Data Science has become synonymous with cancer research as biological and clinical datasets grow bigger and more complex. The multi-modality of data, combining spatial and temporal multi-OMICs with digital pathology, and medical imaging, bring new challenges to researchers as they attempt to make use of the wealth of information that can be collected. But what purpose serves the cumulation of Big Data if they cannot be meaningfully resolved? 

Data sciences offer solutions

By integrating applied mathematics, computing, informatics and operational knowledge, Data Science designs new approaches to data analysis, processing and visualisation. It guides researchers through the complexity of the biological picture laid before them, to find hidden clues and patterns, offering answers to crucial cancer enigmas. Data Science empowers smart clinical trials for better and kinder designs and ensures the data collected are used at their full potential to promote the best care in the clinic of the future. With the advent of artificial intelligence in the clinic, Data Science transforms our approach to diagnosis, and treatment decision and monitoring. It enables personalised medicine and home monitoring for a kinder, more efficient and tailor-made experience of the cancer care pathway. The intervention of machines in cancer care might create wariness, but behind every algorithm is a human being. We, as a community of patients, members of the public, and scientists, have control of the future of artificial intelligence, its ethics, its equitableness and its duty to protect the patient's interest.    


Data science


Our mission in Data Science is to use Convergence to:


Multi-modal approaches to Discovery Research

Multi-modal approaches to Discovery Research

AI-assisted medical imaging and digital pathology

AI-assisted medical imaging and digital pathology

Use of Health Data for cancer stratification, detection and monitoring

Use of Health Data for cancer stratification, detection and monitoring




AI in lung cancer clinical trials, digital art
Oncologist looking at computer with data, digital art

In Discovery Research

Discovery research is the foundation of medical innovation. It allows the free exploration of the biological parameters driving the initiation, development and vulnerability of cancer. Through discovery research, scientists unveil the paradigms, biomarkers and drugs enabling the risk stratification, detection and treatment of tumours with the goal of achieving personalised care for each cancer patient. The advent of multi-OMICs has spawned the existence of multi-modal datasets covering the biological complexity of cancer cells. From bulk to single-cell analysis, the granulation of the data collected is also enriched spatially and temporally by innovations in imaging techniques and nanopipettes allowing live biopsies, enabling longitudinal measurements in cells without killing them. The CRUK Convergence Science Centre aims to support methodological progresses in the analysis of multi-modal datasets, including by connecting discovery research with clinical trial data in an iterative pipeline (see Convergence Discovery Research). 




In Clinical Trials

From the design to the execution and analysis, clinical trials represent a particular data challenge for clinicians. The longitudinal and multi-modal data collected, including outcomes, free text reports, medical imaging, biopsies, patient reported outcomes, and digital pathology require a wealth of expertise in Data Science to extract valuable information on the benefit new procedures would bring to patients. Furthermore, we must reflect on decades of clinical trials to retrospectively establish the lessons learned and move forward to create the most effective and kindest clinical trials. In collaboration with clinicians from the Royal Marsden Hospital and the ICHT, the CRUK Convergence Science Centre plans to promote new Data Science methods to guaranty that information extracted during clinical trials serve the patients, and allow scientists to continually review their practice from discovery to implementation (see Clinical Development).





In Medical Devices

Our entire world is connected through data. Modern medicine is no different. Doctors, clinicians, nurses and surgeons use their computer daily to treat patients. Innovations in medical technologies are impossible without considering the essential contribution of data to care management and treatment decisions. Medical devices in early detection, diagnosis, treatment, and monitoring rely more and more on data collection, processing and analysis. The CRUK Convergence Science Centre supports technologies combining hardware and software to collect real-time information on patient health and disease parameters to provide the most efficient detection, diagnosis, and treatment delivery and monitoring (see Interventional Science). Data Science and artificial intelligence constitute major progress in medical care and allow speedy and accurate decision making for the benefit of cancer patients, as time and performance are two of the major contributors to a beneficial outcome in this deadly disease.  








Convergence Data Challenges in Mathematical Oncology


Examples in Discovery Research:

  • Understand resistance mechanisms and inform drug design using multi-OMICs
  • Visualise and track cellular heterogeneity in 3D patient-derived models

Examples in Clinical Studies:

  • Design accurate early detection cancer signature using a combination of liquid biopsy OMICs and medical imaging
  • Use available patient records to predict responsiveness to therapy (e.g. immunotherapy)




Digital Pathology


The medical field of Pathology has been undergoing several revolutions during the past decades. While Molecular Pathology brought the discipline to the scale of the molecule by embracing the full potential of OMICs, Digital Pathology uploaded what used to be only observable under a microscope to the data cloud leading to the examination of pathological samples to a resolution and depth never achieved before. The CRUK Convergence Science Centre aims to push boundaries in the evolution of digital pathology by combining innovative imaging and quantitative technologies to cutting-edge Data Science to achieve progress in diagnosis and treatment monitoring.





If you have any questions relating to the Data Science and Digital Pathology theme please contact Arnaud Legrand ( /