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

使用图神经网络进行因果建模，用于计算病理学中的个性化医疗

基本信息

批准号：
EP/W02909X/1
负责人：
Fayyaz Ul Amir Afsar Minhas
金额：
$ 47.94万
依托单位：
University of Warwick
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FW02909X%2F1
关键词：
Causal Modelling Graph Neural Networks

项目摘要

"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

“这位患者是什么类型的乳腺癌？这个病人的肿瘤有什么突变？化疗是否有助于提高这个病人的生存率？计算病理学（CPath）通过使用人工智能（AI）分析数字扫描组织切片的数十亿像素全切片图像（WSIs），提供了回答这些问题的革命性新方法。随着提供定量，客观和可重复结果的承诺，计算病理学中的人工智能和机器学习（ML）方法将产生更有效的临床工作流程，并帮助克服当前和未来的挑战，这些挑战是由于患者数量不断增加以及几乎所有发达国家病理学家劳动力规模的减少所带来的。CPath可以帮助病理学家、肿瘤学家和制药行业完成各种诊断和预后任务，以及为癌症患者选择和开发有效的个性化治疗。CPath的这些令人兴奋的可能性也带来了许多科学和计算挑战。这些包括减少训练“数据饥饿”AI方法的大量专业注释数据的需求，提高AI方法对不同中心和人群数据变化的鲁棒性，使AI能够对组织图像的多分辨率性质进行建模，以捕获与诊断，预后和疾病结果相关的有意义的组织学特征，并确保AI方法提供可解释和可操作的结果。虽然CPath目前是一个非常活跃的研究领域，但该领域中的大多数现有方法都无法以最小的训练数据要求以整体方式对WSI进行建模。此外，在这个领域中，没有现有的方法明确地对工作中的潜在因果机制进行建模，以提供反事实的解释（例如，“如果组织切片被不同地染色，这个机器学习模型的输出将如何变化？”）或回答具有临床意义的反事实问题（例如，“如果这个病人得到不同的治疗，会发生什么？").在这个项目中，我们将以工具箱的形式开发计算方法，这将有助于克服这些缺点，并为精准医疗产生有效的人工智能。具体来说，研究小组将开发基于图神经网络（GNN）的方法，该方法可以通过学习WSI的有效表示来建模大型完整切片图像中的细胞拓扑结构和空间异质性，而无需大量训练数据。这些GNN将与因果建模相结合，以提供反事实解释，并提高人工智能方法对非因果变化的鲁棒性，这些非因果变化源于与潜在疾病或治疗机制不直接相关的因素。在这项研究中开发的人工智能工具将为个性化医疗中的临床重要问题提供有效的解决方案。特别是，该研究将能够从常规组织学图像中预测乳腺癌患者的受体状态，这将减少与治疗选择中的这一基本临床步骤相关的等待时间和成本。此外，它将能够更深入地了解患者肿瘤组织图像中的哪些因素可预测其对治疗的反应。因此，拟议的研究将产生新颖有效的CPath技术，并在这一新兴领域开辟一条先前未探索的因果建模途径。根据EPSRC的使命，拟议的研究将有助于确保英国在医疗保健人工智能领域的领导能力，以及通过技术开发和培训高技能劳动力来实现计算病理学的商业可行性。它还符合2019年NHS长期计划中改善人工智能和数字医疗技术使用的国家战略优先事项

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

MesoGraph: Automatic profiling of mesothelioma subtypes from histological images.

仪表术：从组织学图像中自动分析间皮瘤亚型。

DOI：
10.1016/j.xcrm.2023.101226
发表时间：
2023-10-17
期刊：
CELL REPORTS MEDICINE
影响因子：
14.3
作者：
Eastwood, Mark;Sailem, Heba;Marc, Silviu Tudor;Gao, Xiaohong;Offman, Judith;Karteris, Emmanouil;Fernandez, Angeles Montero;Jonigk, Danny;Cookson, William;Moffatt, Miriam;Popat, Sanjay;Minhas, Fayyaz;Robertus, Jan Lukas
通讯作者：
Robertus, Jan Lukas

Neural Graph Modelling of Whole Slide Images for Survival Ranking

用于生存排名的整个幻灯片图像的神经图建模

DOI：
发表时间：
2023
期刊：
影响因子：
0
作者：
Callum Christopher Mackenzie
通讯作者：
Callum Christopher Mackenzie

Screening of normal endoscopic large bowel biopsies with interpretable graph learning: a retrospective study.

DOI：
10.1136/gutjnl-2023-329512
发表时间：
2023-09
期刊：
GUT
影响因子：
24.5
作者：
Graham, Simon;Minhas, Fayyaz;Bilal, Mohsin;Ali, Mahmoud;Tsang, Yee Wah;Eastwood, Mark;Wahab, Noorul;Jahanifar, Mostafa;Hero, Emily;Dodd, Katherine;Sahota, Harvir;Wu, Shaobin;Lu, Wenqi;Azam, Ayesha;Benes, Ksenija;Nimir, Mohammed;Hewitt, Katherine;Bhalerao, Abhir;Robinson, Andrew;Eldaly, Hesham;Raza, Shan E. Ahmed;Gopalakrishnan, Kishore;Snead, David;Rajpoot, Nasir
通讯作者：
Rajpoot, Nasir

SynCLay: Interactive synthesis of histology images from bespoke cellular layouts.

SynCLay：根据定制的细胞布局交互式合成组织学图像。