Dominietto Marco, Pica Alessia, Safai Sairos, Lomax Antony J, Weber Damien C, Capobianco Enrico
Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland.
Radiation Oncology Department, University Hospital of Bern, Bern, Switzerland.
Front Med (Lausanne). 2020 Jan 17;6:333. doi: 10.3389/fmed.2019.00333. eCollection 2019.
Human cancers exhibit phenotypic diversity that medical imaging can precisely and non-invasively detect. Multiple factors underlying innovations and progresses in the medical imaging field exert diagnostic and therapeutic impacts. The emerging field of radiomics has shown unprecedented ability to use imaging information in guiding clinical decisions. To achieve clinical assessment that exploits radiomic knowledge sources, integration between diverse data types is required. A current gap is the accuracy with which radiomics aligns with clinical endpoints. We propose a novel methodological approach that synergizes data volumes (images), tissue-contextualized information breadth, and network-driven resolution depth. Following the Precision Medicine paradigm, disease monitoring and prognostic assessment are tackled at the individual level by examining medical images acquired from two patients affected by intracranial ependymoma (with and without relapse). The challenge of spatially characterizing intratumor heterogeneity is tackled by a network approach that presents two main advantages: (a) Increased detection in the image domain power from high spatial resolution, (b) Superior accuracy in generating hypotheses underlying clinical decisions.
人类癌症表现出表型多样性，医学成像能够精确且无创地检测到这种多样性。医学成像领域创新与进步背后的多种因素产生了诊断和治疗影响。新兴的放射组学领域在利用成像信息指导临床决策方面展现出了前所未有的能力。为了实现利用放射组学知识源的临床评估，需要整合不同的数据类型。当前存在的一个差距是放射组学与临床终点的匹配精度。我们提出了一种新颖的方法，该方法将数据量（图像）、组织背景信息广度和网络驱动的分辨率深度协同起来。遵循精准医学范式，通过检查从两名患有颅内室管膜瘤（有复发和无复发）的患者获取的医学图像，在个体层面解决疾病监测和预后评估问题。通过一种网络方法应对在空间上表征肿瘤内异质性的挑战，该方法具有两个主要优点：（a）通过高空间分辨率在图像域增强检测能力，（b）在生成临床决策背后的假设方面具有更高的准确性。
