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

Finding extracellular vesicle biomarkers for oesophageal cancer early detection

Prof Deborah Goberdhan’s lab is investigating extracellular vesicles and the proteins they express as potential biomarkers for the progression from Barrett’s Oesophagus to oesophageal cancer

Understanding how cancer arises from infected tissue

Dr Francesco Boccellato is investigating the mechanisms behind the pre-cancerous condition known as atrophic gastritis. This may help to identify those who may have cancer, as well as find new ways to prevent cancer from progressing
The NMR machine in the lab of James Larkins, with samples lined up to be analysed

Following the cancer metabolomic breadcrumb trail

By analysing the metabolic molecules that tumour cells leave behind, Dr James Larkin is investigating the applications of metabolomics in the early detection of many cancers.

Bowel cancer patients going undiagnosed due to COVID distruption

A new study led by the University of Oxford has found that since the first coronavirus lockdown the number of people diagnosed with bowel cancer in England has fallen sharply, with a deficit persisting up to October 2020

SCALOP team discover new pancreatic cancer biomarker

The SCALOP clinical trial team have uncovered a new therapeutic target for locally advanced pancreatic cancer. Read more about it and the next steps for the SCALOP-2 trial.
Image from an endoscopy video with the detected artefacts highlighted with coloured boxes.

Using AI to improve the quality of endoscopy videos

A multidisciplinary team of researchers has developed a deep-learning framework for improving endoscopy to aid cancer detection.

The search for pancreatic cancer biomarkers

Nuffield Department of Population Health researchers are using the China Kadoorie Biobank to identify potential protein biomarkers in the blood that could be used to predict the presence or development of pancreatic cancer

Early stage ‘red flag’ symptoms for pancreatic cancer

The ADEPTS study uses the QResearch database to better understand what ‘red flag’ symptoms may be associated with pancreatic cancer, in the hopes of promoting earlier diagnosis from primary care.

Detecting pancreatic cancer through blood tests

The Song Lab recently developed an effective and accurate way of detecting cancer biomarkers in the blood. Now, they are looking at the application of TAPS technology in pancreatic cancer