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儿科肿瘤学中的影像生物标志物和放射组学:来自 PRIMAGE(基于预测性计算多尺度分析以支持癌症个体化诊断和预后,由影像生物标志物赋能)项目的观点。

Imaging biomarkers and radiomics in pediatric oncology: a view from the PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project.

机构信息

Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026, Valencia, Spain.

Área Clínica de Imagen Médica, Área Clínica de Imagen Médica, Hospital Universitari i Politècnic La Fe, Avinguda Fernando Abril Martorell, 106 Torre E planta 0, 46026, València, Spain.

出版信息

Pediatr Radiol. 2024 Apr;54(4):562-570. doi: 10.1007/s00247-023-05770-y. Epub 2023 Sep 25.

Abstract

This review paper presents the practical development of imaging biomarkers in the scope of the PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project, as a noninvasive and reliable way to improve the diagnosis and prognosis in pediatric oncology. The PRIMAGE project is a European multi-center research initiative that focuses on developing medical imaging-derived artificial intelligence (AI) solutions designed to enhance overall management and decision-making for two types of pediatric cancer: neuroblastoma and diffuse intrinsic pontine glioma. To allow this, the PRIMAGE project has created an open-cloud platform that combines imaging, clinical, and molecular data together with AI models developed from this data, creating a comprehensive decision support environment for clinicians managing patients with these two cancers. In order to achieve this, a standardized data processing and analysis workflow was implemented to generate robust and reliable predictions for different clinical endpoints. Magnetic resonance (MR) image harmonization and registration was performed as part of the workflow. Subsequently, an automated tool for the detection and segmentation of tumors was trained and internally validated. The Dice similarity coefficient obtained for the independent validation dataset was 0.997, indicating compatibility with the manual segmentation variability. Following this, radiomics and deep features were extracted and correlated with clinical endpoints. Finally, reproducible and relevant imaging quantitative features were integrated with clinical and molecular data to enrich both the predictive models and a set of visual analytics tools, making the PRIMAGE platform a complete clinical decision aid system. In order to ensure the advancement of research in this field and to foster engagement with the wider research community, the PRIMAGE data repository and platform are currently being integrated into the European Federation for Cancer Images (EUCAIM), which is the largest European cancer imaging research infrastructure created to date.

摘要

本文综述了成像生物标志物在 PRIMAGE(预测性计算多尺度分析以支持癌症个体化诊断和预后,通过成像生物标志物增强)项目范围内的实际发展,作为一种非侵入性和可靠的方法,可提高儿科肿瘤的诊断和预后。PRIMAGE 项目是一个欧洲多中心研究计划,专注于开发基于医学成像的人工智能(AI)解决方案,旨在增强两种儿科癌症(神经母细胞瘤和弥漫性内在脑桥胶质瘤)的整体管理和决策。为此,PRIMAGE 项目创建了一个开放云平台,将成像、临床和分子数据与从这些数据中开发的 AI 模型相结合,为管理这些两种癌症患者的临床医生创建一个全面的决策支持环境。为了实现这一目标,实施了标准化的数据处理和分析工作流程,以针对不同的临床终点生成稳健和可靠的预测。作为工作流程的一部分,进行了磁共振(MR)图像的调和与配准。随后,训练并内部验证了用于肿瘤检测和分割的自动化工具。在独立验证数据集上获得的 Dice 相似系数为 0.997,表明与手动分割的可变性兼容。接下来,提取了放射组学和深度特征,并与临床终点相关联。最后,可重现和相关的成像定量特征与临床和分子数据集成,丰富了预测模型和一套可视化分析工具,使 PRIMAGE 平台成为一个完整的临床决策辅助系统。为了确保该领域研究的进展,并促进与更广泛的研究社区的合作,PRIMAGE 数据存储库和平台目前正在整合到欧洲癌症成像联盟(EUCAIM)中,这是迄今为止创建的最大的欧洲癌症成像研究基础设施。

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