利用放射组学优化 Wilms 肿瘤管理的综述。

BJR open Pub Date : 2024-10-08 eCollection Date: 2024-01-01 DOI:10.1093/bjro/tzae034
Maryam Alhashim, Noushin Anan, Mahbubunnabi Tamal, Hibah Altarrah, Sarah Alshaibani, Robin Hill
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引用次数: 0

摘要

背景:Wilms 肿瘤是一种常见的儿科癌症,在中低收入国家,由于成像技术有限,很难对其进行治疗。人工智能(AI)已被引入肿瘤的分期、检测和分类,帮助医生做出决策。然而,所面临的挑战包括算法的准确性、传统诊断的转化、可重复性和可靠性。随着人工智能技术的发展,放射组学这一人工智能工具应运而生,用于提取肿瘤形态和分期信息:本综述探讨了放射组学在 Wilms 肿瘤管理中的应用,包括其在诊断、预后和治疗方面的潜力。此外,它还讨论了人工智能在这一领域的未来前景以及自动化辅助 Wilms 肿瘤治疗的潜在方向:综述分析了有关在 Wilms 肿瘤管理中使用放射组学的各种研究和文章。其中包括基于深度学习的自动分类研究、组织病理学分析中观察者之间的差异性以及人工智能在Wilms肿瘤分期、检测和分类中的应用:综述发现,放射组学在Wilms瘤管理中提供了几种前景广阔的应用,包括改进诊断:它有助于将Wilms瘤与其他儿科肾脏肿瘤进行分类;预后预测:放射组学特征可用于预测分期和术前化疗反应;治疗反应评估:治疗反应评估:放射组学可用于监测 Wilms 肿瘤的反应,并预测保肾手术的可行性:本综述认为,放射组学有可能显著改善 Wilms 肿瘤的诊断、预后和治疗。尽管存在一些挑战,如需要进一步的研究和验证,但将人工智能整合到 Wilms 肿瘤管理中为改善患者护理提供了大有可为的机会:本综述全面概述了放射组学在 Wilms 肿瘤管理中的潜在应用,并强调了人工智能在改善患者预后方面可发挥的重要作用。它为人工智能辅助诊断和治疗儿科癌症方面不断增长的知识做出了贡献。
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A review on optimization of Wilms tumour management using radiomics.

Background: Wilms tumour, a common paediatric cancer, is difficult to treat in low- and middle-income countries due to limited access to imaging. Artificial intelligence (AI) has been introduced for staging, detecting, and classifying tumours, aiding physicians in decision-making. However, challenges include algorithm accuracy, translation into conventional diagnosis, reproducibility, and reliability. As AI technology advances, radiomics, an AI tool, emerges to extract tumour morphology and stage information.

Objectives: This review explores the application of radiomics in Wilms tumour management, including its potential in diagnosis, prognosis, and treatment. Additionally, it discusses the future prospects of AI in this field and potential directions for automation-aided Wilms tumour treatment.

Methods: The review analyses various research studies and articles on the use of radiomics in Wilms tumour management. This includes studies on automated deep learning-based classification, interobserver variability in histopathological analysis, and the application of AI in staging, detecting, and classifying Wilms tumours.

Results: The review finds that radiomics offers several promising applications in Wilms tumour management, including improved diagnosis: it helps in classifying Wilms tumours from other paediatric kidney tumours, prognosis prediction: radiomic features can be used to predict both staging and response to preoperative chemotherapy, Treatment response assessment: Radiomics can be used to monitor the response of Wilms and to predict the feasibility of nephron-sparing surgery.

Conclusions: This review concludes that radiomics has the potential to significantly improve the diagnosis, prognosis, and treatment of Wilms tumours. Despite some challenges, such as the need for further research and validation, AI integration in Wilms tumour management offers promising opportunities for improved patient care.

Advances in knowledge: This review provides a comprehensive overview of the potential applications of radiomics in Wilms tumour management and highlights the significant role AI can play in improving patient outcomes. It contributes to the growing body of knowledge on AI-assisted diagnosis and treatment of paediatric cancers.

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