr Sharib Ali specialises in the applications of AI to early oesophageal cancer detection

NCITA: a new consortium on cancer imaging

NCITA – the UK National Cancer Imaging Translational Accelerator – is a new consortium that brings together world leading medical imaging experts to create an infrastructure for standardising cancer imaging in order to improve its application in clinical cancer treatment.

AI research discovers link between smell genes and colon cancer

Dr Heba Sailem’s new discovery shows a connection between your sense of smell and the spread of colon cancer.