Institute of Informatics, School of Management, HES-SO Valais-Wallis, Sierre, Switzerland.
Department of Oncology, Precision Oncology Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Eur Radiol Exp. 2023 Mar 22;7(1):16. doi: 10.1186/s41747-023-00326-z.
Radiomics, the field of image-based computational medical biomarker research, has experienced rapid growth over the past decade due to its potential to revolutionize the development of personalized decision support models. However, despite its research momentum and important advances toward methodological standardization, the translation of radiomics prediction models into clinical practice only progresses slowly. The lack of physicians leading the development of radiomics models and insufficient integration of radiomics tools in the clinical workflow contributes to this slow uptake.
We propose a physician-centered vision of radiomics research and derive minimal functional requirements for radiomics research software to support this vision. Free-to-access radiomics tools and frameworks were reviewed to identify best practices and reveal the shortcomings of existing software solutions to optimally support physician-driven radiomics research in a clinical environment.
Support for user-friendly development and evaluation of radiomics prediction models via machine learning was found to be missing in most tools. QuantImage v2 (QI2) was designed and implemented to address these shortcomings. QI2 relies on well-established existing tools and open-source libraries to realize and concretely demonstrate the potential of a one-stop tool for physician-driven radiomics research. It provides web-based access to cohort management, feature extraction, and visualization and supports "no-code" development and evaluation of machine learning models against patient-specific outcome data.
QI2 fills a gap in the radiomics software landscape by enabling "no-code" radiomics research, including model validation, in a clinical environment. Further information about QI2, a public instance of the system, and its source code is available at https://medgift.github.io/quantimage-v2-info/ . Key points As domain experts, physicians play a key role in the development of radiomics models. Existing software solutions do not support physician-driven research optimally. QuantImage v2 implements a physician-centered vision for radiomics research. QuantImage v2 is a web-based, "no-code" radiomics research platform.
放射组学是基于图像的计算医学生物标志物研究领域,由于其有可能彻底改变个性化决策支持模型的开发,因此在过去十年中经历了快速发展。然而,尽管其研究势头强劲,并且在方法标准化方面取得了重要进展,但放射组学预测模型在临床实践中的转化仍然进展缓慢。缺乏主导放射组学模型开发的医生,以及放射组学工具在临床工作流程中的整合不足,是导致这一缓慢进展的原因。
我们提出了以医生为中心的放射组学研究愿景,并为放射组学研究软件推导了最小功能要求,以支持这一愿景。我们回顾了免费的放射组学工具和框架,以确定最佳实践,并揭示现有软件解决方案的不足之处,以在临床环境中最佳地支持医生驱动的放射组学研究。
大多数工具都缺乏对用户友好的放射组学预测模型的开发和评估的支持。为了解决这些不足,我们设计并实现了 QuantImage v2(QI2)。QI2依赖于成熟的现有工具和开源库来实现并具体展示一站式工具在医生驱动的放射组学研究中的潜力。它提供了对队列管理、特征提取和可视化的基于网络的访问,并支持针对特定患者的结果数据进行“无代码”开发和机器学习模型的评估。
QI2 通过在临床环境中实现“无代码”放射组学研究(包括模型验证),填补了放射组学软件领域的空白。有关 QI2 的更多信息、系统的公共实例及其源代码可在 https://medgift.github.io/quantimage-v2-info/ 上获得。
作为领域专家,医生在放射组学模型的开发中发挥着关键作用。
现有的软件解决方案不能最优地支持医生驱动的研究。
QuantImage v2 为放射组学研究实现了以医生为中心的愿景。
QuantImage v2 是一个基于网络的“无代码”放射组学研究平台。