AI-based diagnosis for improving classification of bone and soft tissue tumours across the UK

基于人工智能的诊断可改善英国骨和软组织肿瘤的分类

• 批准号: EP/Y020030/1
• 负责人: Charles-Antoine Collins-Fekete
• 金额: $ 78.13万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY020030%2F1
• 关键词: AI; based; diagnosis; improving; classification

Delivery of pathology tissue diagnoses, most of which are cancer, in the current format is unsustainable. Advances in genomic medicine and immune-oncology have shown that the classification of tumours into subtypes allows selection of patients for specific treatments but also spares patients unnecessary toxic and expensive therapies. Still, making such diagnoses has become more time-consuming, involving the selection and interpretation of ancillary tests which requires an ever-growing specialist knowledge for each cancer type. Whilst the need for diagnostic expertise is increasing, there is already a shortfall of 25% of pathologists who are able to report results: this is set to decline.We propose that the use of AI can ensure that the delivery of tissue diagnoses by pathologists is sustainable and supports delivery of personalised treatments. The benefits of AI in pathology are beginning to be seen, e.g. identification of high-grade areas of prostate cancer shows a reduction in errors and pathologists' time. The development of AI for diagnoses is timely as full adoption of digitised histological images, allowing them to be interrogated by both humans and artificial intelligence (AI), is expected in the UK by 2025. AI is a data-hungry process; it is unrealistic to provide 100,000s images that are required to train a model. Even the most common cancers (e.g. breast) have multiple subtypes; identification of these is required for selection of patients for personalised treatments. To address this challenge, we propose to develop a novel AI strategy using a relatively small sample size (~1000 images per class). Such a model could be adapted to any cancer type. A multiple-instance learning framework will be developed, using transformers for feature extraction and classification. A tool that flags samples that cannot be confidently classified will be applied thereby alerting the pathologist of potentially unseen diseases. The deep learning model will be strengthened by the injection of pathologists' domain knowledge. Soft tissue and bone tumoursWe will develop the AI model on tumours of soft tissue (muscle, fat, blood vessels, etc.) and bone, an area considered to be one of the most challenging diagnostically. These tumours comprise approximately 100 different subtypes, and represent some of the most common cancers in children and young adults. We will build on our existing deep learning model of 15 different subtypes trained on 2122 images, which predicts the correct diagnosis in 87% of cases. Selection of confirmatory ancillary tests is then prompted by the algorithm and streamlines the diagnostic pathway. 17,000 images that have already been scanned will be added to the library and allow the rapid development and extension of the classification model. The image library will be linked to clinical outcomes and expanded to 35,000 images during the project. Added to this is the commitment of the established Sarcoma Network of at least 20 pathologists from across all countries in the UK, to provide the additional 20,000 images mentioned above. Additional benefitsThe study and infrastructure will serve as the framework for the continued development of the model which can rapidly be expanded prospectively with the introduction of digital pathology in the NHS and globally. The model can be developed over time in response to new advances. The image library will be available for training future pathologists, research, validation of other AI algorithms, and contribute to the Sarcoma Genomics England Clinical Interpretation Partnership (GeCIP) offering a valuable resource for future multi-modal multi-omic research.Working closely with Sarcoma charities, and partners, we will involve and engage patients, their families, and the public, to build trust in the use of AI in health care. Development of AI models for digitised pathology images can avert the crisis facing this medical specialty.

以目前的形式提供病理组织诊断，其中大多数是癌症，是不可持续的。基因组医学和免疫肿瘤学的进展表明，将肿瘤分类为亚型可以选择患者进行特定治疗，但也可以使患者免于不必要的毒性和昂贵的治疗。尽管如此，做出这样的诊断已经变得更加耗时，涉及辅助测试的选择和解释，这需要对每种癌症类型不断增长的专业知识。虽然对诊断专业知识的需求正在增加，但能够报告结果的病理学家已经短缺了25%：这将下降。我们建议使用AI可以确保病理学家提供的组织诊断是可持续的，并支持提供个性化治疗。人工智能在病理学中的好处已经开始显现，例如，前列腺癌高级别区域的识别显示出错误和病理学家时间的减少。人工智能诊断的发展是及时的，因为预计到2025年，英国将全面采用数字化组织学图像，允许人类和人工智能（AI）对其进行询问。人工智能是一个数据饥渴的过程;提供训练模型所需的10万张图像是不现实的。即使是最常见的癌症（如乳腺癌）也有多种亚型;需要识别这些亚型才能选择患者进行个性化治疗。为了应对这一挑战，我们建议使用相对较小的样本量（每个类约1000张图像）开发一种新的AI策略。这种模型可以适用于任何癌症类型。将开发一个多实例学习框架，使用变压器进行特征提取和分类。将应用一种标记无法自信地分类的样本的工具，从而提醒病理学家潜在的不可见疾病。通过注入病理学家的领域知识，深度学习模型将得到加强。软组织和骨肿瘤我们将开发软组织（肌肉、脂肪、血管等）肿瘤的AI模型。和骨，这是一个被认为是最具挑战性的诊断领域之一。这些肿瘤包括大约100种不同的亚型，代表了儿童和年轻人中最常见的一些癌症。我们将在现有的深度学习模型基础上构建15种不同的亚型，这些亚型在2122张图像上训练，预测87%的病例的正确诊断。然后通过算法提示选择确认性辅助测试，并简化诊断途径。已经扫描的17 000张图像将被添加到图书馆，并允许快速开发和扩展分类模型。图像库将与临床结果相关联，并在项目期间扩展到35，000张图像。此外，来自英国所有国家的至少20名病理学家组成的肉瘤网络承诺提供上述额外的20，000张图像。该研究和基础设施将作为该模型持续发展的框架，随着NHS和全球数字病理学的引入，该模型可以迅速扩展。随着时间的推移，该模型可以随着新的进展而发展。该图像库将用于培训未来的病理学家、研究、验证其他AI算法，并为英国肉瘤基因组学临床解释合作伙伴关系（GeCIP）做出贡献，为未来的多模式多组学研究提供宝贵的资源。与肉瘤慈善机构和合作伙伴密切合作，我们将让患者、他们的家人和公众参与进来，在医疗保健中使用人工智能建立信任。为数字化病理图像开发人工智能模型可以避免这一医学专业面临的危机。