Causal Modelling with Graph Neural Networks for Personalised Medicine in Computational Pathology

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"What type of breast cancer does this patient have?", "What are the mutations in the tumour of this patient?", "Will chemotherapy help improve survival for this patient?" - Computational pathology (CPath) is providing revolutionary new ways of answering such questions by using Artificial Intelligence (AI) for analysis of multi-gigapixel whole slide images (WSIs) of digitally scanned tissue slides. With the promise of providing quantitative, objective and reproducible results, AI and machine learning (ML) approaches in computational pathology will yield more efficient clinical workflows and help overcome current and future challenges posed by an ever-increasing workload in terms of number of patients and decrease in the size of the pathologist workforce in almost all developed countries. CPath can assist pathologists, oncologists and the pharmaceutical industry in various diagnostic and prognostic tasks as well as the selection and development of effective personalized treatments for cancer patients. These exciting possibilities of CPath also come with many scientific and computational challenges. These include reducing the requirement of large amounts of expertly-annotated data for training "data-hungry" AI methods, improving robustness of AI approaches to variations in data from different centres and populations, enabling AI to model the multiresolution nature of tissue images to capture meaningful histological characteristics associated with diagnosis, prognosis and disease outcomes, and ensuring that AI methods provide explainable and actionable results. While CPath is currently a very active research field, most existing approaches in this domain are unable to model WSIs in a holistic manner with minimal training data requirements. Furthermore, no existing approaches in this domain explicitly model the underlying causal mechanisms at work to provide counterfactual explanations (e.g., "How will the output of this machine learning model change if the tissue slide was stained differently?") or answer counterfactual questions of clinical significance (e.g., "What would have happened had this patient been given a different treatment?"). In this project, we will develop computational approaches in the form of toolboxes that will help overcome these shortcomings and produce effective AI for precision medicine. Specifically, the research team will develop methods based on graph neural networks (GNNs) which can model cellular topology and spatial heterogeneity in large whole slide images by learning effective representations of WSIs without requiring large amounts of training data. These GNNs will be integrated with causal modelling to provide counterfactual explanations and improve robustness of AI methods to non-causal variations stemming from factors that are not directly related to underlying disease or treatment mechanisms. The AI tools developed in this research will deliver effective solutions to clinically important problems in personalised medicine. In particular, the research will enable prediction of receptor status of breast cancer patients from routine histology images which will reduce waiting times and costs associated with this fundamental clinical step in treatment selection. Furthermore, it will enable a deeper understanding of what factors in the tissue image of a patient's tumour are predictive of their response to treatment. The proposed research will thus result in novel and effective CPath technologies and open up a previously unexplored avenue of causal modelling in this emerging field. In line with EPSRC's mission, the proposed research will help ensure the UK's leadership capacity in the field of AI in healthcare and the commercially viable area of computational pathology through technological development as well as training of a highly skilled workforce. It also aligns with the national strategic prioritization of improved use of AI and digital healthcare technologies in the 2019 NHS Long Term Plan