AI gets real: using routine clinical data and Artificial Intelligence to predict worsening of Multiple Sclerosis despite treatment (AIMS)

人工智能变得真实：使用常规临床数据和人工智能来预测多发性硬化症在治疗后的恶化情况 (AIMS)

• 批准号: MR/T024402/1
• 负责人: Radu Tanasescu
• 金额: $ 25.83万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FT024402%2F1
• 关键词: AI; gets; real; using; routine

Multiple sclerosis (MS) can be a severely disabling disease. Widespread use of MRI and revisions to MS diagnosis have enabled earlier identification and considerable progress in developing therapies for MS. Access to the treatments in MS has been improved recently, but although most people with relapsing MS can benefit from them and outcomes are improving as a result, no single treatment is right for everybody. Some people with MS will relapse, and over the long term will gather psychical and cognitive disability. Despite progress in assessing response to treatments, individual prediction of MS outcomes over the long-term is still inaccurate; the need for information on individualised long-term prognosis for MS patients' forecasting is frequently unmet.A wealth of clinical and MRI data from patients with MS are acquired every year in clinical practice, but only part of these data are used for clinical decision making. MRI is paramount in MS diagnosis and monitoring, but most often the only feature of clinical use are the MS lesions. However, the structure of MS brains visualised on imaging is likely related to several aspects of MS biology. We propose that a set of structural characteristics extracted from the MRI brain scans of people with MS are related to biological changes which are meaningful to MS, and may therefore act as predictive markers for outcome. Computational imaging approaches using artificial intelligence (AI) have achieved successes in automatically quantifying lesions. AI-based classification of the scan's features referred to as 'radiomics' can provide more detailed characterisation than is possible by the naked eye and can offer the means to extract more information from the whole-image MRI brain scans.Radiomics-based biomarkers (indicators) have shown success in cancer treatment, but are still in early development in MS. In this study, we aim to use whole-image brain MRI scans processed with AI techniques and detect the clinical and MRI profiles that predict accumulation of MS-related disability or cognitive impairment. We will take advantage of our MS clinic which is one of the largest in England, and the Nottingham MS Society Register. We will draw on a unique environment of research experts in MS, clinical trials, MRI, computational imaging and predictive modelling who work collaboratively with patients and carers within the NIHR Nottingham Biomedical Research Centre. We will use individual data about patients' clinical condition, their demographics and their scans, and analyse it by the means of AI. Using clinical information which patients have consented for us to use, and the MRI images before starting treatment, we'll train a computer to use mathematical models to predict whether a person's MS will determine accumulating disability or cognitive impairment over the long term. Furthermore, we seek to see if the profile can predict development of disability in other patient groups, by validating the models in large sets of MRI scans obtained from other groups of people with MS using different scanners. We will use a large group of patients from the United States, and also align with a clinical trial ongoing in UK and US, which compares treatments for MS with different strengths.At least a third of people with MS starting on a first-line MS treatment require subsequent escalation to a stronger therapy. By identifying early, at diagnosis, who is likely to fare worse over the long term, we could offer them a more tailored treatment approach. This is a crucial step towards "personalised medicine", which means we'll be able to prescribe the right medication for the right person at the right time.

多发性硬化症（MS）可能是一种严重的致残性疾病。MRI的广泛使用和MS诊断的修订使MS的早期识别和开发治疗取得了相当大的进展。MS的治疗最近得到了改善，但是尽管大多数复发性MS患者可以从中受益，结果也在改善，但没有一种治疗方法适合所有人。一些患有MS的人会复发，并且长期将聚集心理和认知障碍。尽管在评估对治疗的反应方面取得了进展，但对MS长期预后的个体预测仍然不准确; MS患者预测的个体化长期预后信息的需求经常得不到满足。在临床实践中，每年都会获得MS患者的大量临床和MRI数据，但这些数据中只有一部分用于临床决策。MRI在MS诊断和监测中至关重要，但临床使用的唯一特征通常是MS病变。然而，在成像上可视化的MS大脑结构可能与MS生物学的几个方面有关。我们提出，从MS患者的MRI脑扫描中提取的一组结构特征与对MS有意义的生物学变化有关，因此可以作为结果的预测标记。使用人工智能（AI）的计算成像方法在自动量化病变方面取得了成功。基于AI的扫描特征分类被称为“放射组学”，可以提供比肉眼更详细的表征，并可以提供从全图像MRI脑部扫描中提取更多信息的方法。（指标）已经显示出在癌症治疗中的成功，但在MS中仍处于早期发展阶段。在这项研究中，我们的目标是使用AI技术处理的全图像脑MRI扫描，并检测预测MS相关残疾或认知障碍累积的临床和MRI特征。我们将利用我们的MS诊所是英国最大的诊所之一，以及诺丁汉MS协会注册。我们将利用MS，临床试验，MRI，计算成像和预测建模研究专家的独特环境，他们与NIHR诺丁汉生物医学研究中心内的患者和护理人员合作。我们将使用有关患者临床状况、人口统计学和扫描的个人数据，并通过人工智能进行分析。使用患者同意我们使用的临床信息，以及开始治疗前的MRI图像，我们将训练计算机使用数学模型来预测一个人的MS是否会长期累积残疾或认知障碍。此外，我们试图通过验证使用不同扫描仪从其他MS患者群体获得的大型MRI扫描集中的模型，来了解该配置文件是否可以预测其他患者群体的残疾发展。我们将使用来自美国的大量患者，并与英国和美国正在进行的临床试验保持一致，该试验比较了不同强度的多发性硬化症治疗方法。至少三分之一的多发性硬化症患者开始接受一线多发性硬化症治疗需要后续升级到更强的治疗。通过在早期诊断，谁可能在长期内表现更糟，我们可以为他们提供更有针对性的治疗方法。这是迈向“个性化医疗”的关键一步，这意味着我们将能够在正确的时间为正确的人开出正确的药物。

项目成果

CSF lymphocytic pleocytosis does not predict a less favourable long-term prognosis in MS.

CSF淋巴细胞性多细胞增多症不会预测MS中的长期预后不太有利。

• DOI: 10.1007/s00415-022-11521-0
• 发表时间: 2023-04
• 期刊: JOURNAL OF NEUROLOGY
• 影响因子: 6
• 作者: Astbury, Lauren;Kalra, Seema;Tanasescu, Radu;Constantinescu, Cris S.
• 通讯作者: Constantinescu, Cris S.

Neutrophil-to-Lymphocyte Ratio as a Biomarker of Response to Immunomodulation: Findings of the WIRMS Trial of Hookworm in RMS

中性粒细胞与淋巴细胞比率作为免疫调节反应的生物标志物：钩虫在 RMS 中的 WIRMS 试验结果

• 发表时间: 2022
• 作者: Cris S Constantinescu

Acute Inflammatory Diseases of the Central Nervous System After SARS-CoV-2 Vaccination.

• DOI: 10.1212/nxi.0000000000200063
• 发表时间: 2023-01
• 期刊: Neurology(R) neuroimmunology & neuroinflammation

Predictors of long-term disability in multiple sclerosis patients using routine magnetic resonance imaging data: A 15-year retrospective study.

• DOI: 10.1177/19714009221150853
• 发表时间: 2023-10
• 期刊: The neuroradiology journal
• 作者: Altokhis A;Alotaibi A;Morgan P;Tanasescu R;Evangelou N
• 通讯作者: Evangelou N

The gut-microbiota-brain axis: An introduction to a special issue on its role in neurological disorders.

肠道-微生物群-大脑轴：介绍其在神经系统疾病中的作用的特刊。

• DOI: 10.1111/ene.16080
• 发表时间: 2023
• 期刊: European journal of neurology
• 影响因子: 5.1
• 作者: De Looze K