In the past decade, a new approach for quantitative analysis of medical images and prognostic modelling has emerged. Defined as the extraction and analysis of a large number of quantitative parameters from medical images, radiomics is an evolving field in precision medicine with the ultimate goal of the discovery of new imaging biomarkers for disease. Radiomics has already shown promising results in extracting diagnostic, prognostic, and molecular information latent in medical images. After acquisition of the medical images as part of the standard of care, a region of interest is defined often via a manual or semi-automatic approach. An algorithm then extracts and computes quantitative radiomics parameters from the region of interest. Whereas radiomics captures quantitative values of shape and texture based on predefined mathematical terms, neural networks have recently been used to directly learn and identify predictive features from medical images. Thereby, neural networks largely forego the need for so called "hand-engineered" features, which appears to result in significantly improved performance and reliability. Opportunities for radiomics and neural networks in pediatric nuclear medicine/radiology/molecular imaging are broad and can be thought of in three categories: automating well-defined administrative or clinical tasks, augmenting broader administrative or clinical tasks, and unlocking new methods of generating value. Specific applications include intelligent order sets, automated protocoling, improved image acquisition, computer aided triage and detection of abnormalities, next generation voice dictation systems, biomarker development, and therapy planning.

中文翻译：

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儿科分子成像中的人工智能和放射组学

在过去的十年中，出现了一种用于医学图像定量分析和预后建模的新方法。放射组学定义为从医学图像中提取和分析大量定量参数，是精准医学中一个不断发展的领域，其最终目标是发现新的疾病成像生物标志物。放射组学在提取医学图像中潜在的诊断、预后和分子信息方面已经显示出有希望的结果。在获取医学图像作为护理标准的一部分之后，通常通过手动或半自动方法定义感兴趣区域。然后，一种算法从感兴趣的区域中提取并计算定量放射组学参数。而放射组学根据预定义的数学术语捕获形状和纹理的定量值，神经网络最近被用来直接从医学图像中学习和识别预测特征。因此，神经网络在很大程度上放弃了对所谓“手工设计”功能的需求，这似乎显着提高了性能和可靠性。放射组学和神经网络在儿科核医学/放射学/分子成像中的机会很广泛，可以分为三类：自动化明确定义的行政或临床任务，扩大更广泛的行政或临床任务，以及解锁产生价值的新方法。具体应用包括智能指令集、自动协议、改进的图像采集、计算机辅助分类和异常检测、下一代语音听写系统、生物标志物开发和治疗计划。